mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-12-01 13:19:17 +08:00
356 lines
12 KiB
C++
356 lines
12 KiB
C++
/* Copyright (c) 2022, NVIDIA CORPORATION. All rights reserved.
|
|
*
|
|
* Redistribution and use in source and binary forms, with or without
|
|
* modification, are permitted provided that the following conditions
|
|
* are met:
|
|
* * Redistributions of source code must retain the above copyright
|
|
* notice, this list of conditions and the following disclaimer.
|
|
* * Redistributions in binary form must reproduce the above copyright
|
|
* notice, this list of conditions and the following disclaimer in the
|
|
* documentation and/or other materials provided with the distribution.
|
|
* * Neither the name of NVIDIA CORPORATION nor the names of its
|
|
* contributors may be used to endorse or promote products derived
|
|
* from this software without specific prior written permission.
|
|
*
|
|
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
|
|
* EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
|
|
* IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
|
|
* PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
|
|
* CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
|
|
* EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
|
|
* PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
|
|
* PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
|
|
* OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
|
|
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
|
|
* OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
|
|
*/
|
|
|
|
|
|
/* Matrix multiplication: C = A * B.
|
|
* Host code.
|
|
*
|
|
* This sample revisits matrix multiplication with CUDA task. The code of matrix
|
|
* multiplication is exactly the same as in matrixMulDrv sample of this SDK.
|
|
* This sample, however, demonstrates how to link CUDA driver at runtime and
|
|
* how to perform JIT (just-in-time) compilation of CUDA kernel from PTX image,
|
|
* stored in memory.
|
|
*
|
|
* For more details on acquiring auto-generated sources refer README.TXT file
|
|
* in "extras" directory.
|
|
*
|
|
* Unlike CUBLAS, the sample doesn't address high-performance matrix
|
|
* multiplication.
|
|
*/
|
|
|
|
// includes, system
|
|
#include <stdlib.h>
|
|
#include <stdio.h>
|
|
#include <string.h>
|
|
#include <math.h>
|
|
|
|
// includes, CUDA
|
|
#include "cuda_drvapi_dynlink.h"
|
|
#include "helper_cuda_drvapi.h"
|
|
|
|
// includes, project
|
|
#include "matrixMul.h"
|
|
#include "matrixMul_kernel_32_ptxdump.h"
|
|
#include "matrixMul_kernel_64_ptxdump.h"
|
|
|
|
extern "C" void computeGold(float *, const float *, const float *, unsigned int, unsigned int, unsigned int);
|
|
|
|
#if defined _MSC_VER
|
|
#pragma warning (disable : 4312)
|
|
#endif
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// Globals
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
CUcontext g_cuContext;
|
|
bool noprompt = false;
|
|
|
|
static const char *sSDKsample = "matrixMulDynlinkJIT (CUDA dynamic linking)";
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// Allocates a matrix with random float entries
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
void randomInit(float *data, size_t size)
|
|
{
|
|
for (size_t i = 0; i < size; ++i)
|
|
{
|
|
data[i] = rand() / (float)RAND_MAX;
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// CUDA driver runtime linking and initialization
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
CUresult initCUDA(int argc, char **argv, CUfunction *pMatrixMul, int *block_size_out)
|
|
{
|
|
CUresult status;
|
|
CUdevice cuDevice;
|
|
CUmodule cuModule;
|
|
CUfunction cuFunction;
|
|
int major, minor, block_size, devID = 0;
|
|
char deviceName[256];
|
|
|
|
// link to cuda driver dynamically
|
|
checkCudaErrors(cuInit(0, __CUDA_API_VERSION));
|
|
|
|
// This assumes that the user is attempting to specify a explicit device -device=n
|
|
if (argc > 1)
|
|
{
|
|
bool bFound = false;
|
|
|
|
for (int param=0; param < argc; param++)
|
|
{
|
|
if (!strncmp(argv[param], "-device", 7))
|
|
{
|
|
int i=(int)strlen(argv[1]);
|
|
|
|
while (argv[1][i] != '=')
|
|
{
|
|
i--;
|
|
}
|
|
|
|
devID = atoi(&argv[1][++i]);
|
|
bFound = true;
|
|
}
|
|
|
|
if (bFound)
|
|
break;
|
|
}
|
|
}
|
|
|
|
// get cuda-capable device count
|
|
int deviceCount = 0;
|
|
checkCudaErrors(cuDeviceGetCount(&deviceCount));
|
|
|
|
if (deviceCount == 0)
|
|
{
|
|
fprintf(stderr, "No devices supporting CUDA detected, exiting...\n");
|
|
exit(EXIT_SUCCESS);
|
|
}
|
|
|
|
if (devID < 0) devID = 0;
|
|
|
|
if (devID > deviceCount -1)
|
|
{
|
|
fprintf(stderr, "initCUDA (Device=%d) invalid GPU device. %d GPU device(s) detected.\n\n", devID, deviceCount);
|
|
status = CUDA_ERROR_NOT_FOUND;
|
|
|
|
cuCtxDestroy(g_cuContext);
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
|
|
// pick up device with zero ordinal (default, or devID)
|
|
checkCudaErrors(cuDeviceGet(&cuDevice, devID));
|
|
|
|
// get compute capabilities and the devicename
|
|
checkCudaErrors(cuDeviceComputeCapability(&major, &minor, cuDevice));
|
|
checkCudaErrors(cuDeviceGetName(deviceName, 256, cuDevice));
|
|
printf("> Device %d: \"%s\" with Compute %d.%d capability\n", cuDevice, deviceName, major, minor);
|
|
|
|
block_size = 32;
|
|
*block_size_out = block_size;
|
|
|
|
// create context for picked device
|
|
status = cuCtxCreate(&g_cuContext, 0, cuDevice);
|
|
|
|
if (CUDA_SUCCESS != status)
|
|
{
|
|
cuCtxDestroy(g_cuContext);
|
|
exit(EXIT_SUCCESS);
|
|
}
|
|
|
|
// setup JIT compilation options and perform compilation
|
|
{
|
|
// in this branch we use compilation with parameters
|
|
const unsigned int jitNumOptions = 3;
|
|
CUjit_option *jitOptions = new CUjit_option[jitNumOptions];
|
|
void **jitOptVals = new void *[jitNumOptions];
|
|
|
|
// set up size of compilation log buffer
|
|
jitOptions[0] = CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES;
|
|
int jitLogBufferSize = 1024;
|
|
jitOptVals[0] = (void *)(size_t)jitLogBufferSize;
|
|
|
|
// set up pointer to the compilation log buffer
|
|
jitOptions[1] = CU_JIT_INFO_LOG_BUFFER;
|
|
char *jitLogBuffer = new char[jitLogBufferSize];
|
|
jitOptVals[1] = jitLogBuffer;
|
|
|
|
// set up pointer to set the Maximum # of registers for a particular kernel
|
|
jitOptions[2] = CU_JIT_MAX_REGISTERS;
|
|
int jitRegCount = 32;
|
|
jitOptVals[2] = (void *)(size_t)jitRegCount;
|
|
|
|
// compile with set parameters
|
|
printf("> Compiling CUDA module\n");
|
|
|
|
#if defined(_WIN64) || defined(__LP64__)
|
|
status = cuModuleLoadDataEx(&cuModule, matrixMul_kernel_64_ptxdump, jitNumOptions, jitOptions, (void **)jitOptVals);
|
|
#else
|
|
status = cuModuleLoadDataEx(&cuModule, matrixMul_kernel_32_ptxdump, jitNumOptions, jitOptions, (void **)jitOptVals);
|
|
#endif
|
|
|
|
printf("> PTX JIT log:\n%s\n", jitLogBuffer);
|
|
|
|
delete [] jitOptions;
|
|
delete [] jitOptVals;
|
|
delete [] jitLogBuffer;
|
|
}
|
|
|
|
if (CUDA_SUCCESS != status)
|
|
{
|
|
printf("Error while compiling PTX\n");
|
|
cuCtxDestroy(g_cuContext);
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
|
|
// retrieve CUDA function from the compiled module
|
|
status = cuModuleGetFunction(&cuFunction, cuModule,
|
|
(block_size == 16) ? "matrixMul_bs16_32bit" : "matrixMul_bs32_32bit");
|
|
|
|
if (CUDA_SUCCESS != status)
|
|
{
|
|
cuCtxDestroy(g_cuContext);
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
|
|
*pMatrixMul = cuFunction;
|
|
return CUDA_SUCCESS;
|
|
}
|
|
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// Entry point
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
int main(int argc, char **argv)
|
|
{
|
|
printf("[ %s ]\n", sSDKsample);
|
|
|
|
// initialize CUDA
|
|
CUfunction matrixMul = NULL;
|
|
int block_size = 0;
|
|
checkCudaErrors(initCUDA(argc, argv, &matrixMul, &block_size));
|
|
|
|
// set seed for rand()
|
|
srand(2006);
|
|
|
|
// allocate host memory for matrices A and B
|
|
size_t size_A = WA * HA;
|
|
size_t mem_size_A = sizeof(float) * size_A;
|
|
size_t size_B = WB * HB;
|
|
size_t mem_size_B = sizeof(float) * size_B;
|
|
|
|
float *h_A = (float *) malloc(mem_size_A);
|
|
float *h_B = (float *) malloc(mem_size_B);
|
|
|
|
// initialize host memory
|
|
randomInit(h_A, size_A);
|
|
randomInit(h_B, size_B);
|
|
|
|
// allocate device memory
|
|
CUdeviceptr d_A;
|
|
checkCudaErrors(cuMemAlloc(&d_A, mem_size_A));
|
|
CUdeviceptr d_B;
|
|
checkCudaErrors(cuMemAlloc(&d_B, mem_size_B));
|
|
|
|
// copy host memory to device
|
|
checkCudaErrors(cuMemcpyHtoD(d_A, h_A, mem_size_A));
|
|
checkCudaErrors(cuMemcpyHtoD(d_B, h_B, mem_size_B));
|
|
|
|
// allocate device memory for result
|
|
size_t size_C = WC * HC;
|
|
size_t mem_size_C = sizeof(float) * size_C;
|
|
|
|
CUdeviceptr d_C;
|
|
checkCudaErrors(cuMemAlloc(&d_C, mem_size_C));
|
|
|
|
// allocate mem for the result on host side
|
|
float *h_C = (float *) malloc(mem_size_C);
|
|
|
|
#if __CUDA_API_VERSION >= 4000
|
|
{
|
|
// This is the new CUDA 4.0 API for Kernel Parameter passing and Kernel Launching (simpler method)
|
|
int Matrix_Width_A = WA;
|
|
int Matrix_Width_B = WB;
|
|
void *args[5] = { &d_C, &d_A, &d_B, &Matrix_Width_A, &Matrix_Width_B };
|
|
|
|
checkCudaErrors(cuLaunchKernel(matrixMul, (WC/block_size), (HC/block_size), 1,
|
|
block_size , block_size , 1,
|
|
0,
|
|
NULL, args, NULL));
|
|
}
|
|
#else // __CUDA_API_VERSION <= 3020
|
|
{
|
|
// This is the older CUDA Driver API for Kernel Parameter passing and Kernel Launching
|
|
int offset = 0;
|
|
{
|
|
// setup execution parameters
|
|
checkCudaErrors(cuParamSetv(matrixMul, offset, &d_C, sizeof(d_C)));
|
|
offset += sizeof(d_C);
|
|
|
|
checkCudaErrors(cuParamSetv(matrixMul, offset, &d_A, sizeof(d_A)));
|
|
offset += sizeof(d_A);
|
|
|
|
checkCudaErrors(cuParamSetv(matrixMul, offset, &d_B, sizeof(d_B)));
|
|
offset += sizeof(d_B);
|
|
}
|
|
|
|
int Matrix_Width_A = WA;
|
|
int Matrix_Width_B = WB;
|
|
|
|
checkCudaErrors(cuParamSeti(matrixMul, offset, Matrix_Width_A));
|
|
offset += sizeof(Matrix_Width_A);
|
|
|
|
checkCudaErrors(cuParamSeti(matrixMul, offset, Matrix_Width_B));
|
|
offset += sizeof(Matrix_Width_B);
|
|
|
|
checkCudaErrors(cuParamSetSize(matrixMul, offset));
|
|
checkCudaErrors(cuFuncSetBlockShape(matrixMul, block_size, block_size, 1));
|
|
checkCudaErrors(cuFuncSetSharedSize(matrixMul, 2*block_size*block_size*sizeof(float)));
|
|
|
|
// set execution configuration for the CUDA kernel
|
|
checkCudaErrors(cuLaunchGrid(matrixMul, WC / block_size, HC / block_size));
|
|
}
|
|
#endif
|
|
|
|
checkCudaErrors(cuCtxSynchronize());
|
|
|
|
// copy result from device to host
|
|
checkCudaErrors(cuMemcpyDtoH((void *) h_C, d_C, mem_size_C));
|
|
|
|
// compute reference solution
|
|
float *reference = (float *) malloc(mem_size_C);
|
|
computeGold(reference, h_A, h_B, HA, WA, WB);
|
|
|
|
// check result
|
|
float diff=0.0f;
|
|
|
|
for (unsigned int i=0; i<size_C; i++)
|
|
{
|
|
float tmp = reference[i] - h_C[i];
|
|
diff += tmp*tmp;
|
|
}
|
|
|
|
int res = (diff / (float)size_C < 1e-6f);
|
|
|
|
// clean up memory
|
|
free(h_A);
|
|
free(h_B);
|
|
free(h_C);
|
|
free(reference);
|
|
checkCudaErrors(cuMemFree(d_A));
|
|
checkCudaErrors(cuMemFree(d_B));
|
|
checkCudaErrors(cuMemFree(d_C));
|
|
checkCudaErrors(cuCtxDestroy(g_cuContext));
|
|
|
|
printf("Test run %s\n", (1==res) ? "success!" : "failed!");
|
|
|
|
exit((1 == res) ? EXIT_SUCCESS : EXIT_FAILURE);
|
|
}
|