Merge pull request #187 from KernelTuner/refactor_interface

Refactor interfaces

doc/source/design.rst

@@ -98,33 +98,33 @@ kernel_tuner.core.DeviceInterface
     :special-members: __init__
     :members:
 
-kernel_tuner.pycuda.PyCudaFunctions
+kernel_tuner.backends.pycuda.PyCudaFunctions
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: kernel_tuner.pycuda.PyCudaFunctions
+.. autoclass:: kernel_tuner.backends.pycuda.PyCudaFunctions
     :special-members: __init__
     :members:
 
-kernel_tuner.cupy.CupyFunctions
+kernel_tuner.backends.cupy.CupyFunctions
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: kernel_tuner.cupy.CupyFunctions
+.. autoclass:: kernel_tuner.backends.cupy.CupyFunctions
     :special-members: __init__
     :members:
 
-kernel_tuner.nvcuda.CudaFunctions
+kernel_tuner.backends.nvcuda.CudaFunctions
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: kernel_tuner.nvcuda.CudaFunctions
+.. autoclass:: kernel_tuner.backends.nvcuda.CudaFunctions
     :special-members: __init__
     :members:
 
-kernel_tuner.opencl.OpenCLFunctions
+kernel_tuner.backends.opencl.OpenCLFunctions
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: kernel_tuner.opencl.OpenCLFunctions
+.. autoclass:: kernel_tuner.backends.opencl.OpenCLFunctions
     :special-members: __init__
     :members:
 
-kernel_tuner.c.CFunctions
+kernel_tuner.backends.c.CFunctions
 ~~~~~~~~~~~~~~~~~~~~~~~~~
-.. autoclass:: kernel_tuner.c.CFunctions
+.. autoclass:: kernel_tuner.backends.c.CFunctions
     :special-members: __init__
     :members:
 

examples/cuda/convolution_correct.py

@@ -26,66 +26,88 @@
 import kernel_tuner
 from collections import OrderedDict
 
+
 def tune():
-    with open('convolution.cu', 'r') as f:
+    with open("convolution.cu", "r") as f:
         kernel_string = f.read()
 
     filter_size = (17, 17)
     problem_size = (4096, 4096)
     size = numpy.prod(problem_size)
-    border_size = (filter_size[0]//2*2, filter_size[1]//2*2)
-    input_size = ((problem_size[0]+border_size[0]) * (problem_size[1]+border_size[1]))
+    border_size = (filter_size[0] // 2 * 2, filter_size[1] // 2 * 2)
+    input_size = (problem_size[0] + border_size[0]) * (problem_size[1] + border_size[1])
 
     output = numpy.zeros(size).astype(numpy.float32)
     input = numpy.random.randn(input_size).astype(numpy.float32)
 
-    filter = numpy.random.randn(filter_size[0]*filter_size[1]).astype(numpy.float32)
-    cmem_args= {'d_filter': filter }
+    filter = numpy.random.randn(filter_size[0] * filter_size[1]).astype(numpy.float32)
+    cmem_args = {"d_filter": filter}
 
     args = [output, input, filter]
     tune_params = OrderedDict()
     tune_params["filter_width"] = [filter_size[0]]
     tune_params["filter_height"] = [filter_size[1]]
 
-    #tune_params["block_size_x"] = [16*i for i in range(1,3)]
-    tune_params["block_size_x"] = [16*i for i in range(1,9)]
-    #tune_params["block_size_y"] = [2**i for i in range(1,5)]
-    tune_params["block_size_y"] = [2**i for i in range(1,6)]
+    # tune_params["block_size_x"] = [16*i for i in range(1,3)]
+    tune_params["block_size_x"] = [16 * i for i in range(1, 9)]
+    # tune_params["block_size_y"] = [2**i for i in range(1,5)]
+    tune_params["block_size_y"] = [2**i for i in range(1, 6)]
 
     tune_params["tile_size_x"] = [2**i for i in range(3)]
     tune_params["tile_size_y"] = [2**i for i in range(3)]
 
-    tune_params["use_padding"] = [0,1]  #toggle the insertion of padding in shared memory
-    tune_params["read_only"] = [0,1]    #toggle using the read-only cache
+    tune_params["use_padding"] = [
+        0,
+        1,
+    ]  # toggle the insertion of padding in shared memory
+    tune_params["read_only"] = [0, 1]  # toggle using the read-only cache
 
     grid_div_x = ["block_size_x", "tile_size_x"]
     grid_div_y = ["block_size_y", "tile_size_y"]
 
-    #compute the answer using a naive kernel
-    params = { "block_size_x": 16, "block_size_y": 16}
+    # compute the answer using a naive kernel
+    params = {"block_size_x": 16, "block_size_y": 16}
     tune_params["filter_width"] = [filter_size[0]]
     tune_params["filter_height"] = [filter_size[1]]
-    results = kernel_tuner.run_kernel("convolution_naive", kernel_string,
-        problem_size, args, params,
-        grid_div_y=["block_size_y"], grid_div_x=["block_size_x"], lang='cupy')
-
-    #set non-output fields to None
+    results = kernel_tuner.run_kernel(
+        "convolution_naive",
+        kernel_string,
+        problem_size,
+        args,
+        params,
+        grid_div_y=["block_size_y"],
+        grid_div_x=["block_size_x"],
+        lang="cupy",
+    )
+
+    # set non-output fields to None
     answer = [results[0], None, None]
 
-    #start kernel tuning with correctness verification
-    return kernel_tuner.tune_kernel("convolution_kernel", kernel_string,
-        problem_size, args, tune_params,
-        grid_div_y=grid_div_y, grid_div_x=grid_div_x, verbose=True, cmem_args=cmem_args, answer=answer, lang='cupy')
+    # start kernel tuning with correctness verification
+    return kernel_tuner.tune_kernel(
+        "convolution_kernel",
+        kernel_string,
+        problem_size,
+        args,
+        tune_params,
+        grid_div_y=grid_div_y,
+        grid_div_x=grid_div_x,
+        verbose=True,
+        cmem_args=cmem_args,
+        answer=answer,
+        lang="cupy",
+    )
 
 
 if __name__ == "__main__":
     import time
-    s1 = time.time()*1000
+
+    s1 = time.time() * 1000
     results = tune()
 
-    e1 = time.time()*1000
-    print("\n Actualy time used:", e1-s1)
+    e1 = time.time() * 1000
+    print("\n Actual time used:", e1 - s1)
     import json
-    with open("convolution_RTX_2070.json", 'w') as fp:
-        json.dump(results, fp)
 
+    with open("convolution_RTX_2070.json", "w") as fp:
+        json.dump(results, fp)

examples/cuda/vector_add_observers.py

@@ -6,7 +6,7 @@
 
 import numpy
 from kernel_tuner import tune_kernel
-from kernel_tuner.nvml import NVMLObserver
+from kernel_tuner.observers.nvml import NVMLObserver
 
 def tune():
 

examples/opencl/vector_add_observers.py

@@ -6,7 +6,7 @@
 
 import numpy
 from kernel_tuner import tune_kernel
-from kernel_tuner.nvml import NVMLObserver
+from kernel_tuner.observers.nvml import NVMLObserver
 
 def tune():
 

kernel_tuner/backends/backend.py

@@ -0,0 +1,89 @@
+"""This module contains the interface of all kernel_tuner backends"""
+from __future__ import print_function
+
+from abc import ABC, abstractmethod
+
+
+class Backend(ABC):
+    """Base class for kernel_tuner backends"""
+
+    @abstractmethod
+    def ready_argument_list(self, arguments):
+        """This method must implement the allocation of the arguments on device memory."""
+        pass
+
+    @abstractmethod
+    def compile(self, kernel_instance):
+        """This method must implement the compilation of a kernel into a callable function."""
+        pass
+
+    @abstractmethod
+    def start_event(self):
+        """This method must implement the recording of the start of a measurement."""
+        pass
+
+    @abstractmethod
+    def stop_event(self):
+        """This method must implement the recording of the end of a measurement."""
+        pass
+
+    @abstractmethod
+    def kernel_finished(self):
+        """This method must implement a check that returns True if the kernel has finished, False otherwise."""
+        pass
+
+    @abstractmethod
+    def synchronize(self):
+        """This method must implement a barrier that halts execution until device has finished its tasks."""
+        pass
+
+    @abstractmethod
+    def run_kernel(self, func, gpu_args, threads, grid, stream):
+        """This method must implement the execution of the kernel on the device."""
+        pass
+
+    @abstractmethod
+    def memset(self, allocation, value, size):
+        """This method must implement setting the memory to a value on the device."""
+        pass
+
+    @abstractmethod
+    def memcpy_dtoh(self, dest, src):
+        """This method must implement a device to host copy."""
+        pass
+
+    @abstractmethod
+    def memcpy_htod(self, dest, src):
+        """This method must implement a host to device copy."""
+        pass
+
+
+class GPUBackend(Backend):
+    """Base class for GPU backends"""
+
+    @abstractmethod
+    def __init__(self, device, iterations, compiler_options, observers):
+        pass
+
+    @abstractmethod
+    def copy_constant_memory_args(self, cmem_args):
+        """This method must implement the allocation and copy of constant memory to the GPU."""
+        pass
+
+    @abstractmethod
+    def copy_shared_memory_args(self, smem_args):
+        """This method must implement the dynamic allocation of shared memory on the GPU."""
+        pass
+
+    @abstractmethod
+    def copy_texture_memory_args(self, texmem_args):
+        """This method must implement the allocation and copy of texture memory to the GPU."""
+        pass
+
+
+class CompilerBackend(Backend):
+    """Base class for compiler backends"""
+
+    @abstractmethod
+    def __init__(self, iterations, compiler_options, compiler):
+        pass