From 34c0bda8cbfed41e3f31561084ec09b1e9d1d9fe Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Fri, 26 Jul 2024 17:53:38 +0530 Subject: [PATCH 01/32] refresh everything --- Common/include/CConfig.hpp | 3 + Common/include/containers/C2DContainer.hpp | 2 +- .../linear_algebra/CMatrixVectorProduct.hpp | 2 +- Common/include/linear_algebra/CSysMatrix.hpp | 11 ++++ Common/src/CConfig.cpp | 1 + Common/src/linear_algebra/CSysMatrix.cpp | 9 +-- Common/src/linear_algebra/CSysVector.cpp | 2 + Common/src/linear_algebra/meson.build | 1 + SU2_CFD/src/solvers/CFEM_DG_EulerSolver.cpp | 3 +- SU2_CFD/src/solvers/CFEM_DG_NSSolver.cpp | 63 ++++++++++--------- meson.build | 11 +++- meson_options.txt | 1 + 12 files changed, 70 insertions(+), 39 deletions(-) diff --git a/Common/include/CConfig.hpp b/Common/include/CConfig.hpp index c28c3aaef54..0c0dedb1d06 100644 --- a/Common/include/CConfig.hpp +++ b/Common/include/CConfig.hpp @@ -1131,6 +1131,7 @@ class CConfig { su2double Theta_Interior_Penalty_DGFEM; /*!< \brief Factor for the symmetrizing terms in the DG discretization of the viscous fluxes. */ unsigned short byteAlignmentMatMul; /*!< \brief Number of bytes in the vectorization direction for the matrix multiplication. Multipe of 64. */ unsigned short sizeMatMulPadding; /*!< \brief The matrix size in the vectorization direction padded to a multiple of 8. Computed from byteAlignmentMatMul. */ + unsigned short gpuSizeMatMulPadding; bool Compute_Entropy; /*!< \brief Whether or not to compute the entropy in the fluid model. */ bool Use_Lumped_MassMatrix_DGFEM; /*!< \brief Whether or not to use the lumped mass matrix for DGFEM. */ bool Jacobian_Spatial_Discretization_Only; /*!< \brief Flag to know if only the exact Jacobian of the spatial discretization must be computed. */ @@ -9089,6 +9090,8 @@ class CConfig { */ unsigned short GetSizeMatMulPadding(void) const { return sizeMatMulPadding; } + unsigned short GetGPUSizeMatMulPadding(void) const { return gpuSizeMatMulPadding; } + /*! * \brief Function to make available whether or not the entropy must be computed. * \return The boolean whether or not the entropy must be computed. diff --git a/Common/include/containers/C2DContainer.hpp b/Common/include/containers/C2DContainer.hpp index 633c6b7defe..a517a2cd442 100644 --- a/Common/include/containers/C2DContainer.hpp +++ b/Common/include/containers/C2DContainer.hpp @@ -417,7 +417,7 @@ class C2DContainer * read-only, generic non-contiguous access. */ template - class CInnerIterGather { + class CInnerIterGather { private: static_assert(std::is_integral::value, ""); enum { Size = IndexSIMD_t::Size }; diff --git a/Common/include/linear_algebra/CMatrixVectorProduct.hpp b/Common/include/linear_algebra/CMatrixVectorProduct.hpp index 21e88dfb920..fff29537ef9 100644 --- a/Common/include/linear_algebra/CMatrixVectorProduct.hpp +++ b/Common/include/linear_algebra/CMatrixVectorProduct.hpp @@ -94,6 +94,6 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { * \param[out] v - CSysVector that is the result of the product */ inline void operator()(const CSysVector& u, CSysVector& v) const override { - matrix.MatrixVectorProduct(u, v, geometry, config); + matrix.MatrixVectorProduct(u, v, geometry, config); } }; diff --git a/Common/include/linear_algebra/CSysMatrix.hpp b/Common/include/linear_algebra/CSysMatrix.hpp index 573e6217d93..6001ed7b907 100644 --- a/Common/include/linear_algebra/CSysMatrix.hpp +++ b/Common/include/linear_algebra/CSysMatrix.hpp @@ -837,6 +837,17 @@ class CSysMatrix { */ void MatrixVectorProduct(const CSysVector& vec, CSysVector& prod, CGeometry* geometry, const CConfig* config) const; + + /*! + * \brief Performs the product of a sparse matrix by a CSysVector. + * \param[in] vec - CSysVector to be multiplied by the sparse matrix A. + * \param[in] geometry - Geometrical definition of the problem. + * \param[in] config - Definition of the particular problem. + * \param[out] prod - Result of the product. + */ + + void GPUMatrixVectorProduct(const CSysVector& vec, CSysVector& prod, CGeometry* geometry, + const CConfig* config) const; /*! * \brief Build the Jacobi preconditioner. diff --git a/Common/src/CConfig.cpp b/Common/src/CConfig.cpp index 18da76556c4..eaa0aab811f 100644 --- a/Common/src/CConfig.cpp +++ b/Common/src/CConfig.cpp @@ -2399,6 +2399,7 @@ void CConfig::SetConfig_Options() { /* DESCRIPTION: Number of aligned bytes for the matrix multiplications. Multiple of 64. (128 by default) */ addUnsignedShortOption("ALIGNED_BYTES_MATMUL", byteAlignmentMatMul, 128); + addUnsignedShortOption("GPU Matrix Multiplication Size", gpuSizeMatMulPadding, 65472); /*!\par CONFIG_CATEGORY: FEA solver \ingroup Config*/ /*--- Options related to the FEA solver ---*/ diff --git a/Common/src/linear_algebra/CSysMatrix.cpp b/Common/src/linear_algebra/CSysMatrix.cpp index 10747fadbcb..9f4c3607a0c 100644 --- a/Common/src/linear_algebra/CSysMatrix.cpp +++ b/Common/src/linear_algebra/CSysMatrix.cpp @@ -25,8 +25,9 @@ * License along with SU2. If not, see . */ -#include "../../include/linear_algebra/CSysMatrix.inl" +#include "../../include/linear_algebra/CSysMatrix.inl" +#include "../../include/linear_algebra/CSysMatrix.hpp" #include "../../include/geometry/CGeometry.hpp" #include "../../include/toolboxes/allocation_toolbox.hpp" @@ -479,7 +480,7 @@ void CSysMatrix::Gauss_Elimination(ScalarType* matrix, ScalarType* v LAPACKE_dgetrf(LAPACK_ROW_MAJOR, nVar, nVar, matrix, nVar, ipiv); LAPACKE_dgetrs(LAPACK_ROW_MAJOR, 'N', nVar, 1, matrix, nVar, ipiv, vec, 1); #else -#define A(I, J) matrix[(I)*nVar + (J)] +#define A(I, J) matrix[(I) * nVar + (J)] /*--- Transform system in Upper Matrix ---*/ for (auto iVar = 1ul; iVar < nVar; iVar++) { @@ -509,7 +510,7 @@ void CSysMatrix::MatrixInverse(ScalarType* matrix, ScalarType* inver assert((matrix != inverse) && "Output cannot be the same as the input."); -#define M(I, J) inverse[(I)*nVar + (J)] +#define M(I, J) inverse[(I) * nVar + (J)] /*--- Initialize the inverse with the identity. ---*/ for (auto iVar = 0ul; iVar < nVar; iVar++) @@ -522,7 +523,7 @@ void CSysMatrix::MatrixInverse(ScalarType* matrix, ScalarType* inver LAPACKE_dgetrf(LAPACK_ROW_MAJOR, nVar, nVar, matrix, nVar, ipiv); LAPACKE_dgetrs(LAPACK_ROW_MAJOR, 'N', nVar, nVar, matrix, nVar, ipiv, inverse, nVar); #else -#define A(I, J) matrix[(I)*nVar + (J)] +#define A(I, J) matrix[(I) * nVar + (J)] /*--- Transform system in Upper Matrix ---*/ for (auto iVar = 1ul; iVar < nVar; iVar++) { diff --git a/Common/src/linear_algebra/CSysVector.cpp b/Common/src/linear_algebra/CSysVector.cpp index d33a5863b01..754e7c39b52 100644 --- a/Common/src/linear_algebra/CSysVector.cpp +++ b/Common/src/linear_algebra/CSysVector.cpp @@ -52,6 +52,8 @@ void CSysVector::Initialize(unsigned long numBlk, unsigned long numB if (vec_val == nullptr) vec_val = MemoryAllocation::aligned_alloc(64, nElm * sizeof(ScalarType)); + + if (val != nullptr) { if (!valIsArray) { for (auto i = 0ul; i < nElm; i++) vec_val[i] = *val; diff --git a/Common/src/linear_algebra/meson.build b/Common/src/linear_algebra/meson.build index f8904b7f0f8..701cae786b4 100644 --- a/Common/src/linear_algebra/meson.build +++ b/Common/src/linear_algebra/meson.build @@ -2,5 +2,6 @@ common_src += files(['CSysSolve_b.cpp', 'CSysSolve.cpp', 'CSysVector.cpp', 'CSysMatrix.cpp', + 'GPU_lin_alg.cu', 'CPastixWrapper.cpp', 'blas_structure.cpp']) diff --git a/SU2_CFD/src/solvers/CFEM_DG_EulerSolver.cpp b/SU2_CFD/src/solvers/CFEM_DG_EulerSolver.cpp index f01ea90e7b1..96d2558023a 100644 --- a/SU2_CFD/src/solvers/CFEM_DG_EulerSolver.cpp +++ b/SU2_CFD/src/solvers/CFEM_DG_EulerSolver.cpp @@ -35,7 +35,7 @@ #include "../../include/fluid/CDataDrivenFluid.hpp" enum { -SIZE_ARR_NORM = 8 +SIZE_ARR_NORM = 16385 }; CFEM_DG_EulerSolver::CFEM_DG_EulerSolver() : CSolver() { @@ -6447,6 +6447,7 @@ void CFEM_DG_EulerSolver::InviscidFluxesInternalMatchingFace( const su2double* arrNorm[SIZE_ARR_NORM]; const su2double* arrGridVel[SIZE_ARR_NORM]; + if(llEnd > SIZE_ARR_NORM) SU2_MPI::Error("SIZE_ARR_NORM is too small. Increase it or decrease ALIGNED_BYTES_MATMUL", CURRENT_FUNCTION); diff --git a/SU2_CFD/src/solvers/CFEM_DG_NSSolver.cpp b/SU2_CFD/src/solvers/CFEM_DG_NSSolver.cpp index efbe74b0aa1..ce7aba6a620 100644 --- a/SU2_CFD/src/solvers/CFEM_DG_NSSolver.cpp +++ b/SU2_CFD/src/solvers/CFEM_DG_NSSolver.cpp @@ -30,7 +30,7 @@ #include "../../../Common/include/toolboxes/printing_toolbox.hpp" enum { -SIZE_ARR_NORM = 8 +SIZE_ARR_NORM = 16385 }; CFEM_DG_NSSolver::CFEM_DG_NSSolver() : CFEM_DG_EulerSolver() { @@ -198,7 +198,7 @@ void CFEM_DG_NSSolver::Friction_Forces(const CGeometry* geometry, const CConfig* /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetSizeMatMulPadding(); + const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -476,7 +476,7 @@ void CFEM_DG_NSSolver::Friction_Forces(const CGeometry* geometry, const CConfig* /* Determine the offset between r- and -s-derivatives, which is also the offset between s- and t-derivatives. */ - const unsigned short offDeriv = NPad*nInt; + const unsigned int offDeriv = NPad*nInt; /* Make a distinction between two and three space dimensions in order to have the most efficient code. */ @@ -922,7 +922,7 @@ void CFEM_DG_NSSolver::SetTime_Step(CGeometry *geometry, CSolver **solver_contai /* Determine the number of elements that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetSizeMatMulPadding(); + const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); const unsigned short nElemSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -1345,8 +1345,8 @@ void CFEM_DG_NSSolver::ADER_DG_AliasedPredictorResidual_2D(CConfig /* Determine the offset between the r-derivatives and s-derivatives of the fluxes in the integration points and the offset between the r-derivatives and s-derivatives of the solution in the DOFs. */ - const unsigned short offDerivSol = NPad*nDOFs; - const unsigned short offDerivFluxes = NPad*nInt; + const unsigned int offDerivSol = NPad*nDOFs; + const unsigned int offDerivFluxes = NPad*nInt; /* Store the number of metric points per integration point for readability. */ const unsigned short nMetricPerPoint = 5; /* nDim*nDim + 1. */ @@ -1671,8 +1671,8 @@ void CFEM_DG_NSSolver::ADER_DG_AliasedPredictorResidual_3D(CConfig /* Determine the offset between the r-derivatives and s-derivatives of the fluxes in the integration points and the offset between the r-derivatives and s-derivatives of the solution in the DOFs. */ - const unsigned short offDerivSol = NPad*nDOFs; - const unsigned short offDerivFluxes = NPad*nInt; + const unsigned int offDerivSol = NPad*nDOFs; + const unsigned int offDerivFluxes = NPad*nInt; /* Store the number of metric points per integration point/DOF for readability. */ const unsigned short nMetricPerPoint = 10; /* nDim*nDim + 1. */ @@ -2062,7 +2062,7 @@ void CFEM_DG_NSSolver::ADER_DG_NonAliasedPredictorResidual_2D(CConfig /* Determine the offset between the solution variables and the r-derivatives, which is also the offset between the r- and s-derivatives in the integration points. */ - const unsigned short offDerivInt = NPad*nInt; + const unsigned int offDerivInt = NPad*nInt; /* Set the pointer for the second derivatives such that they are stored after the first derivatives. */ @@ -2455,7 +2455,7 @@ void CFEM_DG_NSSolver::ADER_DG_NonAliasedPredictorResidual_3D(CConfig /* Determine the offset between the solution variables and the r-derivatives, which is also the offset between the r- and s-derivatives in the integration points. */ - const unsigned short offDerivInt = NPad*nInt; + const unsigned int offDerivInt = NPad*nInt; /* Set the pointer for the second derivatives such that they are stored after the first derivatives. */ @@ -3165,7 +3165,8 @@ void CFEM_DG_NSSolver::Volume_Residual(CConfig *config, /* Determine the number of elements that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetSizeMatMulPadding(); + + const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); const unsigned short nElemSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -3243,7 +3244,7 @@ void CFEM_DG_NSSolver::Volume_Residual(CConfig *config, /* Determine the offset between the solution variables and the r-derivatives, which is also the offset between the r- and s-derivatives and the offset between s- and t-derivatives. */ - const unsigned short offDeriv = NPad*nInt; + const unsigned int offDeriv = NPad*nInt; /* Make a distinction between two and three space dimensions in order to have the most efficient code. */ @@ -3731,7 +3732,7 @@ void CFEM_DG_NSSolver::ResidualFaces(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetSizeMatMulPadding(); + const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -3869,7 +3870,7 @@ void CFEM_DG_NSSolver::ResidualFaces(CConfig *config, /*--- Subtract half of the viscous fluxes from the inviscid fluxes. The factor 0.5 comes from the fact that the average of the viscous fluxes of side 0 and side 1 must be taken in the DG-FEM formulation. ---*/ - for(unsigned short j=0; j<(NPad*nInt); ++j) fluxes[j] -= 0.5*viscFluxes[j]; + for(unsigned int j=0; j<(NPad*nInt); ++j) fluxes[j] -= 0.5*viscFluxes[j]; /*---------------------------*/ /*--- Side 1 of the face. ---*/ @@ -3941,7 +3942,7 @@ void CFEM_DG_NSSolver::ResidualFaces(CConfig *config, } /*--- Subtract half of the viscous fluxes from the inviscid fluxes. ---*/ - for(unsigned short j=0; j<(NPad*nInt); ++j) fluxes[j] -= 0.5*viscFluxes[j]; + for(unsigned int j=0; j<(NPad*nInt); ++j) fluxes[j] -= 0.5*viscFluxes[j]; /*------------------------------------------------------------------------*/ /*--- Step 3: Compute the penalty terms in the integration points of ---*/ @@ -3973,7 +3974,7 @@ void CFEM_DG_NSSolver::ResidualFaces(CConfig *config, } /* Add the penalty fluxes to the earlier computed fluxes. */ - for(unsigned short j=0; j<(NPad*nInt); ++j) fluxes[j] += viscFluxes[j]; + for(unsigned int j=0; j<(NPad*nInt); ++j) fluxes[j] += viscFluxes[j]; /* Multiply the fluxes with the integration weight of the corresponding integration point. */ @@ -4163,7 +4164,7 @@ void CFEM_DG_NSSolver::ViscousNormalFluxFace(const CVolumeElementFEM *adjVolElem /* Determine the offset between r- and -s-derivatives, which is also the offset between s- and t-derivatives. */ - const unsigned short offDeriv = NPad*nInt; + const unsigned int offDeriv = NPad*nInt; /* Make a distinction between two and three space dimensions in order to have the most efficient code. */ @@ -4991,7 +4992,7 @@ void CFEM_DG_NSSolver::BC_Euler_Wall(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetSizeMatMulPadding(); + const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5057,7 +5058,7 @@ void CFEM_DG_NSSolver::BC_Far_Field(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetSizeMatMulPadding(); + const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5129,7 +5130,7 @@ void CFEM_DG_NSSolver::BC_Sym_Plane(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetSizeMatMulPadding(); + const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5221,7 +5222,7 @@ void CFEM_DG_NSSolver::BC_Sym_Plane(CConfig *config, /* Determine the offset between r- and -s-derivatives, which is also the offset between s- and t-derivatives. */ - const unsigned short offDeriv = NPad*nInt; + const unsigned int offDeriv = NPad*nInt; /* Loop over the faces of the chunk. */ for(unsigned long ll=l; llGetSizeMatMulPadding(); + const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5572,7 +5573,7 @@ void CFEM_DG_NSSolver::BC_Supersonic_Outlet(CConfig *config, /* Set the right state in the integration points to the left state, i.e. no boundary condition is applied for a supersonic outlet. */ - for(unsigned short mm=0; mm<(NPad*nInt); ++mm) + for(unsigned int mm=0; mm<(NPad*nInt); ++mm) solIntR[mm] = solIntL[mm]; /* The remainder of the boundary treatment is the same for all @@ -5602,7 +5603,7 @@ void CFEM_DG_NSSolver::BC_Inlet(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetSizeMatMulPadding(); + const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5668,7 +5669,7 @@ void CFEM_DG_NSSolver::BC_Outlet(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetSizeMatMulPadding(); + const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5745,7 +5746,7 @@ void CFEM_DG_NSSolver::BC_HeatFlux_Wall(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetSizeMatMulPadding(); + const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5882,7 +5883,7 @@ void CFEM_DG_NSSolver::BC_Isothermal_Wall(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetSizeMatMulPadding(); + const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5983,7 +5984,7 @@ void CFEM_DG_NSSolver::BC_Riemann(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetSizeMatMulPadding(); + const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -6048,7 +6049,7 @@ void CFEM_DG_NSSolver::BC_Custom(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetSizeMatMulPadding(); + const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -6431,7 +6432,7 @@ void CFEM_DG_NSSolver::ResidualViscousBoundaryFace( solInt0, solInt1, fluxes, conv_numerics); /* Subtract the viscous fluxes from the inviscid fluxes. */ - for(unsigned short j=0; j<(NPad*nInt); ++j) fluxes[j] -= viscFluxes[j]; + for(unsigned int j=0; j<(NPad*nInt); ++j) fluxes[j] -= viscFluxes[j]; /*--- Loop over the faces in this chunk to compute the penalty fluxes. ---*/ for(unsigned short l=0; l=0.61.1', license: 'LGPL2', @@ -15,6 +15,9 @@ endif pymod = import('python') python = pymod.find_installation() +add_global_arguments('-G', language : 'cuda') + + su2_cpp_args = [] su2_deps = [declare_dependency(include_directories: 'externals/CLI11')] @@ -196,6 +199,12 @@ if get_option('enable-pastix') su2_deps += pastix_dep endif +if get_option('enable-cuda') + su2_cpp_args += '-DHAVE_CUDA' + gpu_dep = dependency('cuda', version : '>=10', modules : ['cudart', 'nvblas']) + su2_deps += gpu_dep +endif + # blas-type dependencies if get_option('enable-mkl') diff --git a/meson_options.txt b/meson_options.txt index 7b2902bba0b..886c0e41930 100644 --- a/meson_options.txt +++ b/meson_options.txt @@ -9,6 +9,7 @@ option('enable-normal', type : 'boolean', value : true, description: 'enable no option('enable-mkl', type : 'boolean', value : false, description: 'enable Intel-MKL support') option('mkl_root', type : 'string', value : '/opt/intel/mkl', description: 'root of Intel-MKL installation (only for non-intel compilers)') option('enable-openblas', type : 'boolean', value : false, description: 'enable BLAS and LAPACK support via OpenBLAS') +option('enable-cuda', type : 'boolean', value : false, description: 'enable GPU acceleration using CUDA') option('blas-name', type : 'string', value : 'openblas', description: 'name of the BLAS/LAPACK dependency') option('enable-pastix', type : 'boolean', value : false, description: 'enable PaStiX support') option('pastix_root', type : 'string', value : 'externals/pastix/', description: 'PaStiX base directory') From c953af8591306449b1a1df85bf92d67647c2015a Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Fri, 26 Jul 2024 17:55:40 +0530 Subject: [PATCH 02/32] readme update --- README.md | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/README.md b/README.md index dd3b86878a4..1f7851d2b84 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,26 @@ +# Experimental GPU Accelerated Version of SU2 Code + +Currently two modifications exist in the code +- NVBLAS Integration which intercepts calls for the Space Integration in the DG Solver - not very useful +- Acceleration of program wide Matrix Vector Product by outsourcing to a CUDA Kernel + +Both can be enabled by giving meson the following option during compilation +``` +-Denable-cuda=true +``` + +You will also need to specify the environment variable CUDA_PATH with the location of the installed CUDA Folder - usually found in the /usr/local directory + +A script has been provided which allows you to input your specific paths and use to compile. **We recommend not using MPI currently** as splitting up chunks of the domain will cause issues with predefined memory transfer (that's not good, I know, I'll fix it) + +To run the script just go +``` +bash build_SU2.sh +``` +A barebones template NVBLAS config file has also been provided for reference. + +# STANDARD README STARTS HERE +

From 231968d823a1cfa1090dc957e4a4cf0e5b3a579a Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Fri, 26 Jul 2024 18:03:35 +0530 Subject: [PATCH 03/32] final push --- .gitignore | 4 + Common/include/linear_algebra/GPU_lin_alg.cuh | 4 + Common/include/linear_algerba | 7 ++ Common/src/linear_algebra/GPU_lin_alg.cu | 94 +++++++++++++++++++ build_SU2.sh | 9 ++ nvblas.conf | 7 ++ 6 files changed, 125 insertions(+) create mode 100644 Common/include/linear_algebra/GPU_lin_alg.cuh create mode 100644 Common/include/linear_algerba create mode 100644 Common/src/linear_algebra/GPU_lin_alg.cu create mode 100644 build_SU2.sh create mode 100644 nvblas.conf diff --git a/.gitignore b/.gitignore index 2177d44e12e..7f060c5e04b 100644 --- a/.gitignore +++ b/.gitignore @@ -103,3 +103,7 @@ su2preconfig.timestamp # Clangd server files .cache + +#Testcases Folders +FEM_AREEN_MESH/ +FVM/ \ No newline at end of file diff --git a/Common/include/linear_algebra/GPU_lin_alg.cuh b/Common/include/linear_algebra/GPU_lin_alg.cuh new file mode 100644 index 00000000000..8e1f9903550 --- /dev/null +++ b/Common/include/linear_algebra/GPU_lin_alg.cuh @@ -0,0 +1,4 @@ +#include +#include"../../include/linear_algebra/CSysMatrix.hpp" + +__global__ void GPUMatrixVectorProductAdd(su2mixedfloat* matrix, double* vec, double* prod, unsigned long* row_ptr, unsigned long* col_ind); \ No newline at end of file diff --git a/Common/include/linear_algerba b/Common/include/linear_algerba new file mode 100644 index 00000000000..7c96114aec1 --- /dev/null +++ b/Common/include/linear_algerba @@ -0,0 +1,7 @@ +NVBLAS_CPU_BLAS_LIB /path/to/libopenblas.so + +NVBLAS_GPU_LIST ALL + +NVBLAS_TILE_DIM 2048 + +NVBLAS_AUTOPIN_MEM_ENABLED diff --git a/Common/src/linear_algebra/GPU_lin_alg.cu b/Common/src/linear_algebra/GPU_lin_alg.cu new file mode 100644 index 00000000000..3d73c09077b --- /dev/null +++ b/Common/src/linear_algebra/GPU_lin_alg.cu @@ -0,0 +1,94 @@ +#include +#include "../../include/linear_algebra/CSysMatrix.inl" +#include "../../include/linear_algebra/CSysMatrix.hpp" +#include "../../include/geometry/CGeometry.hpp" + +__global__ void GPUMatrixVectorProductAdd(su2mixedfloat* matrix, double* vec, double* prod, unsigned long* d_row_ptr, unsigned long* d_col_ind, unsigned long nPointDomain, unsigned long nVar, unsigned long nEqn) +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; + int j = threadIdx.y; + int k = threadIdx.z; + + if(i +void CSysMatrix::GPUMatrixVectorProduct(const CSysVector& vec, CSysVector& prod, + CGeometry* geometry, const CConfig* config) const + { + + unsigned long* d_row_ptr; /*!< \brief Device Pointers to the first element in each row. */ + unsigned long* d_col_ind; /*!< \brief Device Column index for each of the elements in val(). */ + + su2mixedfloat* d_matrix; + double* d_vec; + double* d_prod; + + unsigned long nPointDomain = geometry->GetnPointDomain(); + unsigned long nDim = geometry->GetnDim(); + unsigned long nVar = nDim + 2 ; + unsigned long nEqn = nVar; + + unsigned long mat_size = nnz*nVar*nEqn; + unsigned long vec_size = nPointDomain*nVar; + + cudaMalloc((void**)(&d_row_ptr), (sizeof(row_ptr)*(nPointDomain+1.0))); + cudaMalloc((void**)(&d_col_ind), (sizeof(col_ind)*nnz)); + cudaMalloc((void**)(&d_matrix), (sizeof(&matrix[0])*mat_size)); + cudaMalloc((void**)(&d_vec), (sizeof(&vec[0])*vec_size)); + cudaMalloc((void**)(&d_prod), (sizeof(&prod[0])*vec_size)); + + cudaError_t code1 = cudaGetLastError(); + if(code1 != cudaSuccess) + { + std::cerr << code1 << " Error Code " << std::endl; + } + + cudaMemcpy((void*)(d_row_ptr), (void*)row_ptr, (sizeof(row_ptr)*(nPointDomain+1.0)), cudaMemcpyHostToDevice); + cudaMemcpy((void*)(d_col_ind), (void*)col_ind, (sizeof(col_ind))*nnz, cudaMemcpyHostToDevice); + cudaMemcpy((void*)(d_matrix), (void*)&matrix[0], (sizeof(&matrix[0])*mat_size), cudaMemcpyHostToDevice); + cudaMemcpy((void*)(d_vec), (void*)&vec[0], (sizeof(&vec[0])*vec_size), cudaMemcpyHostToDevice); + cudaMemcpy((void*)(d_prod), (void*)&prod[0], (sizeof(&prod[0])*vec_size), cudaMemcpyHostToDevice); + + cudaError_t code2 = cudaGetLastError(); + if(code2 != cudaSuccess) + { + std::cerr << code2 << " Error Code " << std::endl; + } + + long xDim = floor(512.0/(nVar*nEqn)); + dim3 blockDim(xDim, nEqn, nVar); + dim3 gridDim(ceil(nPointDomain/xDim), 1, 1); + + GPUMatrixVectorProductAdd<<>>(d_matrix, d_vec, d_prod, d_row_ptr, d_col_ind, nPointDomain, nVar, nEqn); + + cudaError_t code3 = cudaGetLastError(); + if(code3 != cudaSuccess) + { + std::cerr << code3 << " Error Code " << std::endl; + } + + cudaMemcpy((void*)(&prod[0]), (void*)d_prod, (sizeof(&prod[0])*vec_size), cudaMemcpyDeviceToHost); + + cudaFree(d_col_ind); + cudaFree(d_row_ptr); + cudaFree(d_vec); + cudaFree(d_prod); + cudaFree(d_matrix); + +} + +template class CSysMatrix; \ No newline at end of file diff --git a/build_SU2.sh b/build_SU2.sh new file mode 100644 index 00000000000..5dbc33a53d5 --- /dev/null +++ b/build_SU2.sh @@ -0,0 +1,9 @@ +#!bin/bash + +rm -r build + +export PKG_CONFIG_PATH="/home/areen/Programs/OpenBLAS_install/lib:$PKG_CONFIG_PATH" +export PKG_CONFIG_PATH="/usr/local/cuda/lib64:$PKG_CONFIG_PATH" +export CUDA_PATH="/usr/local/cuda:$CUDA_PATH" +./meson.py build --buildtype=debug -Denable-pywrapper=true -Dwith-mpi=disabled -Denable-openblas=true -Denable-cuda=true --prefix=/home/areen/Programs/SU2_Install_GPU +./ninja -C build install \ No newline at end of file diff --git a/nvblas.conf b/nvblas.conf new file mode 100644 index 00000000000..7c96114aec1 --- /dev/null +++ b/nvblas.conf @@ -0,0 +1,7 @@ +NVBLAS_CPU_BLAS_LIB /path/to/libopenblas.so + +NVBLAS_GPU_LIST ALL + +NVBLAS_TILE_DIM 2048 + +NVBLAS_AUTOPIN_MEM_ENABLED From 4bddc30a5d9f8dba08473dc38cec6c367fc63c63 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Fri, 26 Jul 2024 18:33:57 +0530 Subject: [PATCH 04/32] Enable GPU Mat Vec --- Common/include/linear_algebra/CMatrixVectorProduct.hpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Common/include/linear_algebra/CMatrixVectorProduct.hpp b/Common/include/linear_algebra/CMatrixVectorProduct.hpp index fff29537ef9..5489ecfe804 100644 --- a/Common/include/linear_algebra/CMatrixVectorProduct.hpp +++ b/Common/include/linear_algebra/CMatrixVectorProduct.hpp @@ -94,6 +94,6 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { * \param[out] v - CSysVector that is the result of the product */ inline void operator()(const CSysVector& u, CSysVector& v) const override { - matrix.MatrixVectorProduct(u, v, geometry, config); + matrix.GPUMatrixVectorProduct(u, v, geometry, config); } }; From 2da57f672e49fd39a299523ddb9a845dae1394a5 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Sat, 27 Jul 2024 21:24:49 +0530 Subject: [PATCH 05/32] New Branch and Optimized Memory Alloc on GPU Slightly --- Common/include/linear_algebra/CSysMatrix.hpp | 5 +++ Common/src/linear_algebra/CSysMatrix.cpp | 9 +++++ Common/src/linear_algebra/GPU_lin_alg.cu | 37 +++++++++----------- 3 files changed, 30 insertions(+), 21 deletions(-) diff --git a/Common/include/linear_algebra/CSysMatrix.hpp b/Common/include/linear_algebra/CSysMatrix.hpp index 6001ed7b907..247456ec04e 100644 --- a/Common/include/linear_algebra/CSysMatrix.hpp +++ b/Common/include/linear_algebra/CSysMatrix.hpp @@ -145,6 +145,11 @@ class CSysMatrix { const unsigned long* col_ind; /*!< \brief Column index for each of the elements in val(). */ const unsigned long* col_ptr; /*!< \brief The transpose of col_ind, pointer to blocks with the same column index. */ + ScalarType* d_matrix; /*!< \brief Entries ot be stored on the device. */ + + unsigned long* d_row_ptr; /*!< \brief Device Pointers to the first element in each row. */ + unsigned long* d_col_ind; /*!< \brief Device Column index for each of the elements in val(). */ + ScalarType* ILU_matrix; /*!< \brief Entries of the ILU sparse matrix. */ unsigned long nnz_ilu; /*!< \brief Number of possible nonzero entries in the matrix (ILU). */ const unsigned long* row_ptr_ilu; /*!< \brief Pointers to the first element in each row (ILU). */ diff --git a/Common/src/linear_algebra/CSysMatrix.cpp b/Common/src/linear_algebra/CSysMatrix.cpp index 9f4c3607a0c..96f315da81e 100644 --- a/Common/src/linear_algebra/CSysMatrix.cpp +++ b/Common/src/linear_algebra/CSysMatrix.cpp @@ -31,6 +31,8 @@ #include "../../include/geometry/CGeometry.hpp" #include "../../include/toolboxes/allocation_toolbox.hpp" +#include "cuda_runtime.h" + #include template @@ -132,6 +134,13 @@ void CSysMatrix::Initialize(unsigned long npoint, unsigned long npoi col_ind = csr.innerIdx(); dia_ptr = csr.diagPtr(); + cudaMalloc((void**)(&d_row_ptr), (sizeof(row_ptr)*(nPointDomain+1.0))); + cudaMalloc((void**)(&d_col_ind), (sizeof(col_ind)*nnz)); + cudaMalloc((void**)(&d_matrix), (sizeof(&matrix[0])*nnz*nVar*nEqn)); + + cudaMemcpy((void*)(d_row_ptr), (void*)row_ptr, (sizeof(row_ptr)*(nPointDomain+1.0)), cudaMemcpyHostToDevice); + cudaMemcpy((void*)(d_col_ind), (void*)col_ind, (sizeof(col_ind))*nnz, cudaMemcpyHostToDevice); + if (needTranspPtr) col_ptr = geometry->GetTransposeSparsePatternMap(type).data(); if (type == ConnectivityType::FiniteVolume) { diff --git a/Common/src/linear_algebra/GPU_lin_alg.cu b/Common/src/linear_algebra/GPU_lin_alg.cu index 3d73c09077b..2843d5aab88 100644 --- a/Common/src/linear_algebra/GPU_lin_alg.cu +++ b/Common/src/linear_algebra/GPU_lin_alg.cu @@ -3,6 +3,12 @@ #include "../../include/linear_algebra/CSysMatrix.hpp" #include "../../include/geometry/CGeometry.hpp" +template +void CSysMatrix::GPUMatrixStructMemAlloc() +{ + +} + __global__ void GPUMatrixVectorProductAdd(su2mixedfloat* matrix, double* vec, double* prod, unsigned long* d_row_ptr, unsigned long* d_col_ind, unsigned long nPointDomain, unsigned long nVar, unsigned long nEqn) { int i = blockIdx.x * blockDim.x + threadIdx.x; @@ -24,16 +30,12 @@ __global__ void GPUMatrixVectorProductAdd(su2mixedfloat* matrix, double* vec, do } - template void CSysMatrix::GPUMatrixVectorProduct(const CSysVector& vec, CSysVector& prod, CGeometry* geometry, const CConfig* config) const { - unsigned long* d_row_ptr; /*!< \brief Device Pointers to the first element in each row. */ - unsigned long* d_col_ind; /*!< \brief Device Column index for each of the elements in val(). */ - su2mixedfloat* d_matrix; double* d_vec; double* d_prod; @@ -45,28 +47,23 @@ void CSysMatrix::GPUMatrixVectorProduct(const CSysVector unsigned long mat_size = nnz*nVar*nEqn; unsigned long vec_size = nPointDomain*nVar; - cudaMalloc((void**)(&d_row_ptr), (sizeof(row_ptr)*(nPointDomain+1.0))); - cudaMalloc((void**)(&d_col_ind), (sizeof(col_ind)*nnz)); - cudaMalloc((void**)(&d_matrix), (sizeof(&matrix[0])*mat_size)); cudaMalloc((void**)(&d_vec), (sizeof(&vec[0])*vec_size)); cudaMalloc((void**)(&d_prod), (sizeof(&prod[0])*vec_size)); - cudaError_t code1 = cudaGetLastError(); - if(code1 != cudaSuccess) + cudaError_t code = cudaGetLastError(); + if(code != cudaSuccess) { - std::cerr << code1 << " Error Code " << std::endl; + std::cerr << code << " Error Code " << std::endl; } - cudaMemcpy((void*)(d_row_ptr), (void*)row_ptr, (sizeof(row_ptr)*(nPointDomain+1.0)), cudaMemcpyHostToDevice); - cudaMemcpy((void*)(d_col_ind), (void*)col_ind, (sizeof(col_ind))*nnz, cudaMemcpyHostToDevice); cudaMemcpy((void*)(d_matrix), (void*)&matrix[0], (sizeof(&matrix[0])*mat_size), cudaMemcpyHostToDevice); cudaMemcpy((void*)(d_vec), (void*)&vec[0], (sizeof(&vec[0])*vec_size), cudaMemcpyHostToDevice); cudaMemcpy((void*)(d_prod), (void*)&prod[0], (sizeof(&prod[0])*vec_size), cudaMemcpyHostToDevice); - cudaError_t code2 = cudaGetLastError(); - if(code2 != cudaSuccess) + code = cudaGetLastError(); + if(code != cudaSuccess) { - std::cerr << code2 << " Error Code " << std::endl; + std::cerr << code << " Error Code " << std::endl; } long xDim = floor(512.0/(nVar*nEqn)); @@ -75,19 +72,17 @@ void CSysMatrix::GPUMatrixVectorProduct(const CSysVector GPUMatrixVectorProductAdd<<>>(d_matrix, d_vec, d_prod, d_row_ptr, d_col_ind, nPointDomain, nVar, nEqn); - cudaError_t code3 = cudaGetLastError(); - if(code3 != cudaSuccess) + code = cudaGetLastError(); + if(code != cudaSuccess) { - std::cerr << code3 << " Error Code " << std::endl; + std::cerr << code << " Error Code " << std::endl; } cudaMemcpy((void*)(&prod[0]), (void*)d_prod, (sizeof(&prod[0])*vec_size), cudaMemcpyDeviceToHost); - cudaFree(d_col_ind); - cudaFree(d_row_ptr); + cudaFree(d_vec); cudaFree(d_prod); - cudaFree(d_matrix); } From aa35779962fc1fe040d9e6e532e02f21ffc1c032 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Tue, 30 Jul 2024 17:30:34 +0530 Subject: [PATCH 06/32] Finished GPU Mat-Vec with CPU Accuracy and Block Matrix Parallelization as well --- Common/src/linear_algebra/GPU_lin_alg.cu | 43 +++++++++++++++--------- meson.build | 2 +- 2 files changed, 28 insertions(+), 17 deletions(-) diff --git a/Common/src/linear_algebra/GPU_lin_alg.cu b/Common/src/linear_algebra/GPU_lin_alg.cu index 2843d5aab88..3fd72cbfcb3 100644 --- a/Common/src/linear_algebra/GPU_lin_alg.cu +++ b/Common/src/linear_algebra/GPU_lin_alg.cu @@ -3,32 +3,36 @@ #include "../../include/linear_algebra/CSysMatrix.hpp" #include "../../include/geometry/CGeometry.hpp" -template -void CSysMatrix::GPUMatrixStructMemAlloc() -{ - -} - __global__ void GPUMatrixVectorProductAdd(su2mixedfloat* matrix, double* vec, double* prod, unsigned long* d_row_ptr, unsigned long* d_col_ind, unsigned long nPointDomain, unsigned long nVar, unsigned long nEqn) -{ +{ + int i = blockIdx.x * blockDim.x + threadIdx.x; int j = threadIdx.y; int k = threadIdx.z; + int prod_index = i * nVar; + + prod[prod_index + j] = 0.0; + + __syncthreads(); + + double res = 0.0; + if(i void CSysMatrix::GPUMatrixVectorProduct(const CSysVector& vec, CSysVector& prod, @@ -66,9 +70,11 @@ void CSysMatrix::GPUMatrixVectorProduct(const CSysVector std::cerr << code << " Error Code " << std::endl; } - long xDim = floor(512.0/(nVar*nEqn)); - dim3 blockDim(xDim, nEqn, nVar); - dim3 gridDim(ceil(nPointDomain/xDim), 1, 1); + + double xDim = (double) 1024.0/(nVar*nEqn); + dim3 blockDim(floor(xDim), nVar, nEqn); + double gridx = (double) nPointDomain/xDim; + dim3 gridDim(ceil(gridx), 1, 1); GPUMatrixVectorProductAdd<<>>(d_matrix, d_vec, d_prod, d_row_ptr, d_col_ind, nPointDomain, nVar, nEqn); @@ -80,7 +86,12 @@ void CSysMatrix::GPUMatrixVectorProduct(const CSysVector cudaMemcpy((void*)(&prod[0]), (void*)d_prod, (sizeof(&prod[0])*vec_size), cudaMemcpyDeviceToHost); - + code = cudaGetLastError(); + if(code != cudaSuccess) + { + std::cerr << code << " Error Code " << std::endl; + } + cudaFree(d_vec); cudaFree(d_prod); diff --git a/meson.build b/meson.build index 74e514e644e..bb278ecd55f 100644 --- a/meson.build +++ b/meson.build @@ -15,7 +15,7 @@ endif pymod = import('python') python = pymod.find_installation() -add_global_arguments('-G', language : 'cuda') +add_global_arguments(['-G', '-arch=sm_60' ], language : 'cuda') su2_cpp_args = [] From 88669365e58e7a66582aef80c3bec06b36fe9c0a Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Tue, 30 Jul 2024 17:54:43 +0530 Subject: [PATCH 07/32] Added fully template kernels to prevent type errors --- Common/src/linear_algebra/GPU_lin_alg.cu | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/Common/src/linear_algebra/GPU_lin_alg.cu b/Common/src/linear_algebra/GPU_lin_alg.cu index 3fd72cbfcb3..036d9ed6411 100644 --- a/Common/src/linear_algebra/GPU_lin_alg.cu +++ b/Common/src/linear_algebra/GPU_lin_alg.cu @@ -3,7 +3,8 @@ #include "../../include/linear_algebra/CSysMatrix.hpp" #include "../../include/geometry/CGeometry.hpp" -__global__ void GPUMatrixVectorProductAdd(su2mixedfloat* matrix, double* vec, double* prod, unsigned long* d_row_ptr, unsigned long* d_col_ind, unsigned long nPointDomain, unsigned long nVar, unsigned long nEqn) +template +__global__ void GPUMatrixVectorProductAdd(matrixType* matrix, vectorType* vec, vectorType* prod, unsigned long* d_row_ptr, unsigned long* d_col_ind, unsigned long nPointDomain, unsigned long nVar, unsigned long nEqn) { int i = blockIdx.x * blockDim.x + threadIdx.x; @@ -16,7 +17,7 @@ __global__ void GPUMatrixVectorProductAdd(su2mixedfloat* matrix, double* vec, do __syncthreads(); - double res = 0.0; + vectorType res = 0.0; if(i::GPUMatrixVectorProduct(const CSysVector CGeometry* geometry, const CConfig* config) const { - - double* d_vec; - double* d_prod; + ScalarType* d_vec; + ScalarType* d_prod; unsigned long nPointDomain = geometry->GetnPointDomain(); unsigned long nDim = geometry->GetnDim(); From 12f01dbe76550e056c57878b3d8ef9670293ad98 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Mon, 5 Aug 2024 23:08:05 +0530 Subject: [PATCH 08/32] Disabled NVBLAS Implementation in the DG Solver --- SU2_CFD/src/solvers/CFEM_DG_NSSolver.cpp | 28 ++++++++++++------------ 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/SU2_CFD/src/solvers/CFEM_DG_NSSolver.cpp b/SU2_CFD/src/solvers/CFEM_DG_NSSolver.cpp index ce7aba6a620..3dfad133ec4 100644 --- a/SU2_CFD/src/solvers/CFEM_DG_NSSolver.cpp +++ b/SU2_CFD/src/solvers/CFEM_DG_NSSolver.cpp @@ -198,7 +198,7 @@ void CFEM_DG_NSSolver::Friction_Forces(const CGeometry* geometry, const CConfig* /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); + const unsigned short nPadInput = config->GetSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -922,7 +922,7 @@ void CFEM_DG_NSSolver::SetTime_Step(CGeometry *geometry, CSolver **solver_contai /* Determine the number of elements that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); + const unsigned short nPadInput = config->GetSizeMatMulPadding(); const unsigned short nElemSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -3166,7 +3166,7 @@ void CFEM_DG_NSSolver::Volume_Residual(CConfig *config, /* Determine the number of elements that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); + const unsigned short nPadInput = config->GetSizeMatMulPadding(); const unsigned short nElemSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -3732,7 +3732,7 @@ void CFEM_DG_NSSolver::ResidualFaces(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); + const unsigned short nPadInput = config->GetSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -4992,7 +4992,7 @@ void CFEM_DG_NSSolver::BC_Euler_Wall(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); + const unsigned short nPadInput = config->GetSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5058,7 +5058,7 @@ void CFEM_DG_NSSolver::BC_Far_Field(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); + const unsigned short nPadInput = config->GetSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5130,7 +5130,7 @@ void CFEM_DG_NSSolver::BC_Sym_Plane(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); + const unsigned short nPadInput = config->GetSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5535,7 +5535,7 @@ void CFEM_DG_NSSolver::BC_Supersonic_Outlet(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); + const unsigned short nPadInput = config->GetSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5603,7 +5603,7 @@ void CFEM_DG_NSSolver::BC_Inlet(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); + const unsigned short nPadInput = config->GetSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5669,7 +5669,7 @@ void CFEM_DG_NSSolver::BC_Outlet(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); + const unsigned short nPadInput = config->GetSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5746,7 +5746,7 @@ void CFEM_DG_NSSolver::BC_HeatFlux_Wall(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); + const unsigned short nPadInput = config->GetSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5883,7 +5883,7 @@ void CFEM_DG_NSSolver::BC_Isothermal_Wall(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); + const unsigned short nPadInput = config->GetSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -5984,7 +5984,7 @@ void CFEM_DG_NSSolver::BC_Riemann(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); + const unsigned short nPadInput = config->GetSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which @@ -6049,7 +6049,7 @@ void CFEM_DG_NSSolver::BC_Custom(CConfig *config, /* Determine the number of faces that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetGPUSizeMatMulPadding(); + const unsigned short nPadInput = config->GetSizeMatMulPadding(); const unsigned short nFaceSimul = nPadInput/nVar; /* Determine the minimum padded size in the matrix multiplications, which From d7cbd5e90da19fbf6071c7ecadf78b0138f8b93e Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Sun, 18 Aug 2024 02:08:26 +0530 Subject: [PATCH 09/32] Added options and error check --- .gitignore | 3 +- Common/include/CConfig.hpp | 7 +++ .../linear_algebra/CMatrixVectorProduct.hpp | 40 ++++++++++++++- Common/include/linear_algebra/CSysMatrix.hpp | 4 ++ Common/include/linear_algebra/GPU_lin_alg.cuh | 10 ++++ Common/src/CConfig.cpp | 2 + Common/src/linear_algebra/CSysMatrix.cpp | 17 ++++--- Common/src/linear_algebra/GPU_lin_alg.cu | 49 ++++++------------- 8 files changed, 88 insertions(+), 44 deletions(-) diff --git a/.gitignore b/.gitignore index 7f060c5e04b..a36aa4a5342 100644 --- a/.gitignore +++ b/.gitignore @@ -106,4 +106,5 @@ su2preconfig.timestamp #Testcases Folders FEM_AREEN_MESH/ -FVM/ \ No newline at end of file +FVM/ +naca0012/ \ No newline at end of file diff --git a/Common/include/CConfig.hpp b/Common/include/CConfig.hpp index 0c0dedb1d06..e7aab52c30c 100644 --- a/Common/include/CConfig.hpp +++ b/Common/include/CConfig.hpp @@ -133,6 +133,7 @@ class CConfig { Sens_Remove_Sharp, /*!< \brief Flag for removing or not the sharp edges from the sensitivity computation. */ Hold_GridFixed, /*!< \brief Flag hold fixed some part of the mesh during the deformation. */ Axisymmetric, /*!< \brief Flag for axisymmetric calculations */ + Enable_Cuda, /*!< \brief Flag for switching GPU computing*/ Integrated_HeatFlux; /*!< \brief Flag for heat flux BC whether it deals with integrated values.*/ su2double Buffet_k; /*!< \brief Sharpness coefficient for buffet sensor.*/ su2double Buffet_lambda; /*!< \brief Offset parameter for buffet sensor.*/ @@ -6225,6 +6226,12 @@ class CConfig { */ bool GetAxisymmetric(void) const { return Axisymmetric; } + /*! + * \brief Get information about GPU support. + * \return TRUE if cuda is enabled; otherwise FALSE. + */ + bool GetCUDA(void) const { return Enable_Cuda; } + /*! * \brief Get information about there is a smoothing of the grid coordinates. * \return TRUE if there is smoothing of the grid coordinates; otherwise FALSE. diff --git a/Common/include/linear_algebra/CMatrixVectorProduct.hpp b/Common/include/linear_algebra/CMatrixVectorProduct.hpp index 5489ecfe804..1dcf6853009 100644 --- a/Common/include/linear_algebra/CMatrixVectorProduct.hpp +++ b/Common/include/linear_algebra/CMatrixVectorProduct.hpp @@ -32,6 +32,11 @@ #include "../geometry/CGeometry.hpp" #include "CSysVector.hpp" #include "CSysMatrix.hpp" +#include "GPU_lin_alg.cuh" +#include +#include +#include +#include /*! * \class CMatrixVectorProduct @@ -93,7 +98,40 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { * \param[in] u - CSysVector that is being multiplied by the sparse matrix * \param[out] v - CSysVector that is the result of the product */ + + + + inline void operator()(const CSysVector& u, CSysVector& v) const override { - matrix.GPUMatrixVectorProduct(u, v, geometry, config); + + /*Create output file*/ + /* + std::ofstream serial; + + auto start = std::chrono::high_resolution_clock::now(); + */ + + if(config->GetCUDA()) + { + matrix.GPUMatrixVectorProduct(u, v, geometry, config); + } + else + { + matrix.MatrixVectorProduct(u, v, geometry, config); + } + + + /* + auto stop = std::chrono::high_resolution_clock::now(); + auto duration = std::chrono::duration_cast(stop - start); + + double time = duration.count(); + + serial.open("serial.txt", std::ios::app); + serial << time << "\n"; + serial.close(); + */ + } }; + diff --git a/Common/include/linear_algebra/CSysMatrix.hpp b/Common/include/linear_algebra/CSysMatrix.hpp index 247456ec04e..e72b17af8b2 100644 --- a/Common/include/linear_algebra/CSysMatrix.hpp +++ b/Common/include/linear_algebra/CSysMatrix.hpp @@ -361,6 +361,7 @@ class CSysMatrix { void RowProduct(const CSysVector& vec, unsigned long row_i, ScalarType* prod) const; public: + /*! * \brief Constructor of the class. */ @@ -853,6 +854,9 @@ class CSysMatrix { void GPUMatrixVectorProduct(const CSysVector& vec, CSysVector& prod, CGeometry* geometry, const CConfig* config) const; + + void FGMRESMainLoop(std::vector W, std::vector Z, su2vector& g, su2vector& sn,CSysVector& cs,su2vector& y,su2vector& H, + int m, CGeometry* geometry, const CConfig* config) const; /*! * \brief Build the Jacobi preconditioner. diff --git a/Common/include/linear_algebra/GPU_lin_alg.cuh b/Common/include/linear_algebra/GPU_lin_alg.cuh index 8e1f9903550..2bb7b9a5320 100644 --- a/Common/include/linear_algebra/GPU_lin_alg.cuh +++ b/Common/include/linear_algebra/GPU_lin_alg.cuh @@ -1,4 +1,14 @@ #include #include"../../include/linear_algebra/CSysMatrix.hpp" +#include"iostream" + +inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true) +{ + if (code != cudaSuccess) + { + fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line); + if (abort) exit(code); + } +} __global__ void GPUMatrixVectorProductAdd(su2mixedfloat* matrix, double* vec, double* prod, unsigned long* row_ptr, unsigned long* col_ind); \ No newline at end of file diff --git a/Common/src/CConfig.cpp b/Common/src/CConfig.cpp index eaa0aab811f..453852e6d74 100644 --- a/Common/src/CConfig.cpp +++ b/Common/src/CConfig.cpp @@ -1144,6 +1144,8 @@ void CConfig::SetConfig_Options() { /*\brief AXISYMMETRIC \n DESCRIPTION: Axisymmetric simulation \n DEFAULT: false \ingroup Config */ addBoolOption("AXISYMMETRIC", Axisymmetric, false); + /*\brief ENABLE_CUDA \n DESCRIPTION: GPU Acceleration \n DEFAULT: false \ingroup Config */ + addBoolOption("ENABLE_CUDA", Enable_Cuda, false); /* DESCRIPTION: Add the gravity force */ addBoolOption("GRAVITY_FORCE", GravityForce, false); /* DESCRIPTION: Add the Vorticity Confinement term*/ diff --git a/Common/src/linear_algebra/CSysMatrix.cpp b/Common/src/linear_algebra/CSysMatrix.cpp index 96f315da81e..9f1dff8cb1a 100644 --- a/Common/src/linear_algebra/CSysMatrix.cpp +++ b/Common/src/linear_algebra/CSysMatrix.cpp @@ -28,13 +28,16 @@ #include "../../include/linear_algebra/CSysMatrix.inl" #include "../../include/linear_algebra/CSysMatrix.hpp" +#include "../../include/linear_algebra/GPU_lin_alg.cuh" #include "../../include/geometry/CGeometry.hpp" #include "../../include/toolboxes/allocation_toolbox.hpp" -#include "cuda_runtime.h" - #include +#ifndef gpuErrChk +#define gpuErrChk(ans) { gpuAssert((ans), __FILE__, __LINE__); } +#endif + template CSysMatrix::CSysMatrix() : rank(SU2_MPI::GetRank()), size(SU2_MPI::GetSize()) { nPoint = nPointDomain = nVar = nEqn = 0; @@ -134,12 +137,12 @@ void CSysMatrix::Initialize(unsigned long npoint, unsigned long npoi col_ind = csr.innerIdx(); dia_ptr = csr.diagPtr(); - cudaMalloc((void**)(&d_row_ptr), (sizeof(row_ptr)*(nPointDomain+1.0))); - cudaMalloc((void**)(&d_col_ind), (sizeof(col_ind)*nnz)); - cudaMalloc((void**)(&d_matrix), (sizeof(&matrix[0])*nnz*nVar*nEqn)); + gpuErrChk(cudaMalloc((void**)(&d_row_ptr), (sizeof(row_ptr)*(nPointDomain+1.0)))); + gpuErrChk(cudaMalloc((void**)(&d_col_ind), (sizeof(col_ind)*nnz))); + gpuErrChk(cudaMalloc((void**)(&d_matrix), (sizeof(ScalarType)*nnz*nVar*nEqn))); - cudaMemcpy((void*)(d_row_ptr), (void*)row_ptr, (sizeof(row_ptr)*(nPointDomain+1.0)), cudaMemcpyHostToDevice); - cudaMemcpy((void*)(d_col_ind), (void*)col_ind, (sizeof(col_ind))*nnz, cudaMemcpyHostToDevice); + gpuErrChk(cudaMemcpy((void*)(d_row_ptr), (void*)row_ptr, (sizeof(row_ptr)*(nPointDomain+1.0)), cudaMemcpyHostToDevice)); + gpuErrChk(cudaMemcpy((void*)(d_col_ind), (void*)col_ind, (sizeof(col_ind))*nnz, cudaMemcpyHostToDevice)); if (needTranspPtr) col_ptr = geometry->GetTransposeSparsePatternMap(type).data(); diff --git a/Common/src/linear_algebra/GPU_lin_alg.cu b/Common/src/linear_algebra/GPU_lin_alg.cu index 036d9ed6411..3e32c502233 100644 --- a/Common/src/linear_algebra/GPU_lin_alg.cu +++ b/Common/src/linear_algebra/GPU_lin_alg.cu @@ -1,8 +1,12 @@ -#include #include "../../include/linear_algebra/CSysMatrix.inl" #include "../../include/linear_algebra/CSysMatrix.hpp" +#include "../../include/linear_algebra/GPU_lin_alg.cuh" #include "../../include/geometry/CGeometry.hpp" +#ifndef gpuErrChk +#define gpuErrChk(ans) { gpuAssert((ans), __FILE__, __LINE__); } +#endif + template __global__ void GPUMatrixVectorProductAdd(matrixType* matrix, vectorType* vec, vectorType* prod, unsigned long* d_row_ptr, unsigned long* d_col_ind, unsigned long nPointDomain, unsigned long nVar, unsigned long nEqn) { @@ -50,26 +54,13 @@ void CSysMatrix::GPUMatrixVectorProduct(const CSysVector unsigned long mat_size = nnz*nVar*nEqn; unsigned long vec_size = nPointDomain*nVar; - cudaMalloc((void**)(&d_vec), (sizeof(&vec[0])*vec_size)); - cudaMalloc((void**)(&d_prod), (sizeof(&prod[0])*vec_size)); - - cudaError_t code = cudaGetLastError(); - if(code != cudaSuccess) - { - std::cerr << code << " Error Code " << std::endl; - } - - cudaMemcpy((void*)(d_matrix), (void*)&matrix[0], (sizeof(&matrix[0])*mat_size), cudaMemcpyHostToDevice); - cudaMemcpy((void*)(d_vec), (void*)&vec[0], (sizeof(&vec[0])*vec_size), cudaMemcpyHostToDevice); - cudaMemcpy((void*)(d_prod), (void*)&prod[0], (sizeof(&prod[0])*vec_size), cudaMemcpyHostToDevice); - - code = cudaGetLastError(); - if(code != cudaSuccess) - { - std::cerr << code << " Error Code " << std::endl; - } - + gpuErrChk(cudaMalloc((void**)(&d_vec), (sizeof(ScalarType)*vec_size))); + gpuErrChk(cudaMalloc((void**)(&d_prod), (sizeof(ScalarType)*vec_size))); + gpuErrChk(cudaMemcpy((void*)(d_matrix), (void*)&matrix[0], (sizeof(ScalarType)*mat_size), cudaMemcpyHostToDevice)); + gpuErrChk(cudaMemcpy((void*)(d_vec), (void*)&vec[0], (sizeof(ScalarType)*vec_size), cudaMemcpyHostToDevice)); + gpuErrChk(cudaMemcpy((void*)(d_prod), (void*)&prod[0], (sizeof(ScalarType)*vec_size), cudaMemcpyHostToDevice)); + double xDim = (double) 1024.0/(nVar*nEqn); dim3 blockDim(floor(xDim), nVar, nEqn); double gridx = (double) nPointDomain/xDim; @@ -77,22 +68,10 @@ void CSysMatrix::GPUMatrixVectorProduct(const CSysVector GPUMatrixVectorProductAdd<<>>(d_matrix, d_vec, d_prod, d_row_ptr, d_col_ind, nPointDomain, nVar, nEqn); - code = cudaGetLastError(); - if(code != cudaSuccess) - { - std::cerr << code << " Error Code " << std::endl; - } - - cudaMemcpy((void*)(&prod[0]), (void*)d_prod, (sizeof(&prod[0])*vec_size), cudaMemcpyDeviceToHost); - - code = cudaGetLastError(); - if(code != cudaSuccess) - { - std::cerr << code << " Error Code " << std::endl; - } + gpuErrChk(cudaMemcpy((void*)(&prod[0]), (void*)d_prod, (sizeof(ScalarType)*vec_size), cudaMemcpyDeviceToHost)); - cudaFree(d_vec); - cudaFree(d_prod); + gpuErrChk(cudaFree(d_vec)); + gpuErrChk(cudaFree(d_prod)); } From 103ec397b3824650b9bef5dca8c5fccef4eff68e Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Sun, 18 Aug 2024 02:26:55 +0530 Subject: [PATCH 10/32] reverting stuff to debug --- Common/src/linear_algebra/CSysMatrix.cpp | 10 +++++----- Common/src/linear_algebra/GPU_lin_alg.cu | 16 ++++++++-------- 2 files changed, 13 insertions(+), 13 deletions(-) diff --git a/Common/src/linear_algebra/CSysMatrix.cpp b/Common/src/linear_algebra/CSysMatrix.cpp index 9f1dff8cb1a..bb8111a71d2 100644 --- a/Common/src/linear_algebra/CSysMatrix.cpp +++ b/Common/src/linear_algebra/CSysMatrix.cpp @@ -137,12 +137,12 @@ void CSysMatrix::Initialize(unsigned long npoint, unsigned long npoi col_ind = csr.innerIdx(); dia_ptr = csr.diagPtr(); - gpuErrChk(cudaMalloc((void**)(&d_row_ptr), (sizeof(row_ptr)*(nPointDomain+1.0)))); - gpuErrChk(cudaMalloc((void**)(&d_col_ind), (sizeof(col_ind)*nnz))); - gpuErrChk(cudaMalloc((void**)(&d_matrix), (sizeof(ScalarType)*nnz*nVar*nEqn))); + cudaMalloc((void**)(&d_row_ptr), (sizeof(row_ptr)*(nPointDomain+1.0))); + cudaMalloc((void**)(&d_col_ind), (sizeof(col_ind)*nnz)); + cudaMalloc((void**)(&d_matrix), (sizeof(ScalarType)*nnz*nVar*nEqn)); - gpuErrChk(cudaMemcpy((void*)(d_row_ptr), (void*)row_ptr, (sizeof(row_ptr)*(nPointDomain+1.0)), cudaMemcpyHostToDevice)); - gpuErrChk(cudaMemcpy((void*)(d_col_ind), (void*)col_ind, (sizeof(col_ind))*nnz, cudaMemcpyHostToDevice)); + cudaMemcpy((void*)(d_row_ptr), (void*)row_ptr, (sizeof(row_ptr)*(nPointDomain+1.0)), cudaMemcpyHostToDevice); + cudaMemcpy((void*)(d_col_ind), (void*)col_ind, (sizeof(col_ind))*nnz, cudaMemcpyHostToDevice); if (needTranspPtr) col_ptr = geometry->GetTransposeSparsePatternMap(type).data(); diff --git a/Common/src/linear_algebra/GPU_lin_alg.cu b/Common/src/linear_algebra/GPU_lin_alg.cu index 3e32c502233..1a8ed197f55 100644 --- a/Common/src/linear_algebra/GPU_lin_alg.cu +++ b/Common/src/linear_algebra/GPU_lin_alg.cu @@ -54,12 +54,12 @@ void CSysMatrix::GPUMatrixVectorProduct(const CSysVector unsigned long mat_size = nnz*nVar*nEqn; unsigned long vec_size = nPointDomain*nVar; - gpuErrChk(cudaMalloc((void**)(&d_vec), (sizeof(ScalarType)*vec_size))); - gpuErrChk(cudaMalloc((void**)(&d_prod), (sizeof(ScalarType)*vec_size))); + cudaMalloc((void**)(&d_vec), (sizeof(ScalarType)*vec_size)); + cudaMalloc((void**)(&d_prod), (sizeof(ScalarType)*vec_size)); - gpuErrChk(cudaMemcpy((void*)(d_matrix), (void*)&matrix[0], (sizeof(ScalarType)*mat_size), cudaMemcpyHostToDevice)); - gpuErrChk(cudaMemcpy((void*)(d_vec), (void*)&vec[0], (sizeof(ScalarType)*vec_size), cudaMemcpyHostToDevice)); - gpuErrChk(cudaMemcpy((void*)(d_prod), (void*)&prod[0], (sizeof(ScalarType)*vec_size), cudaMemcpyHostToDevice)); + cudaMemcpy((void*)(d_matrix), (void*)&matrix[0], (sizeof(ScalarType)*mat_size), cudaMemcpyHostToDevice); + cudaMemcpy((void*)(d_vec), (void*)&vec[0], (sizeof(ScalarType)*vec_size), cudaMemcpyHostToDevice); + cudaMemcpy((void*)(d_prod), (void*)&prod[0], (sizeof(ScalarType)*vec_size), cudaMemcpyHostToDevice); double xDim = (double) 1024.0/(nVar*nEqn); dim3 blockDim(floor(xDim), nVar, nEqn); @@ -68,10 +68,10 @@ void CSysMatrix::GPUMatrixVectorProduct(const CSysVector GPUMatrixVectorProductAdd<<>>(d_matrix, d_vec, d_prod, d_row_ptr, d_col_ind, nPointDomain, nVar, nEqn); - gpuErrChk(cudaMemcpy((void*)(&prod[0]), (void*)d_prod, (sizeof(ScalarType)*vec_size), cudaMemcpyDeviceToHost)); + cudaMemcpy((void*)(&prod[0]), (void*)d_prod, (sizeof(ScalarType)*vec_size), cudaMemcpyDeviceToHost); - gpuErrChk(cudaFree(d_vec)); - gpuErrChk(cudaFree(d_prod)); + cudaFree(d_vec); + cudaFree(d_prod); } From 38d658b4d8951a1a2612e9c5e7dcb32024422fa7 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Sun, 18 Aug 2024 03:37:56 +0530 Subject: [PATCH 11/32] fixed turbulent case but Error Check is a performance hit --- Common/src/linear_algebra/CSysMatrix.cpp | 10 ++++---- Common/src/linear_algebra/GPU_lin_alg.cu | 30 +++++++++++------------- 2 files changed, 19 insertions(+), 21 deletions(-) diff --git a/Common/src/linear_algebra/CSysMatrix.cpp b/Common/src/linear_algebra/CSysMatrix.cpp index bb8111a71d2..9f1dff8cb1a 100644 --- a/Common/src/linear_algebra/CSysMatrix.cpp +++ b/Common/src/linear_algebra/CSysMatrix.cpp @@ -137,12 +137,12 @@ void CSysMatrix::Initialize(unsigned long npoint, unsigned long npoi col_ind = csr.innerIdx(); dia_ptr = csr.diagPtr(); - cudaMalloc((void**)(&d_row_ptr), (sizeof(row_ptr)*(nPointDomain+1.0))); - cudaMalloc((void**)(&d_col_ind), (sizeof(col_ind)*nnz)); - cudaMalloc((void**)(&d_matrix), (sizeof(ScalarType)*nnz*nVar*nEqn)); + gpuErrChk(cudaMalloc((void**)(&d_row_ptr), (sizeof(row_ptr)*(nPointDomain+1.0)))); + gpuErrChk(cudaMalloc((void**)(&d_col_ind), (sizeof(col_ind)*nnz))); + gpuErrChk(cudaMalloc((void**)(&d_matrix), (sizeof(ScalarType)*nnz*nVar*nEqn))); - cudaMemcpy((void*)(d_row_ptr), (void*)row_ptr, (sizeof(row_ptr)*(nPointDomain+1.0)), cudaMemcpyHostToDevice); - cudaMemcpy((void*)(d_col_ind), (void*)col_ind, (sizeof(col_ind))*nnz, cudaMemcpyHostToDevice); + gpuErrChk(cudaMemcpy((void*)(d_row_ptr), (void*)row_ptr, (sizeof(row_ptr)*(nPointDomain+1.0)), cudaMemcpyHostToDevice)); + gpuErrChk(cudaMemcpy((void*)(d_col_ind), (void*)col_ind, (sizeof(col_ind))*nnz, cudaMemcpyHostToDevice)); if (needTranspPtr) col_ptr = geometry->GetTransposeSparsePatternMap(type).data(); diff --git a/Common/src/linear_algebra/GPU_lin_alg.cu b/Common/src/linear_algebra/GPU_lin_alg.cu index 1a8ed197f55..1e9a7be66fd 100644 --- a/Common/src/linear_algebra/GPU_lin_alg.cu +++ b/Common/src/linear_algebra/GPU_lin_alg.cu @@ -17,11 +17,14 @@ __global__ void GPUMatrixVectorProductAdd(matrixType* matrix, vectorType* vec, v int prod_index = i * nVar; - prod[prod_index + j] = 0.0; + if(i::GPUMatrixVectorProduct(const CSysVector ScalarType* d_vec; ScalarType* d_prod; - unsigned long nPointDomain = geometry->GetnPointDomain(); - unsigned long nDim = geometry->GetnDim(); - unsigned long nVar = nDim + 2 ; - unsigned long nEqn = nVar; - unsigned long mat_size = nnz*nVar*nEqn; unsigned long vec_size = nPointDomain*nVar; - cudaMalloc((void**)(&d_vec), (sizeof(ScalarType)*vec_size)); - cudaMalloc((void**)(&d_prod), (sizeof(ScalarType)*vec_size)); + gpuErrChk(cudaMalloc((void**)(&d_vec), (sizeof(ScalarType)*vec_size))); + gpuErrChk(cudaMalloc((void**)(&d_prod), (sizeof(ScalarType)*vec_size))); - cudaMemcpy((void*)(d_matrix), (void*)&matrix[0], (sizeof(ScalarType)*mat_size), cudaMemcpyHostToDevice); - cudaMemcpy((void*)(d_vec), (void*)&vec[0], (sizeof(ScalarType)*vec_size), cudaMemcpyHostToDevice); - cudaMemcpy((void*)(d_prod), (void*)&prod[0], (sizeof(ScalarType)*vec_size), cudaMemcpyHostToDevice); + gpuErrChk(cudaMemcpy((void*)(d_matrix), (void*)&matrix[0], (sizeof(ScalarType)*mat_size), cudaMemcpyHostToDevice)); + gpuErrChk(cudaMemcpy((void*)(d_vec), (void*)&vec[0], (sizeof(ScalarType)*vec_size), cudaMemcpyHostToDevice)); + gpuErrChk(cudaMemcpy((void*)(d_prod), (void*)&prod[0], (sizeof(ScalarType)*vec_size), cudaMemcpyHostToDevice)); double xDim = (double) 1024.0/(nVar*nEqn); dim3 blockDim(floor(xDim), nVar, nEqn); @@ -68,10 +66,10 @@ void CSysMatrix::GPUMatrixVectorProduct(const CSysVector GPUMatrixVectorProductAdd<<>>(d_matrix, d_vec, d_prod, d_row_ptr, d_col_ind, nPointDomain, nVar, nEqn); - cudaMemcpy((void*)(&prod[0]), (void*)d_prod, (sizeof(ScalarType)*vec_size), cudaMemcpyDeviceToHost); + gpuErrChk(cudaMemcpy((void*)(&prod[0]), (void*)d_prod, (sizeof(ScalarType)*vec_size), cudaMemcpyDeviceToHost)); - cudaFree(d_vec); - cudaFree(d_prod); + gpuErrChk(cudaFree(d_vec)); + gpuErrChk(cudaFree(d_prod)); } From 02b9eb8a774a2b3b34fa3748c6e4064623544e47 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Wed, 21 Aug 2024 00:38:50 +0530 Subject: [PATCH 12/32] Updated README for final report --- .../linear_algebra/CMatrixVectorProduct.hpp | 20 ++- Common/src/linear_algebra/GPU_lin_alg.cu | 1 + Docs/Report/Benchmark_Coarse.png | Bin 0 -> 25050 bytes Docs/Report/Benchmark_Fine.png | Bin 0 -> 25231 bytes Docs/Report/Benchmark_Finer.png | Bin 0 -> 24686 bytes Docs/Report/Code_Profile.png | Bin 0 -> 18040 bytes Docs/Report/Current_Algorithm.png | Bin 0 -> 193868 bytes Docs/Report/Deliverables.png | Bin 0 -> 228711 bytes Docs/Report/Execution.png | Bin 0 -> 240497 bytes Docs/Report/Future_Implementation.png | Bin 0 -> 25535 bytes Docs/Report/Milestones.png | Bin 0 -> 90426 bytes Docs/Report/Scaling.png | Bin 0 -> 77692 bytes README.md | 142 +++++++++++++++++- 13 files changed, 155 insertions(+), 8 deletions(-) create mode 100644 Docs/Report/Benchmark_Coarse.png create mode 100644 Docs/Report/Benchmark_Fine.png create mode 100644 Docs/Report/Benchmark_Finer.png create mode 100644 Docs/Report/Code_Profile.png create mode 100644 Docs/Report/Current_Algorithm.png create mode 100644 Docs/Report/Deliverables.png create mode 100644 Docs/Report/Execution.png create mode 100644 Docs/Report/Future_Implementation.png create mode 100644 Docs/Report/Milestones.png create mode 100644 Docs/Report/Scaling.png diff --git a/Common/include/linear_algebra/CMatrixVectorProduct.hpp b/Common/include/linear_algebra/CMatrixVectorProduct.hpp index 1dcf6853009..a16b19ab8c5 100644 --- a/Common/include/linear_algebra/CMatrixVectorProduct.hpp +++ b/Common/include/linear_algebra/CMatrixVectorProduct.hpp @@ -104,13 +104,13 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { inline void operator()(const CSysVector& u, CSysVector& v) const override { - /*Create output file*/ - /* + /*Create output file and start counter*/ + +/* std::ofstream serial; auto start = std::chrono::high_resolution_clock::now(); - */ - +*/ if(config->GetCUDA()) { matrix.GPUMatrixVectorProduct(u, v, geometry, config); @@ -121,16 +121,22 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { } - /* + /*Calculate the duration between stop and start counters*/ + +/* auto stop = std::chrono::high_resolution_clock::now(); auto duration = std::chrono::duration_cast(stop - start); double time = duration.count(); +*/ + + /*Open and Append to File*/ - serial.open("serial.txt", std::ios::app); +/* + serial.open("MVP_Exec_Time.txt", std::ios::app); serial << time << "\n"; serial.close(); - */ +*/ } }; diff --git a/Common/src/linear_algebra/GPU_lin_alg.cu b/Common/src/linear_algebra/GPU_lin_alg.cu index 1e9a7be66fd..ffc54505c80 100644 --- a/Common/src/linear_algebra/GPU_lin_alg.cu +++ b/Common/src/linear_algebra/GPU_lin_alg.cu @@ -65,6 +65,7 @@ void CSysMatrix::GPUMatrixVectorProduct(const CSysVector dim3 gridDim(ceil(gridx), 1, 1); GPUMatrixVectorProductAdd<<>>(d_matrix, d_vec, d_prod, d_row_ptr, d_col_ind, nPointDomain, nVar, nEqn); + gpuErrChk( cudaPeekAtLastError() ); gpuErrChk(cudaMemcpy((void*)(&prod[0]), (void*)d_prod, (sizeof(ScalarType)*vec_size), cudaMemcpyDeviceToHost)); diff --git a/Docs/Report/Benchmark_Coarse.png b/Docs/Report/Benchmark_Coarse.png new file mode 100644 index 0000000000000000000000000000000000000000..7af5162191ca2496f035e964297a999e03b0dac7 GIT binary patch literal 25050 zcmb5W1yoe;+BPoZASE!QB0aPW-6`FIq(cZptCZ3R2tx}?@yV|ontwtsiJ7)YxyIGXym%_uU2L_D>2WNlifm%M4j*#cAYm$M4uVt zJqk-{r|w9(_(;Wo=c;OGi3$&0r)=SDj1rHn7UkiG;r$=`G8=w$>-kEe&wQ(_cq|Dq z5Z-epOEAq~sK(jHKnwdCLSp^Sf=9I_c~l`-pk5q_4rIZj1fTY}U`Kot@ALB;>wzk2q)AqI4*cvT;60ndc*F<+^Y}oY%yYI${O9dGa4&-)!JE)xas;}6 z-Yw6S(7I|!jL7{(8>#-U@jL+EDsk);`JeG*pTkeM28vt9|Ff*%Us>H?NZ0%YyZwKy^%|Q3+CKOC2pjGZXP$hEcd29*!|XF8!yMHC%dJR zR?Z~l{^_9qrd1%vbimKYmGsV@E1y}NA{Qs9z7LhKU1laBDo6qbaPG6#jkv 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From 1a902aef32b2378f87e7dde986fd84e1c42f082e Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Wed, 21 Aug 2024 19:26:34 +0530 Subject: [PATCH 13/32] readme update --- README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/README.md b/README.md index 2a185d537a9..741c086acc0 100644 --- a/README.md +++ b/README.md @@ -24,8 +24,6 @@ bash build_SU2.sh ``` A barebones template NVBLAS config file has also been provided for reference. -A lot of warnings come in - ### Usage To start the use of the GPU in any simulation involving the FGMRES solver. Just set the following keyword in the config file to YES From 3fa00a9b3615b21f005e097bf768e28f67aaa00e Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Wed, 21 Aug 2024 21:39:23 +0530 Subject: [PATCH 14/32] readme update --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 741c086acc0..0ef6459ac4d 100644 --- a/README.md +++ b/README.md @@ -157,6 +157,8 @@ This should alleviate the issue of expensive memory transfers to the device to a - Port the inner loop to the GPU - Open a PR as soons as 1 and 2 are finished +Progress PPT - https://www.canva.com/design/DAGJIWWpMcA/JMN7jXbO_YAcEn6VCT_DiA/edit?utm_content=DAGJIWWpMcA&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton + # STANDARD README STARTS HERE

From 57ffb742e3c9e86e9b83d30edc9f92f3b5029dec Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Wed, 21 Aug 2024 21:43:10 +0530 Subject: [PATCH 15/32] readme update --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index 0ef6459ac4d..c15bebedcb5 100644 --- a/README.md +++ b/README.md @@ -157,7 +157,6 @@ This should alleviate the issue of expensive memory transfers to the device to a - Port the inner loop to the GPU - Open a PR as soons as 1 and 2 are finished -Progress PPT - https://www.canva.com/design/DAGJIWWpMcA/JMN7jXbO_YAcEn6VCT_DiA/edit?utm_content=DAGJIWWpMcA&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton # STANDARD README STARTS HERE From b8f14d369b2a44bf6a123821653aed7ea4b53427 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Fri, 23 Aug 2024 23:12:09 +0530 Subject: [PATCH 16/32] Final Graph Changes --- Docs/Report/Exec.png | Bin 0 -> 109437 bytes Docs/Report/Execution.png | Bin 240497 -> 0 bytes Docs/Report/Scaling.png | Bin 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flatplate c ![alt text](Docs/Report/Benchmark_Fine.png) ![alt text](Docs/Report/Benchmark_Finer.png) -It is very promising to see that even with a preliminary algorithm the GPU implementation manages to break out ahead of the single core CPU one. +It is promising to see that even with a preliminary algorithm the GPU implementation manages to break out ahead of the single core CPU one. Comparisons between the total execution time for a larger number of cases is shown as below -A per call comparison is given below. The time taken to execute the CPU and GPU member functions were measured. The results shown are impressive. +![alt text](Docs/Report/Exec.png) -On a simple execution time basis, the GPU implementation is over twice as fast as its CPU counterpart. The following log scale graph is representative of that +Even with increasing problem size the GPU implementation scales well enough to stay ahead of the CPU one although marginally. -![alt text](Docs/Report/Scaling.png) +The time taken to execute only the CPU and GPU member functions were measured. In this case, the GPU implementation is over twice as fast as its CPU counterpart. The following log scale graph is representative of that -The GPU consistently outperforms the CPU calls by a considerable margin. +![alt text](Docs/Report/Single_Call.png) + +The GPU member function consistently outperforms the CPU one by a considerable margin. ## Moving Forward From 3a000e2d863aaf11fb5cf06ec64c4db533327fb7 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Fri, 23 Aug 2024 23:19:00 +0530 Subject: [PATCH 17/32] Image Changes --- Docs/Report/Future_Implementation.png | Bin 25535 -> 30457 bytes README.md | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/Docs/Report/Future_Implementation.png b/Docs/Report/Future_Implementation.png index 9ad336acc93c849d24873d9d345bd30e012324cc..920535acac752a44cf289380e6719f59da767c85 100644 GIT binary patch literal 30457 zcmeEtWmH_t(kQ_hLLe{@2*D+TySqCK?hs^vNpN=yg9LYi4(oYeq z-_G%~v*!!3K_&!rhVhkl?dW?Sf;4CHkpw{nnhs9-XWyj+f7E8*jt;*SG_Raw(i=9e?aLb$|A2?g6N8D^orM(_ z9U+e|2a+ySQ=9drv6Ug#aXe{VMfNd;gr?U_kJXPowp)W%v4AN#{wE?*xbd!8aovFD 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+0530 Subject: [PATCH 18/32] Added Runtime Polymorphism for selecting execution Path --- .../linear_algebra/CMatrixVectorProduct.hpp | 76 +++++++++++-------- Common/src/linear_algebra/CSysMatrix.cpp | 15 ++-- 2 files changed, 55 insertions(+), 36 deletions(-) diff --git a/Common/include/linear_algebra/CMatrixVectorProduct.hpp b/Common/include/linear_algebra/CMatrixVectorProduct.hpp index a16b19ab8c5..3d81fb01cce 100644 --- a/Common/include/linear_algebra/CMatrixVectorProduct.hpp +++ b/Common/include/linear_algebra/CMatrixVectorProduct.hpp @@ -32,12 +32,10 @@ #include "../geometry/CGeometry.hpp" #include "CSysVector.hpp" #include "CSysMatrix.hpp" -#include "GPU_lin_alg.cuh" #include #include #include #include - /*! * \class CMatrixVectorProduct * \ingroup SpLinSys @@ -65,6 +63,30 @@ class CMatrixVectorProduct { template CMatrixVectorProduct::~CMatrixVectorProduct() {} +template +class executionPath{ +public: + virtual void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, const CConfig* config, const CSysMatrix& matrix) = 0; +}; + +template +class cpuExecution : public executionPath { +public: + void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, const CConfig* config, const CSysMatrix& matrix) override + { + matrix.MatrixVectorProduct(u, v, geometry, config); + } +}; + +template +class gpuExecution : public executionPath { +public: + void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, const CConfig* config, const CSysMatrix& matrix) override + { + matrix.GPUMatrixVectorProduct(u, v, geometry, config); + } +}; + /*! * \class CSysMatrixVectorProduct * \ingroup SpLinSys @@ -76,6 +98,7 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { const CSysMatrix& matrix; /*!< \brief pointer to matrix that defines the product. */ CGeometry* geometry; /*!< \brief geometry associated with the matrix. */ const CConfig* config; /*!< \brief config of the problem. */ + executionPath* exec; public: /*! @@ -86,7 +109,17 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { */ inline CSysMatrixVectorProduct(const CSysMatrix& matrix_ref, CGeometry* geometry_ref, const CConfig* config_ref) - : matrix(matrix_ref), geometry(geometry_ref), config(config_ref) {} + : matrix(matrix_ref), geometry(geometry_ref), config(config_ref) + { + if(config->GetCUDA()) + { + exec = new gpuExecution; + } + else + { + exec = new cpuExecution; + } + } /*! * \note This class cannot be default constructed as that would leave us with invalid pointers. @@ -98,46 +131,29 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { * \param[in] u - CSysVector that is being multiplied by the sparse matrix * \param[out] v - CSysVector that is the result of the product */ - - - - inline void operator()(const CSysVector& u, CSysVector& v) const override { - - /*Create output file and start counter*/ - -/* + + /* std::ofstream serial; auto start = std::chrono::high_resolution_clock::now(); -*/ - if(config->GetCUDA()) - { - matrix.GPUMatrixVectorProduct(u, v, geometry, config); - } - else - { - matrix.MatrixVectorProduct(u, v, geometry, config); - } - - - /*Calculate the duration between stop and start counters*/ + */ -/* + exec->mat_vec_prod(u, v, geometry, config, matrix); + + /* auto stop = std::chrono::high_resolution_clock::now(); auto duration = std::chrono::duration_cast(stop - start); double time = duration.count(); -*/ - - /*Open and Append to File*/ -/* serial.open("MVP_Exec_Time.txt", std::ios::app); serial << time << "\n"; serial.close(); -*/ - + */ + } }; + + diff --git a/Common/src/linear_algebra/CSysMatrix.cpp b/Common/src/linear_algebra/CSysMatrix.cpp index 9f1dff8cb1a..233c665b07f 100644 --- a/Common/src/linear_algebra/CSysMatrix.cpp +++ b/Common/src/linear_algebra/CSysMatrix.cpp @@ -137,12 +137,15 @@ void CSysMatrix::Initialize(unsigned long npoint, unsigned long npoi col_ind = csr.innerIdx(); dia_ptr = csr.diagPtr(); - gpuErrChk(cudaMalloc((void**)(&d_row_ptr), (sizeof(row_ptr)*(nPointDomain+1.0)))); - gpuErrChk(cudaMalloc((void**)(&d_col_ind), (sizeof(col_ind)*nnz))); - gpuErrChk(cudaMalloc((void**)(&d_matrix), (sizeof(ScalarType)*nnz*nVar*nEqn))); - - gpuErrChk(cudaMemcpy((void*)(d_row_ptr), (void*)row_ptr, (sizeof(row_ptr)*(nPointDomain+1.0)), cudaMemcpyHostToDevice)); - gpuErrChk(cudaMemcpy((void*)(d_col_ind), (void*)col_ind, (sizeof(col_ind))*nnz, cudaMemcpyHostToDevice)); + if(config->GetCUDA()) + { + gpuErrChk(cudaMalloc((void**)(&d_row_ptr), (sizeof(row_ptr)*(nPointDomain+1.0)))); + gpuErrChk(cudaMalloc((void**)(&d_col_ind), (sizeof(col_ind)*nnz))); + gpuErrChk(cudaMalloc((void**)(&d_matrix), (sizeof(ScalarType)*nnz*nVar*nEqn))); + + gpuErrChk(cudaMemcpy((void*)(d_row_ptr), (void*)row_ptr, (sizeof(row_ptr)*(nPointDomain+1.0)), cudaMemcpyHostToDevice)); + gpuErrChk(cudaMemcpy((void*)(d_col_ind), (void*)col_ind, (sizeof(col_ind))*nnz, cudaMemcpyHostToDevice)); + } if (needTranspPtr) col_ptr = geometry->GetTransposeSparsePatternMap(type).data(); From e131308d58650c813a60cd7e0c5b7fa34bc96f47 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Sat, 24 Aug 2024 02:51:35 +0530 Subject: [PATCH 19/32] Added Runtime Polymorphism to select between CPU and GPU Execution --- .../linear_algebra/CMatrixVectorProduct.hpp | 23 ++++++++++-- Common/include/linear_algebra/GPU_lin_alg.cuh | 36 +++++++++++++++++++ Common/src/linear_algebra/GPU_lin_alg.cu | 28 +++++++++++++++ 3 files changed, 85 insertions(+), 2 deletions(-) diff --git a/Common/include/linear_algebra/CMatrixVectorProduct.hpp b/Common/include/linear_algebra/CMatrixVectorProduct.hpp index 3d81fb01cce..5248df75197 100644 --- a/Common/include/linear_algebra/CMatrixVectorProduct.hpp +++ b/Common/include/linear_algebra/CMatrixVectorProduct.hpp @@ -53,6 +53,12 @@ * handle the different types of matrix-vector products and still be * passed to a single implementation of the Krylov solvers. * This abstraction may also be used to define matrix-free products. + * + * There is also the use of a dummy class being made to select the + * correct function as defined by the user while deciding between + * CPU or GPU execution. This dummy class calls the correct member + * functions from its derived classes to map the suitable path of + * execution - CPU or GPU. */ template class CMatrixVectorProduct { @@ -63,12 +69,21 @@ class CMatrixVectorProduct { template CMatrixVectorProduct::~CMatrixVectorProduct() {} +/*! + * \class executionPath + * \brief Dummy super class that holds the correct member functions in its child classes + */ + template class executionPath{ public: virtual void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, const CConfig* config, const CSysMatrix& matrix) = 0; }; +/*! + * \class cpuExecution + * \brief Derived class containing the CPU Matrix Vector Product Function + */ template class cpuExecution : public executionPath { public: @@ -78,6 +93,10 @@ class cpuExecution : public executionPath { } }; +/*! + * \class gpuExecution + * \brief Derived class containing the GPU Matrix Vector Product Function + */ template class gpuExecution : public executionPath { public: @@ -98,7 +117,7 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { const CSysMatrix& matrix; /*!< \brief pointer to matrix that defines the product. */ CGeometry* geometry; /*!< \brief geometry associated with the matrix. */ const CConfig* config; /*!< \brief config of the problem. */ - executionPath* exec; + executionPath* exec; /*!< \brief interface that decides which path of execution to choose from*/ public: /*! @@ -151,7 +170,7 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { serial << time << "\n"; serial.close(); */ - + } }; diff --git a/Common/include/linear_algebra/GPU_lin_alg.cuh b/Common/include/linear_algebra/GPU_lin_alg.cuh index 2bb7b9a5320..50dfb025da1 100644 --- a/Common/include/linear_algebra/GPU_lin_alg.cuh +++ b/Common/include/linear_algebra/GPU_lin_alg.cuh @@ -1,3 +1,31 @@ +/*! + * \file GPU_lin_alg.cuh + * \brief Declaration of the GPU Matrix Vector Product CUDA Kernel. + * The implemtation is in GPU_lin_alg.cu. + * \author A. Raj + * \version 8.0.1 "Harrier" + * + * SU2 Project Website: https://su2code.github.io + * + * The SU2 Project is maintained by the SU2 Foundation + * (http://su2foundation.org) + * + * Copyright 2012-2024, SU2 Contributors (cf. AUTHORS.md) + * + * SU2 is free software; you can redistribute it and/or + * modify it under the terms of the GNU Lesser General Public + * License as published by the Free Software Foundation; either + * version 2.1 of the License, or (at your option) any later version. + * + * SU2 is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU + * Lesser General Public License for more details. + * + * You should have received a copy of the GNU Lesser General Public + * License along with SU2. If not, see . + */ + #include #include"../../include/linear_algebra/CSysMatrix.hpp" #include"iostream" @@ -11,4 +39,12 @@ inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=t } } +/*! + * \brief CUDA Kernel that performns the Matrix Vector Product: All threads execute product += matrix*vector + * \param[in] matrix - matrix to be multiplied + * \param[in] vec - vector to multiply the matrix with + * \param[in] prod - storing the output of the operation + * \param[in] row_ptr - array of pointers pointing to the first non-zero element in each row of the block matrix + * \param[in] col_ind - column index in each element of the block matrix + */ __global__ void GPUMatrixVectorProductAdd(su2mixedfloat* matrix, double* vec, double* prod, unsigned long* row_ptr, unsigned long* col_ind); \ No newline at end of file diff --git a/Common/src/linear_algebra/GPU_lin_alg.cu b/Common/src/linear_algebra/GPU_lin_alg.cu index ffc54505c80..397e78365ca 100644 --- a/Common/src/linear_algebra/GPU_lin_alg.cu +++ b/Common/src/linear_algebra/GPU_lin_alg.cu @@ -1,3 +1,30 @@ +/*! + * \file CSysSolve.cpp + * \brief Implementation of Matrix Vector Product CUDA Kernel + * \author A. Raj + * \version 8.0.1 "Harrier" + * + * SU2 Project Website: https://su2code.github.io + * + * The SU2 Project is maintained by the SU2 Foundation + * (http://su2foundation.org) + * + * Copyright 2012-2024, SU2 Contributors (cf. AUTHORS.md) + * + * SU2 is free software; you can redistribute it and/or + * modify it under the terms of the GNU Lesser General Public + * License as published by the Free Software Foundation; either + * version 2.1 of the License, or (at your option) any later version. + * + * SU2 is distributed in the hope that it will be useful, + * but WITHOUT ANY WARRANTY; without even the implied warranty of + * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU + * Lesser General Public License for more details. + * + * You should have received a copy of the GNU Lesser General Public + * License along with SU2. If not, see . + */ + #include "../../include/linear_algebra/CSysMatrix.inl" #include "../../include/linear_algebra/CSysMatrix.hpp" #include "../../include/linear_algebra/GPU_lin_alg.cuh" @@ -41,6 +68,7 @@ __global__ void GPUMatrixVectorProductAdd(matrixType* matrix, vectorType* vec, v } + template void CSysMatrix::GPUMatrixVectorProduct(const CSysVector& vec, CSysVector& prod, CGeometry* geometry, const CConfig* config) const From 7695314ec349bc5468c433a2eb9d36f967ababb2 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Sat, 24 Aug 2024 03:19:41 +0530 Subject: [PATCH 20/32] Added runtime polymorphism to select execution path --- Common/src/linear_algebra/GPU_lin_alg.cu | 4 +--- doxyfile | 7 +++++-- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/Common/src/linear_algebra/GPU_lin_alg.cu b/Common/src/linear_algebra/GPU_lin_alg.cu index 397e78365ca..7b852780db9 100644 --- a/Common/src/linear_algebra/GPU_lin_alg.cu +++ b/Common/src/linear_algebra/GPU_lin_alg.cu @@ -1,5 +1,5 @@ /*! - * \file CSysSolve.cpp + * \file GPU_lin_alg.cu * \brief Implementation of Matrix Vector Product CUDA Kernel * \author A. Raj * \version 8.0.1 "Harrier" @@ -25,10 +25,8 @@ * License along with SU2. If not, see . */ -#include "../../include/linear_algebra/CSysMatrix.inl" #include "../../include/linear_algebra/CSysMatrix.hpp" #include "../../include/linear_algebra/GPU_lin_alg.cuh" -#include "../../include/geometry/CGeometry.hpp" #ifndef gpuErrChk #define gpuErrChk(ans) { gpuAssert((ans), __FILE__, __LINE__); } diff --git a/doxyfile b/doxyfile index b249d12d1b1..d7f30a3c83f 100755 --- a/doxyfile +++ b/doxyfile @@ -291,7 +291,8 @@ OPTIMIZE_OUTPUT_VHDL = NO # Note that for custom extensions you also need to set FILE_PATTERNS otherwise # the files are not read by doxygen. -EXTENSION_MAPPING = +EXTENSION_MAPPING = cu=c++ \ + cuh=c++ # If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments # according to the Markdown format, which allows for more readable @@ -818,7 +819,9 @@ INPUT_ENCODING = UTF-8 FILE_PATTERNS = *.cpp \ *.inl \ *.hpp \ - *.md + *.md \ + *.cu \ + *.cuh # The RECURSIVE tag can be used to specify whether or not subdirectories should # be searched for input files as well. From 809c2d058585aef0263e7d9ad4407e10bdc521dc Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Sat, 24 Aug 2024 03:20:50 +0530 Subject: [PATCH 21/32] Added runtime polymorphism to select execution path --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index af0f5bf8037..0ce53535bf8 100644 --- a/README.md +++ b/README.md @@ -155,7 +155,7 @@ This should alleviate the issue of expensive memory transfers to the device to a ## To Do List - Make sure that error handling is implemented properly -- Work on removing the unnecessary warnings that are currently appearing during compilation +- **FIXED - 24/08/2024** Work on removing the unnecessary warnings that are currently appearing during compilation - Port the inner loop to the GPU - Open a PR as soons as 1 and 2 are finished From 9832f33cf6b2bc5c325ff806cda1e2745a884fd7 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Sat, 24 Aug 2024 03:24:33 +0530 Subject: [PATCH 22/32] Added Runtime Polymorphism to select between CPU and GPU Execution --- .gitignore | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index a36aa4a5342..506b4589f59 100644 --- a/.gitignore +++ b/.gitignore @@ -107,4 +107,5 @@ su2preconfig.timestamp #Testcases Folders FEM_AREEN_MESH/ FVM/ -naca0012/ \ No newline at end of file +naca0012/ +Docs/html \ No newline at end of file From 1f41592f349f4c49f3baf3079c8d1d02d8dd5297 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Sat, 24 Aug 2024 03:41:59 +0530 Subject: [PATCH 23/32] Added Runtime Polymorphism to select between CPU and GPU Execution --- Common/include/linear_algebra/GPU_lin_alg.cuh | 20 +++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/Common/include/linear_algebra/GPU_lin_alg.cuh b/Common/include/linear_algebra/GPU_lin_alg.cuh index 50dfb025da1..6e8601dcb02 100644 --- a/Common/include/linear_algebra/GPU_lin_alg.cuh +++ b/Common/include/linear_algebra/GPU_lin_alg.cuh @@ -30,6 +30,13 @@ #include"../../include/linear_algebra/CSysMatrix.hpp" #include"iostream" +/*! + * \brief assert style function that reads return codes after intercepting CUDA API calls. + * It returns the result code and its location if the call is unsuccessful. + * \param[in] code - result code of CUDA function + * \param[in] file - name of file holding the function + * \param[in] line - line containing the function + */ inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true) { if (code != cudaSuccess) @@ -40,11 +47,16 @@ inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=t } /*! - * \brief CUDA Kernel that performns the Matrix Vector Product: All threads execute product += matrix*vector + * \brief CUDA Kernel that performs the Matrix Vector Product: All threads execute product += matrix*vector * \param[in] matrix - matrix to be multiplied * \param[in] vec - vector to multiply the matrix with * \param[in] prod - storing the output of the operation - * \param[in] row_ptr - array of pointers pointing to the first non-zero element in each row of the block matrix - * \param[in] col_ind - column index in each element of the block matrix + * \param[in] d_row_ptr - a device array of pointers pointing to the first non-zero element in each row of the block matrix + * \param[in] d_col_ind - a device array holding the column index of each element of the block matrix + * \param[in] nPointDomain - number of real points of the mesh + * \param[in] nVar - number of variables of the problem + * \param[in] nEqn - number of equations of the problem */ -__global__ void GPUMatrixVectorProductAdd(su2mixedfloat* matrix, double* vec, double* prod, unsigned long* row_ptr, unsigned long* col_ind); \ No newline at end of file + +template +__global__ void GPUMatrixVectorProductAdd(matrixType* matrix, vectorType* vec, vectorType* prod, unsigned long* d_row_ptr, unsigned long* d_col_ind, unsigned long nPointDomain, unsigned long nVar, unsigned long nEqn); \ No newline at end of file From de0810a0220fb7e67ccea290b06ab76a249b7bc5 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Sat, 24 Aug 2024 23:28:13 +0530 Subject: [PATCH 24/32] Create REPORT.md Created Final Report --- REPORT.md | 160 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 160 insertions(+) create mode 100644 REPORT.md diff --git a/REPORT.md b/REPORT.md new file mode 100644 index 00000000000..01a96f9b4c6 --- /dev/null +++ b/REPORT.md @@ -0,0 +1,160 @@ +# Google Summer of Code '24 Project - Feasibility of GPU Acceleration in SU2 + +This code repository contains a version of the SU2 codebase that has CUDA support enabled for offloading particular linear algebra options to the GPU. The current implementations show promise for increased performance with further optimizations and changes. We are also currently working in a new research direction as will be outlined further below. + +## Compiling and Running the Code + +### Compiling +Currently two modifications exist in the code +- NVBLAS Integration which intercepts calls for the Space Integration in the DG Solver - not very useful +- Acceleration of program wide Matrix Vector Product by outsourcing to a CUDA Kernel + +Both can be enabled by giving meson the following option during compilation +``` +-Denable-cuda=true +``` + +You will also need to specify the environment variable CUDA_PATH with the location of the installed CUDA Folder - usually found in the /usr/local directory + +A script has been provided which allows you to input your specific paths and use to compile. **We recommend not using MPI currently** as splitting up chunks of the domain will cause issues with predefined memory transfer (that's not good, I know, I'll fix it) + +To run the script just go +``` +bash build_SU2.sh +``` +A barebones template NVBLAS config file has also been provided for reference. + +### Usage + +To start the use of the GPU in any simulation involving the FGMRES solver. Just set the following keyword in the config file to YES + +``` +ENABLE_CUDA=YES +``` + +Error handling is done by an Error Check Wrapper and should provide exit codes at the file and line of the anomaly. + +### Benchmarking + +To benchmark the individual member functions, the following sections of code in the CMatrixVectorProduct.hpp file have to be uncommented. + +``` + + std::ofstream serial; + + auto start = std::chrono::high_resolution_clock::now(); +. +. +. + + auto stop = std::chrono::high_resolution_clock::now(); + auto duration = std::chrono::duration_cast(stop - start); + + double time = duration.count(); + + serial.open("MVP_Exec_Time.txt", std::ios::app); + serial << time << "\n"; + serial.close(); + +``` + +This will create a file named MVP_Exec_Time.txt which contains the execution time in microseconds. Always remove or delete the file before starting another run or else the new times will get appended onto it. + +Also it is recommended to disable GPU Error Check functions before starting as this may cause excessive overhead in execution. These may be removed from the GPU_lin_alg.cu and CSysMatrix.cpp files. + +## Initial Proposal and Deliverables + +This section outlines the original plan of the project and the changes that were made throughout the course of its duration. + +Initially we decided to approach the addition of GPU functionality to SU2 using the NVBLAS framework. Since the FEM solver used level 3 BLAS calls which can be intercepted by the NVBLAS library directly and routed to the GPU if it is deemed that the particular call would execute faster on the GPU. + +Our proposed milestones were the implementation and further optimization of this approach. + +![alt text](Docs/Report/Milestones.png) + +After creating an NVBLAS enabled version of the code, our investigation revealed that the BLAS calls are made only during the Space Integration subroutine that would not give us a sizeable speedup in the code. + +The major chunk of linear algebra operations happens while using an implicit time scheme that involves solving a matrix system. This Time Integration subroutine is also one of the most computationally intensive sections of the code. + +## Pivoting to a more Practical Approach + +This is the reason that the NVBLAS integration did not offer a path of execution that could provide considerable speed up to our execution time. Therefore, we chose to switch our focus to a more impactful implementation by directly working on the linear solvers that handle the Matrix operations in the Time Integration subroutine. + +This would + +- Offer a bigger overall boost to the performance of the code as we would be targetting a critical area +- Automatically extend this endeavor to other solvers, not only fulfilling the fourth milestone, but also being a wide-reaching solution. + +**We took this decision keeping in mind that the Summer of Code program is not geared towards only fulfilling deliverables and keeping in line with timelines. But that it serves the overall goal of writing more useful code that has a bigger effect.** + +![alt text](Docs/Report/Deliverables.png) + +- An NVBLAS integrated version was successfully created by linking the necessary library into the code +- Acceleration of the program was made possible by outsourcing Matrix-Vector Products usinga a CUDA kernel +- This file serves as the execution + extension report that is mentioned above. NSight tools were used to profile the code as initially proposed + +## Current Implementation + +For the working problem, we have focused on two aspects of the solving algorithm + +- The FGMRES solver that heavily relies on Matrix-Vector Products +- Owing to the above point, we modify the matrix vector product class and functions + +A flowchart of the current algorithm that we use is presented below. + +![alt text](Docs/Report/Current_Algorithm.png) + +The GPUMatrixVectorProduct Member Function belongs to the CSysMatrix Class + +- The function makes cudaMalloc and cudaMemcpy API calls to transfer the participating matrix and vector data to the GPU +- The GPUMatrixVectorProductAdd kernel is launched which multiplies the block matrix with the vector using CUDA +- The data of the Product Vector is transferred back to the CPU and the device pointers are freed + +## Code Profiling + +Using NSight Systems, we can get the analysis of the implementation. This profiling was done for the flat plate case in the FVM NSS Solver with the number of elements being 4096. + +![alt text](Docs/Report/Code_Profile.png) + +As expected, the memory transfer between the host and device comprises of the main downtime in this implementation (2.315 ms per call). The Kernel launch itself is only 0.014 ms long. + +## Results + +All benchmarks are done without GPU error checking as it adds a considerable overhead to the entire operation. + +The following results were done with increasing mesh density for the flatplate case. + +![alt text](Docs/Report/Benchmark_Coarse.png) +![alt text](Docs/Report/Benchmark_Fine.png) +![alt text](Docs/Report/Benchmark_Finer.png) + +It is promising to see that even with a preliminary algorithm the GPU implementation manages to break out ahead of the single core CPU one. Comparisons between the total execution time for a larger number of cases is shown as below + +![alt text](Docs/Report/Exec.png) + +Even with increasing problem size the GPU implementation scales well enough to stay ahead of the CPU one although marginally. + +The time taken to execute only the CPU and GPU member functions were measured. In this case, the GPU implementation is over twice as fast as its CPU counterpart. The following log scale graph is representative of that + +![alt text](Docs/Report/Single_Call.png) + +The GPU member function consistently outperforms the CPU one by a considerable margin. + +## Moving Forward + +If the number of the repeated memory transfers can be reduced then we can gain a considerable boost as cudaMemcpy is invoked per call. The individual performance of the member function clearly shows an exploitable edge when it comes to the linear algebra functions. + +Our current direction happens to be to cut down on the transfers by porting not only each iteration of the FGMRES inner loop but the entire loop itself to the GPU. This allows us for only one single memory transfer per iteration. + +The algorithm would be changed approximately to what is represented below. + + + +This should alleviate the issue of expensive memory transfers to the device to a sizeable extent, firther providing a performance uplift. **This line of work will be pursued even after the finishing of the GSoC Program** + +## To Do List + +- Make sure that error handling is implemented properly +- **FIXED - 24/08/2024** Work on removing the unnecessary warnings that are currently appearing during compilation +- Port the inner loop to the GPU +- Open a PR as soons as 1 and 2 are finished From 66878d196cf474446c9e942f5eb6845dcc818e2a Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Sat, 24 Aug 2024 23:28:55 +0530 Subject: [PATCH 25/32] Delete REPORT.md --- REPORT.md | 160 ------------------------------------------------------ 1 file changed, 160 deletions(-) delete mode 100644 REPORT.md diff --git a/REPORT.md b/REPORT.md deleted file mode 100644 index 01a96f9b4c6..00000000000 --- a/REPORT.md +++ /dev/null @@ -1,160 +0,0 @@ -# Google Summer of Code '24 Project - Feasibility of GPU Acceleration in SU2 - -This code repository contains a version of the SU2 codebase that has CUDA support enabled for offloading particular linear algebra options to the GPU. The current implementations show promise for increased performance with further optimizations and changes. We are also currently working in a new research direction as will be outlined further below. - -## Compiling and Running the Code - -### Compiling -Currently two modifications exist in the code -- NVBLAS Integration which intercepts calls for the Space Integration in the DG Solver - not very useful -- Acceleration of program wide Matrix Vector Product by outsourcing to a CUDA Kernel - -Both can be enabled by giving meson the following option during compilation -``` --Denable-cuda=true -``` - -You will also need to specify the environment variable CUDA_PATH with the location of the installed CUDA Folder - usually found in the /usr/local directory - -A script has been provided which allows you to input your specific paths and use to compile. **We recommend not using MPI currently** as splitting up chunks of the domain will cause issues with predefined memory transfer (that's not good, I know, I'll fix it) - -To run the script just go -``` -bash build_SU2.sh -``` -A barebones template NVBLAS config file has also been provided for reference. - -### Usage - -To start the use of the GPU in any simulation involving the FGMRES solver. Just set the following keyword in the config file to YES - -``` -ENABLE_CUDA=YES -``` - -Error handling is done by an Error Check Wrapper and should provide exit codes at the file and line of the anomaly. - -### Benchmarking - -To benchmark the individual member functions, the following sections of code in the CMatrixVectorProduct.hpp file have to be uncommented. - -``` - - std::ofstream serial; - - auto start = std::chrono::high_resolution_clock::now(); -. -. -. - - auto stop = std::chrono::high_resolution_clock::now(); - auto duration = std::chrono::duration_cast(stop - start); - - double time = duration.count(); - - serial.open("MVP_Exec_Time.txt", std::ios::app); - serial << time << "\n"; - serial.close(); - -``` - -This will create a file named MVP_Exec_Time.txt which contains the execution time in microseconds. Always remove or delete the file before starting another run or else the new times will get appended onto it. - -Also it is recommended to disable GPU Error Check functions before starting as this may cause excessive overhead in execution. These may be removed from the GPU_lin_alg.cu and CSysMatrix.cpp files. - -## Initial Proposal and Deliverables - -This section outlines the original plan of the project and the changes that were made throughout the course of its duration. - -Initially we decided to approach the addition of GPU functionality to SU2 using the NVBLAS framework. Since the FEM solver used level 3 BLAS calls which can be intercepted by the NVBLAS library directly and routed to the GPU if it is deemed that the particular call would execute faster on the GPU. - -Our proposed milestones were the implementation and further optimization of this approach. - -![alt text](Docs/Report/Milestones.png) - -After creating an NVBLAS enabled version of the code, our investigation revealed that the BLAS calls are made only during the Space Integration subroutine that would not give us a sizeable speedup in the code. - -The major chunk of linear algebra operations happens while using an implicit time scheme that involves solving a matrix system. This Time Integration subroutine is also one of the most computationally intensive sections of the code. - -## Pivoting to a more Practical Approach - -This is the reason that the NVBLAS integration did not offer a path of execution that could provide considerable speed up to our execution time. Therefore, we chose to switch our focus to a more impactful implementation by directly working on the linear solvers that handle the Matrix operations in the Time Integration subroutine. - -This would - -- Offer a bigger overall boost to the performance of the code as we would be targetting a critical area -- Automatically extend this endeavor to other solvers, not only fulfilling the fourth milestone, but also being a wide-reaching solution. - -**We took this decision keeping in mind that the Summer of Code program is not geared towards only fulfilling deliverables and keeping in line with timelines. But that it serves the overall goal of writing more useful code that has a bigger effect.** - -![alt text](Docs/Report/Deliverables.png) - -- An NVBLAS integrated version was successfully created by linking the necessary library into the code -- Acceleration of the program was made possible by outsourcing Matrix-Vector Products usinga a CUDA kernel -- This file serves as the execution + extension report that is mentioned above. NSight tools were used to profile the code as initially proposed - -## Current Implementation - -For the working problem, we have focused on two aspects of the solving algorithm - -- The FGMRES solver that heavily relies on Matrix-Vector Products -- Owing to the above point, we modify the matrix vector product class and functions - -A flowchart of the current algorithm that we use is presented below. - -![alt text](Docs/Report/Current_Algorithm.png) - -The GPUMatrixVectorProduct Member Function belongs to the CSysMatrix Class - -- The function makes cudaMalloc and cudaMemcpy API calls to transfer the participating matrix and vector data to the GPU -- The GPUMatrixVectorProductAdd kernel is launched which multiplies the block matrix with the vector using CUDA -- The data of the Product Vector is transferred back to the CPU and the device pointers are freed - -## Code Profiling - -Using NSight Systems, we can get the analysis of the implementation. This profiling was done for the flat plate case in the FVM NSS Solver with the number of elements being 4096. - -![alt text](Docs/Report/Code_Profile.png) - -As expected, the memory transfer between the host and device comprises of the main downtime in this implementation (2.315 ms per call). The Kernel launch itself is only 0.014 ms long. - -## Results - -All benchmarks are done without GPU error checking as it adds a considerable overhead to the entire operation. - -The following results were done with increasing mesh density for the flatplate case. - -![alt text](Docs/Report/Benchmark_Coarse.png) -![alt text](Docs/Report/Benchmark_Fine.png) -![alt text](Docs/Report/Benchmark_Finer.png) - -It is promising to see that even with a preliminary algorithm the GPU implementation manages to break out ahead of the single core CPU one. Comparisons between the total execution time for a larger number of cases is shown as below - -![alt text](Docs/Report/Exec.png) - -Even with increasing problem size the GPU implementation scales well enough to stay ahead of the CPU one although marginally. - -The time taken to execute only the CPU and GPU member functions were measured. In this case, the GPU implementation is over twice as fast as its CPU counterpart. The following log scale graph is representative of that - -![alt text](Docs/Report/Single_Call.png) - -The GPU member function consistently outperforms the CPU one by a considerable margin. - -## Moving Forward - -If the number of the repeated memory transfers can be reduced then we can gain a considerable boost as cudaMemcpy is invoked per call. The individual performance of the member function clearly shows an exploitable edge when it comes to the linear algebra functions. - -Our current direction happens to be to cut down on the transfers by porting not only each iteration of the FGMRES inner loop but the entire loop itself to the GPU. This allows us for only one single memory transfer per iteration. - -The algorithm would be changed approximately to what is represented below. - - - -This should alleviate the issue of expensive memory transfers to the device to a sizeable extent, firther providing a performance uplift. **This line of work will be pursued even after the finishing of the GSoC Program** - -## To Do List - -- Make sure that error handling is implemented properly -- **FIXED - 24/08/2024** Work on removing the unnecessary warnings that are currently appearing during compilation -- Port the inner loop to the GPU -- Open a PR as soons as 1 and 2 are finished From 729bfc823141559d371c49617e42fd76dde2dbac Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Sun, 25 Aug 2024 01:07:48 +0530 Subject: [PATCH 26/32] Added Preprocessor Directives --- .../linear_algebra/CMatrixVectorProduct.hpp | 32 +--- Common/src/linear_algebra/CSysMatrix.cpp | 14 +- Docs/Report/Benchmark_Coarse.png | Bin 25050 -> 0 bytes Docs/Report/Benchmark_Fine.png | Bin 25231 -> 0 bytes Docs/Report/Benchmark_Finer.png | Bin 24686 -> 0 bytes Docs/Report/Code_Profile.png | Bin 18040 -> 0 bytes Docs/Report/Current_Algorithm.png | Bin 193868 -> 0 bytes Docs/Report/Deliverables.png | Bin 228711 -> 0 bytes Docs/Report/Exec.png | Bin 109437 -> 0 bytes Docs/Report/Future_Implementation.png | Bin 30457 -> 0 bytes Docs/Report/Milestones.png | Bin 90426 -> 0 bytes Docs/Report/Single_Call.png | Bin 109440 -> 0 bytes README.md | 164 ------------------ build_SU2_CPU.sh | 7 + 14 files changed, 23 insertions(+), 194 deletions(-) delete mode 100644 Docs/Report/Benchmark_Coarse.png delete mode 100644 Docs/Report/Benchmark_Fine.png delete mode 100644 Docs/Report/Benchmark_Finer.png delete mode 100644 Docs/Report/Code_Profile.png delete mode 100644 Docs/Report/Current_Algorithm.png delete mode 100644 Docs/Report/Deliverables.png delete mode 100644 Docs/Report/Exec.png delete mode 100644 Docs/Report/Future_Implementation.png delete mode 100644 Docs/Report/Milestones.png delete mode 100644 Docs/Report/Single_Call.png create mode 100644 build_SU2_CPU.sh diff --git a/Common/include/linear_algebra/CMatrixVectorProduct.hpp b/Common/include/linear_algebra/CMatrixVectorProduct.hpp index 5248df75197..6db12fcf7aa 100644 --- a/Common/include/linear_algebra/CMatrixVectorProduct.hpp +++ b/Common/include/linear_algebra/CMatrixVectorProduct.hpp @@ -32,10 +32,7 @@ #include "../geometry/CGeometry.hpp" #include "CSysVector.hpp" #include "CSysMatrix.hpp" -#include -#include -#include -#include + /*! * \class CMatrixVectorProduct * \ingroup SpLinSys @@ -102,7 +99,11 @@ class gpuExecution : public executionPath { public: void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, const CConfig* config, const CSysMatrix& matrix) override { + #ifdef HAVE_CUDA matrix.GPUMatrixVectorProduct(u, v, geometry, config); + #else + matrix.MatrixVectorProduct(u, v, geometry, config); + #endif } }; @@ -117,7 +118,7 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { const CSysMatrix& matrix; /*!< \brief pointer to matrix that defines the product. */ CGeometry* geometry; /*!< \brief geometry associated with the matrix. */ const CConfig* config; /*!< \brief config of the problem. */ - executionPath* exec; /*!< \brief interface that decides which path of execution to choose from*/ + executionPath* exec; /*!< \brief interface that decides which path of execution to choose from. */ public: /*! @@ -152,27 +153,8 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { */ inline void operator()(const CSysVector& u, CSysVector& v) const override { - /* - std::ofstream serial; - - auto start = std::chrono::high_resolution_clock::now(); - */ - - exec->mat_vec_prod(u, v, geometry, config, matrix); - - /* - auto stop = std::chrono::high_resolution_clock::now(); - auto duration = std::chrono::duration_cast(stop - start); - - double time = duration.count(); - - serial.open("MVP_Exec_Time.txt", std::ios::app); - serial << time << "\n"; - serial.close(); - */ + exec->mat_vec_prod(u, v, geometry, config, matrix); } }; - - diff --git a/Common/src/linear_algebra/CSysMatrix.cpp b/Common/src/linear_algebra/CSysMatrix.cpp index 233c665b07f..69aedfa0b22 100644 --- a/Common/src/linear_algebra/CSysMatrix.cpp +++ b/Common/src/linear_algebra/CSysMatrix.cpp @@ -28,16 +28,20 @@ #include "../../include/linear_algebra/CSysMatrix.inl" #include "../../include/linear_algebra/CSysMatrix.hpp" -#include "../../include/linear_algebra/GPU_lin_alg.cuh" #include "../../include/geometry/CGeometry.hpp" #include "../../include/toolboxes/allocation_toolbox.hpp" #include -#ifndef gpuErrChk +#ifdef HAVE_CUDA +#include "../../include/linear_algebra/GPU_lin_alg.cuh" + +#ifndef gpuErrChk #define gpuErrChk(ans) { gpuAssert((ans), __FILE__, __LINE__); } #endif +#endif + template CSysMatrix::CSysMatrix() : rank(SU2_MPI::GetRank()), size(SU2_MPI::GetSize()) { nPoint = nPointDomain = nVar = nEqn = 0; @@ -137,15 +141,15 @@ void CSysMatrix::Initialize(unsigned long npoint, unsigned long npoi col_ind = csr.innerIdx(); dia_ptr = csr.diagPtr(); - if(config->GetCUDA()) - { +#if defined(HAVE_CUDA) gpuErrChk(cudaMalloc((void**)(&d_row_ptr), (sizeof(row_ptr)*(nPointDomain+1.0)))); gpuErrChk(cudaMalloc((void**)(&d_col_ind), (sizeof(col_ind)*nnz))); gpuErrChk(cudaMalloc((void**)(&d_matrix), (sizeof(ScalarType)*nnz*nVar*nEqn))); gpuErrChk(cudaMemcpy((void*)(d_row_ptr), (void*)row_ptr, (sizeof(row_ptr)*(nPointDomain+1.0)), cudaMemcpyHostToDevice)); gpuErrChk(cudaMemcpy((void*)(d_col_ind), (void*)col_ind, (sizeof(col_ind))*nnz, cudaMemcpyHostToDevice)); - } + +#endif if (needTranspPtr) col_ptr = geometry->GetTransposeSparsePatternMap(type).data(); diff --git a/Docs/Report/Benchmark_Coarse.png b/Docs/Report/Benchmark_Coarse.png deleted file mode 100644 index 7af5162191ca2496f035e964297a999e03b0dac7..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 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z0Zj=l8X?RC5vMlD&16gC;Z_h%Fk?GVnZSM5j&{_-s0c%$Hh%3V=x*nKSj}IMbiTmb z4@@#{B^43x!c+!XaKAo+FCy_Zy{H9@Yz_C`%kM%9n-HlX1GI@N1TGJpx}ybTSG?8C z>oc*vM5$!so6|dQ1Kkh4UB-_x1^)fW$)q{c77?s8U{n<{!$i^*xI_`_JB63~+L7uG zlD4~Tx9b~{WN^k50Wy&n=LVQCtOdyB6m|zhYFdo1A$mzUn~TBCs}6tO0f!m{?gY*S z2x}6}diXD3DFjdhtv`<8Bw)iF`N;9`)YENE_-Udl0M=iKx9+=o7%HE@ z+L8AG>WMrTamYU~aQ)34@#f7HKr0S3qDH~nizui%{H7je1#kVy{Fvr9!HjPrz<2Nk z2ZE{+?RSm~piy;%(6C|AgbWv3klZ1r0uc#q1b_+AXEoB3_O1gu>HIO=R(Vf6%=d z_Mc8D1G}VBHWdXq=RXTL0SgFVM_#si4{%)xa8MAK*jAko-n?lObb4v;d7$?dByWBI zE|^@fX*X~jzsz{{)TzKWz>Y8g+3L4b{4y#A)xf*p1I7_ywWSkMgGuy}KGIw)d}`~amD&=dzSM)|DYYycJ_z=g@qXpMXtC;GhO*bQYK_fW5tsz(%dsN4r!1SGVy>1N#8&z=WI) z9FPX~o+sV}x)8W@;V?)mFnECd&)L(aO`ARYHn1~3JAYp#Fl&M?q`0RJ%yJW!C;?aV lEGh;vK_Fp$1o0y}|K-J(KhWsO>G=oJ>gnp|vd$@?2>{l%nQj08 diff --git a/README.md b/README.md index 0ce53535bf8..dd3b86878a4 100644 --- a/README.md +++ b/README.md @@ -1,167 +1,3 @@ -# Google Summer of Code '24 Project - Feasibility of GPU Acceleration in SU2 - -This code repository contains a version of the SU2 codebase that has CUDA support enabled for offloading particular linear algebra options to the GPU. The current implementations show promise for increased performance with further optimizations and changes. We are also currently working in a new research direction as will be outlined further below. - -## Compiling and Running the Code - -### Compiling -Currently two modifications exist in the code -- NVBLAS Integration which intercepts calls for the Space Integration in the DG Solver - not very useful -- Acceleration of program wide Matrix Vector Product by outsourcing to a CUDA Kernel - -Both can be enabled by giving meson the following option during compilation -``` --Denable-cuda=true -``` - -You will also need to specify the environment variable CUDA_PATH with the location of the installed CUDA Folder - usually found in the /usr/local directory - -A script has been provided which allows you to input your specific paths and use to compile. **We recommend not using MPI currently** as splitting up chunks of the domain will cause issues with predefined memory transfer (that's not good, I know, I'll fix it) - -To run the script just go -``` -bash build_SU2.sh -``` -A barebones template NVBLAS config file has also been provided for reference. - -### Usage - -To start the use of the GPU in any simulation involving the FGMRES solver. Just set the following keyword in the config file to YES - -``` -ENABLE_CUDA=YES -``` - -Error handling is done by an Error Check Wrapper and should provide exit codes at the file and line of the anomaly. - -### Benchmarking - -To benchmark the individual member functions, the following sections of code in the CMatrixVectorProduct.hpp file have to be uncommented. - -``` - - std::ofstream serial; - - auto start = std::chrono::high_resolution_clock::now(); -. -. -. - - auto stop = std::chrono::high_resolution_clock::now(); - auto duration = std::chrono::duration_cast(stop - start); - - double time = duration.count(); - - serial.open("MVP_Exec_Time.txt", std::ios::app); - serial << time << "\n"; - serial.close(); - -``` - -This will create a file named MVP_Exec_Time.txt which contains the execution time in microseconds. Always remove or delete the file before starting another run or else the new times will get appended onto it. - -Also it is recommended to disable GPU Error Check functions before starting as this may cause excessive overhead in execution. These may be removed from the GPU_lin_alg.cu and CSysMatrix.cpp files. - -## Initial Proposal and Deliverables - -This section outlines the original plan of the project and the changes that were made throughout the course of its duration. - -Initially we decided to approach the addition of GPU functionality to SU2 using the NVBLAS framework. Since the FEM solver used level 3 BLAS calls which can be intercepted by the NVBLAS library directly and routed to the GPU if it is deemed that the particular call would execute faster on the GPU. - -Our proposed milestones were the implementation and further optimization of this approach. - -![alt text](Docs/Report/Milestones.png) - -After creating an NVBLAS enabled version of the code, our investigation revealed that the BLAS calls are made only during the Space Integration subroutine that would not give us a sizeable speedup in the code. - -The major chunk of linear algebra operations happens while using an implicit time scheme that involves solving a matrix system. This Time Integration subroutine is also one of the most computationally intensive sections of the code. - -## Pivoting to a more Practical Approach - -This is the reason that the NVBLAS integration did not offer a path of execution that could provide considerable speed up to our execution time. Therefore, we chose to switch our focus to a more impactful implementation by directly working on the linear solvers that handle the Matrix operations in the Time Integration subroutine. - -This would - -- Offer a bigger overall boost to the performance of the code as we would be targetting a critical area -- Automatically extend this endeavor to other solvers, not only fulfilling the fourth milestone, but also being a wide-reaching solution. - -**We took this decision keeping in mind that the Summer of Code program is not geared towards only fulfilling deliverables and keeping in line with timelines. But that it serves the overall goal of writing more useful code that has a bigger effect.** - -![alt text](Docs/Report/Deliverables.png) - -- An NVBLAS integrated version was successfully created by linking the necessary library into the code -- Acceleration of the program was made possible by outsourcing Matrix-Vector Products usinga a CUDA kernel -- This file serves as the execution + extension report that is mentioned above. NSight tools were used to profile the code as initially proposed - -## Current Implementation - -For the working problem, we have focused on two aspects of the solving algorithm - -- The FGMRES solver that heavily relies on Matrix-Vector Products -- Owing to the above point, we modify the matrix vector product class and functions - -A flowchart of the current algorithm that we use is presented below. - -![alt text](Docs/Report/Current_Algorithm.png) - -The GPUMatrixVectorProduct Member Function belongs to the CSysMatrix Class - -- The function makes cudaMalloc and cudaMemcpy API calls to transfer the participating matrix and vector data to the GPU -- The GPUMatrixVectorProductAdd kernel is launched which multiplies the block matrix with the vector using CUDA -- The data of the Product Vector is transferred back to the CPU and the device pointers are freed - -## Code Profiling - -Using NSight Systems, we can get the analysis of the implementation. This profiling was done for the flat plate case in the FVM NSS Solver with the number of elements being 4096. - -![alt text](Docs/Report/Code_Profile.png) - -As expected, the memory transfer between the host and device comprises of the main downtime in this implementation (2.315 ms per call). The Kernel launch itself is only 0.014 ms long. - -## Results - -All benchmarks are done without GPU error checking as it adds a considerable overhead to the entire operation. - -The following results were done with increasing mesh density for the flatplate case. - -![alt text](Docs/Report/Benchmark_Coarse.png) -![alt text](Docs/Report/Benchmark_Fine.png) -![alt text](Docs/Report/Benchmark_Finer.png) - -It is promising to see that even with a preliminary algorithm the GPU implementation manages to break out ahead of the single core CPU one. Comparisons between the total execution time for a larger number of cases is shown as below - -![alt text](Docs/Report/Exec.png) - -Even with increasing problem size the GPU implementation scales well enough to stay ahead of the CPU one although marginally. - -The time taken to execute only the CPU and GPU member functions were measured. In this case, the GPU implementation is over twice as fast as its CPU counterpart. The following log scale graph is representative of that - -![alt text](Docs/Report/Single_Call.png) - -The GPU member function consistently outperforms the CPU one by a considerable margin. - -## Moving Forward - -If the number of the repeated memory transfers can be reduced then we can gain a considerable boost as cudaMemcpy is invoked per call. The individual performance of the member function clearly shows an exploitable edge when it comes to the linear algebra functions. - -Our current direction happens to be to cut down on the transfers by porting not only each iteration of the FGMRES inner loop but the entire loop itself to the GPU. This allows us for only one single memory transfer per iteration. - -The algorithm would be changed approximately to what is represented below. - - - -This should alleviate the issue of expensive memory transfers to the device to a sizeable extent, firther providing a performance uplift. **This line of work will be pursued even after the finishing of the GSoC Program** - -## To Do List - -- Make sure that error handling is implemented properly -- **FIXED - 24/08/2024** Work on removing the unnecessary warnings that are currently appearing during compilation -- Port the inner loop to the GPU -- Open a PR as soons as 1 and 2 are finished - - -# STANDARD README STARTS HERE -

diff --git a/build_SU2_CPU.sh b/build_SU2_CPU.sh new file mode 100644 index 00000000000..63ab894db81 --- /dev/null +++ b/build_SU2_CPU.sh @@ -0,0 +1,7 @@ +#!bin/bash + +rm -r build + +export PKG_CONFIG_PATH="/home/areen/Programs/OpenBLAS_install/lib:$PKG_CONFIG_PATH" +./meson.py build --buildtype=debug -Denable-pywrapper=true -Dwith-mpi=disabled -Denable-openblas=true --prefix=/home/areen/Programs/SU2_Install_CPU +./ninja -C build install \ No newline at end of file From 3f351db4ef88b278522f43793ba6658e771a49e4 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Sun, 25 Aug 2024 01:25:43 +0530 Subject: [PATCH 27/32] Making Repo PR Ready --- .gitignore | 8 +---- Common/include/containers/C2DContainer.hpp | 2 +- .../linear_algebra/CMatrixVectorProduct.hpp | 3 +- Common/include/linear_algebra/CSysMatrix.hpp | 1 - .../{linear_algerba => template_nvblas.conf} | 0 Common/src/linear_algebra/CSysMatrix.cpp | 4 +-- Common/src/linear_algebra/CSysVector.cpp | 2 -- SU2_CFD/src/solvers/CFEM_DG_EulerSolver.cpp | 3 +- SU2_CFD/src/solvers/CFEM_DG_NSSolver.cpp | 29 +++++++++---------- meson.build | 2 +- nvblas.conf | 7 ----- 11 files changed, 21 insertions(+), 40 deletions(-) rename Common/include/{linear_algerba => template_nvblas.conf} (100%) delete mode 100644 nvblas.conf diff --git a/.gitignore b/.gitignore index 506b4589f59..9c7f890ac8a 100644 --- a/.gitignore +++ b/.gitignore @@ -102,10 +102,4 @@ ninja su2preconfig.timestamp # Clangd server files -.cache - -#Testcases Folders -FEM_AREEN_MESH/ -FVM/ -naca0012/ -Docs/html \ No newline at end of file +.cache \ No newline at end of file diff --git a/Common/include/containers/C2DContainer.hpp b/Common/include/containers/C2DContainer.hpp index a517a2cd442..633c6b7defe 100644 --- a/Common/include/containers/C2DContainer.hpp +++ b/Common/include/containers/C2DContainer.hpp @@ -417,7 +417,7 @@ class C2DContainer * read-only, generic non-contiguous access. */ template - class CInnerIterGather { + class CInnerIterGather { private: static_assert(std::is_integral::value, ""); enum { Size = IndexSIMD_t::Size }; diff --git a/Common/include/linear_algebra/CMatrixVectorProduct.hpp b/Common/include/linear_algebra/CMatrixVectorProduct.hpp index 6db12fcf7aa..b0fcd0a3a3d 100644 --- a/Common/include/linear_algebra/CMatrixVectorProduct.hpp +++ b/Common/include/linear_algebra/CMatrixVectorProduct.hpp @@ -156,5 +156,4 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { exec->mat_vec_prod(u, v, geometry, config, matrix); } -}; - +}; \ No newline at end of file diff --git a/Common/include/linear_algebra/CSysMatrix.hpp b/Common/include/linear_algebra/CSysMatrix.hpp index e72b17af8b2..588cde2399d 100644 --- a/Common/include/linear_algebra/CSysMatrix.hpp +++ b/Common/include/linear_algebra/CSysMatrix.hpp @@ -361,7 +361,6 @@ class CSysMatrix { void RowProduct(const CSysVector& vec, unsigned long row_i, ScalarType* prod) const; public: - /*! * \brief Constructor of the class. */ diff --git a/Common/include/linear_algerba b/Common/include/template_nvblas.conf similarity index 100% rename from Common/include/linear_algerba rename to Common/include/template_nvblas.conf diff --git a/Common/src/linear_algebra/CSysMatrix.cpp b/Common/src/linear_algebra/CSysMatrix.cpp index 69aedfa0b22..401460c2fb1 100644 --- a/Common/src/linear_algebra/CSysMatrix.cpp +++ b/Common/src/linear_algebra/CSysMatrix.cpp @@ -27,7 +27,7 @@ #include "../../include/linear_algebra/CSysMatrix.inl" -#include "../../include/linear_algebra/CSysMatrix.hpp" + #include "../../include/geometry/CGeometry.hpp" #include "../../include/toolboxes/allocation_toolbox.hpp" @@ -148,7 +148,7 @@ void CSysMatrix::Initialize(unsigned long npoint, unsigned long npoi gpuErrChk(cudaMemcpy((void*)(d_row_ptr), (void*)row_ptr, (sizeof(row_ptr)*(nPointDomain+1.0)), cudaMemcpyHostToDevice)); gpuErrChk(cudaMemcpy((void*)(d_col_ind), (void*)col_ind, (sizeof(col_ind))*nnz, cudaMemcpyHostToDevice)); - + #endif if (needTranspPtr) col_ptr = geometry->GetTransposeSparsePatternMap(type).data(); diff --git a/Common/src/linear_algebra/CSysVector.cpp b/Common/src/linear_algebra/CSysVector.cpp index 754e7c39b52..d33a5863b01 100644 --- a/Common/src/linear_algebra/CSysVector.cpp +++ b/Common/src/linear_algebra/CSysVector.cpp @@ -52,8 +52,6 @@ void CSysVector::Initialize(unsigned long numBlk, unsigned long numB if (vec_val == nullptr) vec_val = MemoryAllocation::aligned_alloc(64, nElm * sizeof(ScalarType)); - - if (val != nullptr) { if (!valIsArray) { for (auto i = 0ul; i < nElm; i++) vec_val[i] = *val; diff --git a/SU2_CFD/src/solvers/CFEM_DG_EulerSolver.cpp b/SU2_CFD/src/solvers/CFEM_DG_EulerSolver.cpp index 96d2558023a..f01ea90e7b1 100644 --- a/SU2_CFD/src/solvers/CFEM_DG_EulerSolver.cpp +++ b/SU2_CFD/src/solvers/CFEM_DG_EulerSolver.cpp @@ -35,7 +35,7 @@ #include "../../include/fluid/CDataDrivenFluid.hpp" enum { -SIZE_ARR_NORM = 16385 +SIZE_ARR_NORM = 8 }; CFEM_DG_EulerSolver::CFEM_DG_EulerSolver() : CSolver() { @@ -6447,7 +6447,6 @@ void CFEM_DG_EulerSolver::InviscidFluxesInternalMatchingFace( const su2double* arrNorm[SIZE_ARR_NORM]; const su2double* arrGridVel[SIZE_ARR_NORM]; - if(llEnd > SIZE_ARR_NORM) SU2_MPI::Error("SIZE_ARR_NORM is too small. Increase it or decrease ALIGNED_BYTES_MATMUL", CURRENT_FUNCTION); diff --git a/SU2_CFD/src/solvers/CFEM_DG_NSSolver.cpp b/SU2_CFD/src/solvers/CFEM_DG_NSSolver.cpp index 3dfad133ec4..694d96401a3 100644 --- a/SU2_CFD/src/solvers/CFEM_DG_NSSolver.cpp +++ b/SU2_CFD/src/solvers/CFEM_DG_NSSolver.cpp @@ -30,7 +30,7 @@ #include "../../../Common/include/toolboxes/printing_toolbox.hpp" enum { -SIZE_ARR_NORM = 16385 +SIZE_ARR_NORM = 8 }; CFEM_DG_NSSolver::CFEM_DG_NSSolver() : CFEM_DG_EulerSolver() { @@ -476,7 +476,7 @@ void CFEM_DG_NSSolver::Friction_Forces(const CGeometry* geometry, const CConfig* /* Determine the offset between r- and -s-derivatives, which is also the offset between s- and t-derivatives. */ - const unsigned int offDeriv = NPad*nInt; + const unsigned short offDeriv = NPad*nInt; /* Make a distinction between two and three space dimensions in order to have the most efficient code. */ @@ -1345,8 +1345,8 @@ void CFEM_DG_NSSolver::ADER_DG_AliasedPredictorResidual_2D(CConfig /* Determine the offset between the r-derivatives and s-derivatives of the fluxes in the integration points and the offset between the r-derivatives and s-derivatives of the solution in the DOFs. */ - const unsigned int offDerivSol = NPad*nDOFs; - const unsigned int offDerivFluxes = NPad*nInt; + const unsigned short offDerivSol = NPad*nDOFs; + const unsigned short offDerivFluxes = NPad*nInt; /* Store the number of metric points per integration point for readability. */ const unsigned short nMetricPerPoint = 5; /* nDim*nDim + 1. */ @@ -2062,7 +2062,7 @@ void CFEM_DG_NSSolver::ADER_DG_NonAliasedPredictorResidual_2D(CConfig /* Determine the offset between the solution variables and the r-derivatives, which is also the offset between the r- and s-derivatives in the integration points. */ - const unsigned int offDerivInt = NPad*nInt; + const unsigned short offDerivInt = NPad*nInt; /* Set the pointer for the second derivatives such that they are stored after the first derivatives. */ @@ -2455,7 +2455,7 @@ void CFEM_DG_NSSolver::ADER_DG_NonAliasedPredictorResidual_3D(CConfig /* Determine the offset between the solution variables and the r-derivatives, which is also the offset between the r- and s-derivatives in the integration points. */ - const unsigned int offDerivInt = NPad*nInt; + const unsigned short offDerivInt = NPad*nInt; /* Set the pointer for the second derivatives such that they are stored after the first derivatives. */ @@ -3165,7 +3165,6 @@ void CFEM_DG_NSSolver::Volume_Residual(CConfig *config, /* Determine the number of elements that are treated simultaneously in the matrix products to obtain good gemm performance. */ - const unsigned short nPadInput = config->GetSizeMatMulPadding(); const unsigned short nElemSimul = nPadInput/nVar; @@ -3244,7 +3243,7 @@ void CFEM_DG_NSSolver::Volume_Residual(CConfig *config, /* Determine the offset between the solution variables and the r-derivatives, which is also the offset between the r- and s-derivatives and the offset between s- and t-derivatives. */ - const unsigned int offDeriv = NPad*nInt; + const unsigned short offDeriv = NPad*nInt; /* Make a distinction between two and three space dimensions in order to have the most efficient code. */ @@ -3942,7 +3941,7 @@ void CFEM_DG_NSSolver::ResidualFaces(CConfig *config, } /*--- Subtract half of the viscous fluxes from the inviscid fluxes. ---*/ - for(unsigned int j=0; j<(NPad*nInt); ++j) fluxes[j] -= 0.5*viscFluxes[j]; + for(unsigned short j=0; j<(NPad*nInt); ++j) fluxes[j] -= 0.5*viscFluxes[j]; /*------------------------------------------------------------------------*/ /*--- Step 3: Compute the penalty terms in the integration points of ---*/ @@ -3974,7 +3973,7 @@ void CFEM_DG_NSSolver::ResidualFaces(CConfig *config, } /* Add the penalty fluxes to the earlier computed fluxes. */ - for(unsigned int j=0; j<(NPad*nInt); ++j) fluxes[j] += viscFluxes[j]; + for(unsigned short j=0; j<(NPad*nInt); ++j) fluxes[j] += viscFluxes[j]; /* Multiply the fluxes with the integration weight of the corresponding integration point. */ @@ -4164,7 +4163,7 @@ void CFEM_DG_NSSolver::ViscousNormalFluxFace(const CVolumeElementFEM *adjVolElem /* Determine the offset between r- and -s-derivatives, which is also the offset between s- and t-derivatives. */ - const unsigned int offDeriv = NPad*nInt; + const unsigned short offDeriv = NPad*nInt; /* Make a distinction between two and three space dimensions in order to have the most efficient code. */ @@ -5222,7 +5221,7 @@ void CFEM_DG_NSSolver::BC_Sym_Plane(CConfig *config, /* Determine the offset between r- and -s-derivatives, which is also the offset between s- and t-derivatives. */ - const unsigned int offDeriv = NPad*nInt; + const unsigned short offDeriv = NPad*nInt; /* Loop over the faces of the chunk. */ for(unsigned long ll=l; ll Date: Sun, 25 Aug 2024 01:29:47 +0530 Subject: [PATCH 28/32] Making Repo PR Ready --- Common/src/linear_algebra/CSysMatrix.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/Common/src/linear_algebra/CSysMatrix.cpp b/Common/src/linear_algebra/CSysMatrix.cpp index 401460c2fb1..23127777da6 100644 --- a/Common/src/linear_algebra/CSysMatrix.cpp +++ b/Common/src/linear_algebra/CSysMatrix.cpp @@ -499,7 +499,7 @@ void CSysMatrix::Gauss_Elimination(ScalarType* matrix, ScalarType* v LAPACKE_dgetrf(LAPACK_ROW_MAJOR, nVar, nVar, matrix, nVar, ipiv); LAPACKE_dgetrs(LAPACK_ROW_MAJOR, 'N', nVar, 1, matrix, nVar, ipiv, vec, 1); #else -#define A(I, J) matrix[(I) * nVar + (J)] +#define A(I, J) matrix[(I)*nVar + (J)] /*--- Transform system in Upper Matrix ---*/ for (auto iVar = 1ul; iVar < nVar; iVar++) { @@ -529,7 +529,7 @@ void CSysMatrix::MatrixInverse(ScalarType* matrix, ScalarType* inver assert((matrix != inverse) && "Output cannot be the same as the input."); -#define M(I, J) inverse[(I) * nVar + (J)] +#define M(I, J) inverse[(I)*nVar + (J)] /*--- Initialize the inverse with the identity. ---*/ for (auto iVar = 0ul; iVar < nVar; iVar++) @@ -542,7 +542,7 @@ void CSysMatrix::MatrixInverse(ScalarType* matrix, ScalarType* inver LAPACKE_dgetrf(LAPACK_ROW_MAJOR, nVar, nVar, matrix, nVar, ipiv); LAPACKE_dgetrs(LAPACK_ROW_MAJOR, 'N', nVar, nVar, matrix, nVar, ipiv, inverse, nVar); #else -#define A(I, J) matrix[(I) * nVar + (J)] +#define A(I, J) matrix[(I)*nVar + (J)] /*--- Transform system in Upper Matrix ---*/ for (auto iVar = 1ul; iVar < nVar; iVar++) { From a0e09d76112012b1a35c9a40e7abfce2f5a13504 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Tue, 27 Aug 2024 14:35:40 +0530 Subject: [PATCH 29/32] Making it PR Ready --- build_SU2.sh | 9 --------- build_SU2_CPU.sh | 7 ------- 2 files changed, 16 deletions(-) delete mode 100644 build_SU2.sh delete mode 100644 build_SU2_CPU.sh diff --git a/build_SU2.sh b/build_SU2.sh deleted file mode 100644 index 5dbc33a53d5..00000000000 --- a/build_SU2.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!bin/bash - -rm -r build - -export PKG_CONFIG_PATH="/home/areen/Programs/OpenBLAS_install/lib:$PKG_CONFIG_PATH" -export PKG_CONFIG_PATH="/usr/local/cuda/lib64:$PKG_CONFIG_PATH" -export CUDA_PATH="/usr/local/cuda:$CUDA_PATH" -./meson.py build --buildtype=debug -Denable-pywrapper=true -Dwith-mpi=disabled -Denable-openblas=true -Denable-cuda=true --prefix=/home/areen/Programs/SU2_Install_GPU -./ninja -C build install \ No newline at end of file diff --git a/build_SU2_CPU.sh b/build_SU2_CPU.sh deleted file mode 100644 index 63ab894db81..00000000000 --- a/build_SU2_CPU.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!bin/bash - -rm -r build - -export PKG_CONFIG_PATH="/home/areen/Programs/OpenBLAS_install/lib:$PKG_CONFIG_PATH" -./meson.py build --buildtype=debug -Denable-pywrapper=true -Dwith-mpi=disabled -Denable-openblas=true --prefix=/home/areen/Programs/SU2_Install_CPU -./ninja -C build install \ No newline at end of file From b489d0934ce54893cd0c6949168effa5a3ab351c Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Tue, 27 Aug 2024 14:46:42 +0530 Subject: [PATCH 30/32] Pre-Commit Hook Ran --- .gitignore | 2 +- .../linear_algebra/CMatrixVectorProduct.hpp | 67 +++++++++---------- Common/include/linear_algebra/CSysMatrix.hpp | 13 ++-- Common/include/linear_algebra/GPU_lin_alg.cuh | 4 +- Common/include/template_nvblas.conf | 6 +- Common/src/linear_algebra/CSysMatrix.cpp | 17 ++--- Common/src/linear_algebra/GPU_lin_alg.cu | 12 ++-- 7 files changed, 59 insertions(+), 62 deletions(-) diff --git a/.gitignore b/.gitignore index 9c7f890ac8a..2177d44e12e 100644 --- a/.gitignore +++ b/.gitignore @@ -102,4 +102,4 @@ ninja su2preconfig.timestamp # Clangd server files -.cache \ No newline at end of file +.cache diff --git a/Common/include/linear_algebra/CMatrixVectorProduct.hpp b/Common/include/linear_algebra/CMatrixVectorProduct.hpp index b0fcd0a3a3d..eb1729f1517 100644 --- a/Common/include/linear_algebra/CMatrixVectorProduct.hpp +++ b/Common/include/linear_algebra/CMatrixVectorProduct.hpp @@ -50,11 +50,11 @@ * handle the different types of matrix-vector products and still be * passed to a single implementation of the Krylov solvers. * This abstraction may also be used to define matrix-free products. - * - * There is also the use of a dummy class being made to select the + * + * There is also the use of a dummy class being made to select the * correct function as defined by the user while deciding between * CPU or GPU execution. This dummy class calls the correct member - * functions from its derived classes to map the suitable path of + * functions from its derived classes to map the suitable path of * execution - CPU or GPU. */ template @@ -72,9 +72,10 @@ CMatrixVectorProduct::~CMatrixVectorProduct() {} */ template -class executionPath{ -public: - virtual void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, const CConfig* config, const CSysMatrix& matrix) = 0; +class executionPath { + public: + virtual void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, + const CConfig* config, const CSysMatrix& matrix) = 0; }; /*! @@ -83,28 +84,28 @@ class executionPath{ */ template class cpuExecution : public executionPath { -public: - void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, const CConfig* config, const CSysMatrix& matrix) override - { - matrix.MatrixVectorProduct(u, v, geometry, config); - } + public: + void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, + const CConfig* config, const CSysMatrix& matrix) override { + matrix.MatrixVectorProduct(u, v, geometry, config); + } }; /*! * \class gpuExecution * \brief Derived class containing the GPU Matrix Vector Product Function */ -template +template class gpuExecution : public executionPath { -public: - void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, const CConfig* config, const CSysMatrix& matrix) override - { - #ifdef HAVE_CUDA - matrix.GPUMatrixVectorProduct(u, v, geometry, config); - #else - matrix.MatrixVectorProduct(u, v, geometry, config); - #endif - } + public: + void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, + const CConfig* config, const CSysMatrix& matrix) override { +#ifdef HAVE_CUDA + matrix.GPUMatrixVectorProduct(u, v, geometry, config); +#else + matrix.MatrixVectorProduct(u, v, geometry, config); +#endif + } }; /*! @@ -129,17 +130,13 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { */ inline CSysMatrixVectorProduct(const CSysMatrix& matrix_ref, CGeometry* geometry_ref, const CConfig* config_ref) - : matrix(matrix_ref), geometry(geometry_ref), config(config_ref) - { - if(config->GetCUDA()) - { - exec = new gpuExecution; - } - else - { - exec = new cpuExecution; - } - } + : matrix(matrix_ref), geometry(geometry_ref), config(config_ref) { + if (config->GetCUDA()) { + exec = new gpuExecution; + } else { + exec = new cpuExecution; + } + } /*! * \note This class cannot be default constructed as that would leave us with invalid pointers. @@ -152,8 +149,6 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { * \param[out] v - CSysVector that is the result of the product */ inline void operator()(const CSysVector& u, CSysVector& v) const override { - - exec->mat_vec_prod(u, v, geometry, config, matrix); - + exec->mat_vec_prod(u, v, geometry, config, matrix); } -}; \ No newline at end of file +}; diff --git a/Common/include/linear_algebra/CSysMatrix.hpp b/Common/include/linear_algebra/CSysMatrix.hpp index 588cde2399d..87012c85e07 100644 --- a/Common/include/linear_algebra/CSysMatrix.hpp +++ b/Common/include/linear_algebra/CSysMatrix.hpp @@ -145,7 +145,7 @@ class CSysMatrix { const unsigned long* col_ind; /*!< \brief Column index for each of the elements in val(). */ const unsigned long* col_ptr; /*!< \brief The transpose of col_ind, pointer to blocks with the same column index. */ - ScalarType* d_matrix; /*!< \brief Entries ot be stored on the device. */ + ScalarType* d_matrix; /*!< \brief Entries ot be stored on the device. */ unsigned long* d_row_ptr; /*!< \brief Device Pointers to the first element in each row. */ unsigned long* d_col_ind; /*!< \brief Device Column index for each of the elements in val(). */ @@ -842,7 +842,7 @@ class CSysMatrix { */ void MatrixVectorProduct(const CSysVector& vec, CSysVector& prod, CGeometry* geometry, const CConfig* config) const; - + /*! * \brief Performs the product of a sparse matrix by a CSysVector. * \param[in] vec - CSysVector to be multiplied by the sparse matrix A. @@ -851,11 +851,12 @@ class CSysMatrix { * \param[out] prod - Result of the product. */ - void GPUMatrixVectorProduct(const CSysVector& vec, CSysVector& prod, CGeometry* geometry, + void GPUMatrixVectorProduct(const CSysVector& vec, CSysVector& prod, CGeometry* geometry, const CConfig* config) const; - - void FGMRESMainLoop(std::vector W, std::vector Z, su2vector& g, su2vector& sn,CSysVector& cs,su2vector& y,su2vector& H, - int m, CGeometry* geometry, const CConfig* config) const; + + void FGMRESMainLoop(std::vector W, std::vector Z, su2vector& g, + su2vector& sn, CSysVector& cs, su2vector& y, + su2vector& H, int m, CGeometry* geometry, const CConfig* config) const; /*! * \brief Build the Jacobi preconditioner. diff --git a/Common/include/linear_algebra/GPU_lin_alg.cuh b/Common/include/linear_algebra/GPU_lin_alg.cuh index 6e8601dcb02..a35ad389302 100644 --- a/Common/include/linear_algebra/GPU_lin_alg.cuh +++ b/Common/include/linear_algebra/GPU_lin_alg.cuh @@ -39,7 +39,7 @@ */ inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true) { - if (code != cudaSuccess) + if (code != cudaSuccess) { fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line); if (abort) exit(code); @@ -59,4 +59,4 @@ inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=t */ template -__global__ void GPUMatrixVectorProductAdd(matrixType* matrix, vectorType* vec, vectorType* prod, unsigned long* d_row_ptr, unsigned long* d_col_ind, unsigned long nPointDomain, unsigned long nVar, unsigned long nEqn); \ No newline at end of file +__global__ void GPUMatrixVectorProductAdd(matrixType* matrix, vectorType* vec, vectorType* prod, unsigned long* d_row_ptr, unsigned long* d_col_ind, unsigned long nPointDomain, unsigned long nVar, unsigned long nEqn); diff --git a/Common/include/template_nvblas.conf b/Common/include/template_nvblas.conf index 7c96114aec1..a86d1625b06 100644 --- a/Common/include/template_nvblas.conf +++ b/Common/include/template_nvblas.conf @@ -1,7 +1,7 @@ -NVBLAS_CPU_BLAS_LIB /path/to/libopenblas.so +NVBLAS_CPU_BLAS_LIB /path/to/libopenblas.so -NVBLAS_GPU_LIST ALL +NVBLAS_GPU_LIST ALL -NVBLAS_TILE_DIM 2048 +NVBLAS_TILE_DIM 2048 NVBLAS_AUTOPIN_MEM_ENABLED diff --git a/Common/src/linear_algebra/CSysMatrix.cpp b/Common/src/linear_algebra/CSysMatrix.cpp index 23127777da6..08c6377c076 100644 --- a/Common/src/linear_algebra/CSysMatrix.cpp +++ b/Common/src/linear_algebra/CSysMatrix.cpp @@ -25,7 +25,6 @@ * License along with SU2. If not, see . */ - #include "../../include/linear_algebra/CSysMatrix.inl" #include "../../include/geometry/CGeometry.hpp" @@ -36,8 +35,9 @@ #ifdef HAVE_CUDA #include "../../include/linear_algebra/GPU_lin_alg.cuh" -#ifndef gpuErrChk -#define gpuErrChk(ans) { gpuAssert((ans), __FILE__, __LINE__); } +#ifndef gpuErrChk +#define gpuErrChk(ans) \ + { gpuAssert((ans), __FILE__, __LINE__); } #endif #endif @@ -142,12 +142,13 @@ void CSysMatrix::Initialize(unsigned long npoint, unsigned long npoi dia_ptr = csr.diagPtr(); #if defined(HAVE_CUDA) - gpuErrChk(cudaMalloc((void**)(&d_row_ptr), (sizeof(row_ptr)*(nPointDomain+1.0)))); - gpuErrChk(cudaMalloc((void**)(&d_col_ind), (sizeof(col_ind)*nnz))); - gpuErrChk(cudaMalloc((void**)(&d_matrix), (sizeof(ScalarType)*nnz*nVar*nEqn))); + gpuErrChk(cudaMalloc((void**)(&d_row_ptr), (sizeof(row_ptr) * (nPointDomain + 1.0)))); + gpuErrChk(cudaMalloc((void**)(&d_col_ind), (sizeof(col_ind) * nnz))); + gpuErrChk(cudaMalloc((void**)(&d_matrix), (sizeof(ScalarType) * nnz * nVar * nEqn))); - gpuErrChk(cudaMemcpy((void*)(d_row_ptr), (void*)row_ptr, (sizeof(row_ptr)*(nPointDomain+1.0)), cudaMemcpyHostToDevice)); - gpuErrChk(cudaMemcpy((void*)(d_col_ind), (void*)col_ind, (sizeof(col_ind))*nnz, cudaMemcpyHostToDevice)); + gpuErrChk( + cudaMemcpy((void*)(d_row_ptr), (void*)row_ptr, (sizeof(row_ptr) * (nPointDomain + 1.0)), cudaMemcpyHostToDevice)); + gpuErrChk(cudaMemcpy((void*)(d_col_ind), (void*)col_ind, (sizeof(col_ind)) * nnz, cudaMemcpyHostToDevice)); #endif diff --git a/Common/src/linear_algebra/GPU_lin_alg.cu b/Common/src/linear_algebra/GPU_lin_alg.cu index 7b852780db9..75dd452fa11 100644 --- a/Common/src/linear_algebra/GPU_lin_alg.cu +++ b/Common/src/linear_algebra/GPU_lin_alg.cu @@ -34,7 +34,7 @@ template __global__ void GPUMatrixVectorProductAdd(matrixType* matrix, vectorType* vec, vectorType* prod, unsigned long* d_row_ptr, unsigned long* d_col_ind, unsigned long nPointDomain, unsigned long nVar, unsigned long nEqn) -{ +{ int i = blockIdx.x * blockDim.x + threadIdx.x; int j = threadIdx.y; @@ -42,7 +42,7 @@ __global__ void GPUMatrixVectorProductAdd(matrixType* matrix, vectorType* vec, v int prod_index = i * nVar; - if(i::GPUMatrixVectorProduct(const CSysVector gpuErrChk(cudaMalloc((void**)(&d_vec), (sizeof(ScalarType)*vec_size))); gpuErrChk(cudaMalloc((void**)(&d_prod), (sizeof(ScalarType)*vec_size))); - + gpuErrChk(cudaMemcpy((void*)(d_matrix), (void*)&matrix[0], (sizeof(ScalarType)*mat_size), cudaMemcpyHostToDevice)); gpuErrChk(cudaMemcpy((void*)(d_vec), (void*)&vec[0], (sizeof(ScalarType)*vec_size), cudaMemcpyHostToDevice)); gpuErrChk(cudaMemcpy((void*)(d_prod), (void*)&prod[0], (sizeof(ScalarType)*vec_size), cudaMemcpyHostToDevice)); @@ -94,10 +94,10 @@ void CSysMatrix::GPUMatrixVectorProduct(const CSysVector gpuErrChk( cudaPeekAtLastError() ); gpuErrChk(cudaMemcpy((void*)(&prod[0]), (void*)d_prod, (sizeof(ScalarType)*vec_size), cudaMemcpyDeviceToHost)); - + gpuErrChk(cudaFree(d_vec)); gpuErrChk(cudaFree(d_prod)); } -template class CSysMatrix; \ No newline at end of file +template class CSysMatrix; From 4926d348397f62cd0fc67812d5a5e760404d0b6b Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Tue, 27 Aug 2024 15:11:55 +0530 Subject: [PATCH 31/32] PR Ready --- .../linear_algebra/CMatrixVectorProduct.hpp | 18 ++-- TestCases/gpu/flatplate/lam_flatplate.cfg | 88 +++++++++++++++++++ config_template.cfg | 3 + 3 files changed, 100 insertions(+), 9 deletions(-) create mode 100644 TestCases/gpu/flatplate/lam_flatplate.cfg diff --git a/Common/include/linear_algebra/CMatrixVectorProduct.hpp b/Common/include/linear_algebra/CMatrixVectorProduct.hpp index eb1729f1517..c177bfee5ea 100644 --- a/Common/include/linear_algebra/CMatrixVectorProduct.hpp +++ b/Common/include/linear_algebra/CMatrixVectorProduct.hpp @@ -67,23 +67,23 @@ template CMatrixVectorProduct::~CMatrixVectorProduct() {} /*! - * \class executionPath + * \class CExecutionPath * \brief Dummy super class that holds the correct member functions in its child classes */ template -class executionPath { +class CExecutionPath { public: virtual void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, const CConfig* config, const CSysMatrix& matrix) = 0; }; /*! - * \class cpuExecution + * \class CCpuExecution * \brief Derived class containing the CPU Matrix Vector Product Function */ template -class cpuExecution : public executionPath { +class CCpuExecution : public executionPath { public: void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, const CConfig* config, const CSysMatrix& matrix) override { @@ -92,11 +92,11 @@ class cpuExecution : public executionPath { }; /*! - * \class gpuExecution + * \class CGpuExecution * \brief Derived class containing the GPU Matrix Vector Product Function */ template -class gpuExecution : public executionPath { +class CGpuExecution : public executionPath { public: void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, const CConfig* config, const CSysMatrix& matrix) override { @@ -119,7 +119,7 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { const CSysMatrix& matrix; /*!< \brief pointer to matrix that defines the product. */ CGeometry* geometry; /*!< \brief geometry associated with the matrix. */ const CConfig* config; /*!< \brief config of the problem. */ - executionPath* exec; /*!< \brief interface that decides which path of execution to choose from. */ + CExecutionPath* exec; /*!< \brief interface that decides which path of execution to choose from. */ public: /*! @@ -132,9 +132,9 @@ class CSysMatrixVectorProduct final : public CMatrixVectorProduct { const CConfig* config_ref) : matrix(matrix_ref), geometry(geometry_ref), config(config_ref) { if (config->GetCUDA()) { - exec = new gpuExecution; + exec = new CGpuExecution; } else { - exec = new cpuExecution; + exec = new CCpuExecution; } } diff --git a/TestCases/gpu/flatplate/lam_flatplate.cfg b/TestCases/gpu/flatplate/lam_flatplate.cfg new file mode 100644 index 00000000000..0ceec37620b --- /dev/null +++ b/TestCases/gpu/flatplate/lam_flatplate.cfg @@ -0,0 +1,88 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% % +% SU2 configuration file % +% Case description: Laminar flow over a flat plate with zero pressure gradient % +% Author: Thomas D. Economon % +% Institution: Stanford University % +% Date: 2013.09.30 % +% File Version 8.0.1 "Harrier" % +% % +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +% ------------- DIRECT, ADJOINT, AND LINEARIZED PROBLEM DEFINITION ------------% +% +SOLVER= NAVIER_STOKES +KIND_TURB_MODEL= NONE +MATH_PROBLEM= DIRECT +RESTART_SOL= NO + +% -------------------- COMPRESSIBLE FREE-STREAM DEFINITION --------------------% +% +MACH_NUMBER= 0.2 +AOA= 0.0 +SIDESLIP_ANGLE= 0.0 +FREESTREAM_TEMPERATURE= 297.62 +REYNOLDS_NUMBER= 1301233.166 +REYNOLDS_LENGTH= 0.3048 + +% ---------------------- REFERENCE VALUE DEFINITION ---------------------------% +% +REF_ORIGIN_MOMENT_X = 0.25 +REF_ORIGIN_MOMENT_Y = 0.00 +REF_ORIGIN_MOMENT_Z = 0.00 +REF_LENGTH= 1.0 +REF_AREA= 0.3048 + +% -------------------- BOUNDARY CONDITION DEFINITION --------------------------% +% +MARKER_HEATFLUX= ( wall, 0.0 ) +MARKER_SYM= ( symmetry ) +MARKER_INLET= ( inlet, 300.0, 100000.0, 1.0, 0.0, 0.0 ) +MARKER_OUTLET= ( outlet, 97250.0) +MARKER_PLOTTING= ( wall ) +MARKER_MONITORING= ( wall ) +MARKER_ANALYZE= (inlet, outlet) + +% ------------- COMMON PARAMETERS DEFINING THE NUMERICAL METHOD ---------------% +% +NUM_METHOD_GRAD= WEIGHTED_LEAST_SQUARES +CFL_NUMBER= 10000 +CFL_ADAPT= NO + +% ------------------------ LINEAR SOLVER DEFINITION ---------------------------% +% +LINEAR_SOLVER= FGMRES +LINEAR_SOLVER_PREC= LU_SGS +LINEAR_SOLVER_ERROR= 0.05 +LINEAR_SOLVER_ITER= 3 + +% -------------------------- MULTIGRID PARAMETERS -----------------------------% +% + +% -------------------- FLOW NUMERICAL METHOD DEFINITION -----------------------% +% +CONV_NUM_METHOD_FLOW= ROE +MUSCL_FLOW= YES +SLOPE_LIMITER_FLOW= NONE +TIME_DISCRE_FLOW= EULER_IMPLICIT + +% --------------------------- CONVERGENCE PARAMETERS --------------------------% +% +CONV_FIELD= RMS_DENSITY +CONV_RESIDUAL_MINVAL= -12 +CONV_STARTITER= 10 +ITER= 500 + +% ------------------------- INPUT/OUTPUT INFORMATION --------------------------% +% +MESH_FILENAME= mesh_flatplate_fine.su2 +ENABLE_CUDA= NO +MESH_FORMAT= SU2 +SOLUTION_FILENAME= solution_flow.dat +SOLUTION_ADJ_FILENAME= solution_adj.dat +TABULAR_FORMAT= CSV +RESTART_FILENAME= restart_flow.dat +RESTART_ADJ_FILENAME= restart_adj.dat +GRAD_OBJFUNC_FILENAME= of_grad.dat +OUTPUT_WRT_FREQ= 250 +SCREEN_OUTPUT= (INNER_ITER, RMS_DENSITY, RMS_ENERGY, DRAG, SURFACE_MASSFLOW, TOTAL_HEATFLUX, TOTAL_HEATFLUX_ON_SURFACE) diff --git a/config_template.cfg b/config_template.cfg index bf3e6bae907..9466b1e91e9 100644 --- a/config_template.cfg +++ b/config_template.cfg @@ -1463,6 +1463,9 @@ DISCADJ_LIN_SOLVER= FGMRES % Linear solver for the turbulent adjoint systems ADJTURB_LIN_SOLVER= FGMRES % +% Use CUDA GPU Acceleration for FGMRES Linear Solver Only +ENABLE_CUDA=NO +% % Preconditioner for the turbulent adjoint Krylov linear solvers ADJTURB_LIN_PREC= ILU % From c691960fcd8f2879e40c494c81eb2c20d9972f45 Mon Sep 17 00:00:00 2001 From: Areen Raj Date: Fri, 30 Aug 2024 11:26:41 +0530 Subject: [PATCH 32/32] added some fixes and error handling --- Common/include/linear_algebra/CMatrixVectorProduct.hpp | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/Common/include/linear_algebra/CMatrixVectorProduct.hpp b/Common/include/linear_algebra/CMatrixVectorProduct.hpp index c177bfee5ea..6cba53f5dbe 100644 --- a/Common/include/linear_algebra/CMatrixVectorProduct.hpp +++ b/Common/include/linear_algebra/CMatrixVectorProduct.hpp @@ -83,7 +83,7 @@ class CExecutionPath { * \brief Derived class containing the CPU Matrix Vector Product Function */ template -class CCpuExecution : public executionPath { +class CCpuExecution : public CExecutionPath { public: void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, const CConfig* config, const CSysMatrix& matrix) override { @@ -96,14 +96,16 @@ class CCpuExecution : public executionPath { * \brief Derived class containing the GPU Matrix Vector Product Function */ template -class CGpuExecution : public executionPath { +class CGpuExecution : public CExecutionPath { public: void mat_vec_prod(const CSysVector& u, CSysVector& v, CGeometry* geometry, const CConfig* config, const CSysMatrix& matrix) override { #ifdef HAVE_CUDA matrix.GPUMatrixVectorProduct(u, v, geometry, config); #else - matrix.MatrixVectorProduct(u, v, geometry, config); + std::cerr << "\nError in launching Matrix-Vector Product Function\n"; + std::cerr << "ENABLE_CUDA is set to YES\n"; + std::cerr << "Please compile with CUDA options enabled in Meson to access GPU Functions" << std::endl; #endif } };

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b/README.md index 1f7851d2b84..2a185d537a9 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,10 @@ -# Experimental GPU Accelerated Version of SU2 Code +# Google Summer of Code '24 Project - Feasibility of GPU Acceleration in SU2 +This code repository contains a version of the SU2 codebase that has CUDA support enabled for offloading particular linear algebra options to the GPU. The current implementations show promise for increased performance with further optimizations and changes. We are also currently working in a new research direction as will be outlined further below. + +## Compiling and Running the Code + +### Compiling Currently two modifications exist in the code - NVBLAS Integration which intercepts calls for the Space Integration in the DG Solver - not very useful - Acceleration of program wide Matrix Vector Product by outsourcing to a CUDA Kernel @@ -19,6 +24,141 @@ bash build_SU2.sh ``` A barebones template NVBLAS config file has also been provided for reference. +A lot of warnings come in + +### Usage + +To start the use of the GPU in any simulation involving the FGMRES solver. Just set the following keyword in the config file to YES + +``` +ENABLE_CUDA=YES +``` + +Error handling is done by an Error Check Wrapper and should provide exit codes at the file and line of the anomaly. + +### Benchmarking + +To benchmark the individual member functions, the following sections of code in the CMatrixVectorProduct.hpp file have to be uncommented. + +``` + + std::ofstream serial; + + auto start = std::chrono::high_resolution_clock::now(); +. +. +. + + auto stop = std::chrono::high_resolution_clock::now(); + auto duration = std::chrono::duration_cast(stop - start); + + double time = duration.count(); + + serial.open("MVP_Exec_Time.txt", std::ios::app); + serial << time << "\n"; + serial.close(); + +``` + +This will create a file named MVP_Exec_Time.txt which contains the execution time in microseconds. Always remove or delete the file before starting another run or else the new times will get appended onto it. + +Also it is recommended to disable GPU Error Check functions before starting as this may cause excessive overhead in execution. These may be removed from the GPU_lin_alg.cu and CSysMatrix.cpp files. + +## Initial Proposal and Deliverables + +This section outlines the original plan of the project and the changes that were made throughout the course of its duration. + +Initially we decided to approach the addition of GPU functionality to SU2 using the NVBLAS framework. Since the FEM solver used level 3 BLAS calls which can be intercepted by the NVBLAS library directly and routed to the GPU if it is deemed that the particular call would execute faster on the GPU. + +Our proposed milestones were the implementation and further optimization of this approach. + +![alt text](Docs/Report/Milestones.png) + +After creating an NVBLAS enabled version of the code, our investigation revealed that the BLAS calls are made only during the Space Integration subroutine that would not give us a sizeable speedup in the code. + +The major chunk of linear algebra operations happens while using an implicit time scheme that involves solving a matrix system. This Time Integration subroutine is also one of the most computationally intensive sections of the code. + +## Pivoting to a more Practical Approach + +This is the reason that the NVBLAS integration did not offer a path of execution that could provide considerable speed up to our execution time. Therefore, we chose to switch our focus to a more impactful implementation by directly working on the linear solvers that handle the Matrix operations in the Time Integration subroutine. + +This would + +- Offer a bigger overall boost to the performance of the code as we would be targetting a critical area +- Automatically extend this endeavor to other solvers, not only fulfilling the fourth milestone, but also being a wide-reaching solution. + +**We took this decision keeping in mind that the Summer of Code program is not geared towards only fulfilling deliverables and keeping in line with timelines. But that it serves the overall goal of writing more useful code that has a bigger effect.** + +![alt text](Docs/Report/Deliverables.png) + +- An NVBLAS integrated version was successfully created by linking the necessary library into the code +- Acceleration of the program was made possible by outsourcing Matrix-Vector Products usinga a CUDA kernel +- This file serves as the execution + extension report that is mentioned above. NSight tools were used to profile the code as initially proposed + +## Current Implementation + +For the working problem, we have focused on two aspects of the solving algorithm + +- The FGMRES solver that heavily relies on Matrix-Vector Products +- Owing to the above point, we modify the matrix vector product class and functions + +A flowchart of the current algorithm that we use is presented below. + +![alt text](Docs/Report/Current_Algorithm.png) + +The GPUMatrixVectorProduct Member Function belongs to the CSysMatrix Class + +- The function makes cudaMalloc and cudaMemcpy API calls to transfer the participating matrix and vector data to the GPU +- The GPUMatrixVectorProductAdd kernel is launched which multiplies the block matrix with the vector using CUDA +- The data of the Product Vector is transferred back to the CPU and the device pointers are freed + +## Code Profiling + +Using NSight Systems, we can get the analysis of the implementation. This profiling was done for the flat plate case in the FVM NSS Solver with the number of elements being 4096. + +![alt text](Docs/Report/Code_Profile.png) + +As expected, the memory transfer between the host and device comprises of the main downtime in this implementation (2.315 ms per call). The Kernel launch itself is only 0.014 ms long. + +## Results + +All benchmarks are done without GPU error checking as it adds a considerable overhead to the entire operation. + +The following results were done with increasing mesh density for the flatplate case. + +![alt text](Docs/Report/Benchmark_Coarse.png) +![alt text](Docs/Report/Benchmark_Fine.png) +![alt text](Docs/Report/Benchmark_Finer.png) + +It is very promising to see that even with a preliminary algorithm the GPU implementation manages to break out ahead of the single core CPU one. + +A per call comparison is given below. The time taken to execute the CPU and GPU member functions were measured. The results shown are impressive. + +On a simple execution time basis, the GPU implementation is over twice as fast as its CPU counterpart. The following log scale graph is representative of that + +![alt text](Docs/Report/Scaling.png) + +The GPU consistently outperforms the CPU calls by a considerable margin. + +## Moving Forward + +If the number of the repeated memory transfers can be reduced then we can gain a considerable boost as cudaMemcpy is invoked per call. The individual performance of the member function clearly shows an exploitable edge when it comes to the linear algebra functions. + +Our current direction happens to be to cut down on the transfers by porting not only each iteration of the FGMRES inner loop but the entire loop itself to the GPU. This allows us for only one single memory transfer per iteration. + +The algorithm would be changed approximately to what is represented below. + +![alt text](Docs/Report/Future_Implementation.png) + +This should alleviate the issue of expensive memory transfers to the device to a sizeable extent, firther providing a performance uplift. **This line of work will be pursued even after the finishing of the GSoC Program** + +## To Do List + +- Make sure that error handling is implemented properly +- Work on removing the unnecessary warnings that are currently appearing during compilation +- Port the inner loop to the GPU +- Open a PR as soons as 1 and 2 are finished + # STANDARD README STARTS HERE