mirror of
https://github.com/NVIDIA/cuda-samples.git
synced 2024-10-06 12:29:15 +08:00
366 lines
13 KiB
C++
366 lines
13 KiB
C++
/* Copyright (c) 2021, 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 implements matrix multiplication as described in Chapter 3
|
|
* of the programming guide and uses the CUBLAS library to demonstrate
|
|
* the best performance.
|
|
|
|
* SOME PRECAUTIONS:
|
|
* IF WE WANT TO CALCULATE ROW-MAJOR MATRIX MULTIPLY C = A * B,
|
|
* WE JUST NEED CALL CUBLAS API IN A REVERSE ORDER: cublasSegemm(B, A)!
|
|
* The reason is explained as follows:
|
|
|
|
* CUBLAS library uses column-major storage, but C/C++ use row-major storage.
|
|
* When passing the matrix pointer to CUBLAS, the memory layout alters from
|
|
* row-major to column-major, which is equivalent to an implicit transpose.
|
|
|
|
* In the case of row-major C/C++ matrix A, B, and a simple matrix multiplication
|
|
* C = A * B, we can't use the input order like cublasSgemm(A, B) because of
|
|
* implicit transpose. The actual result of cublasSegemm(A, B) is A(T) * B(T).
|
|
* If col(A(T)) != row(B(T)), equal to row(A) != col(B), A(T) and B(T) are not
|
|
* multipliable. Moreover, even if A(T) and B(T) are multipliable, the result C
|
|
* is a column-based cublas matrix, which means C(T) in C/C++, we need extra
|
|
* transpose code to convert it to a row-based C/C++ matrix.
|
|
|
|
* To solve the problem, let's consider our desired result C, a row-major matrix.
|
|
* In cublas format, it is C(T) actually (because of the implicit transpose).
|
|
* C = A * B, so C(T) = (A * B) (T) = B(T) * A(T). Cublas matrice B(T) and A(T)
|
|
* happen to be C/C++ matrice B and A (still because of the implicit transpose)!
|
|
* We don't need extra transpose code, we only need alter the input order!
|
|
*
|
|
* CUBLAS provides high-performance matrix multiplication.
|
|
* See also:
|
|
* V. Volkov and J. Demmel, "Benchmarking GPUs to tune dense linear algebra,"
|
|
* in Proc. 2008 ACM/IEEE Conf. on Supercomputing (SC '08),
|
|
* Piscataway, NJ: IEEE Press, 2008, pp. Art. 31:1-11.
|
|
*/
|
|
|
|
// Utilities and system includes
|
|
#include <assert.h>
|
|
#include <helper_string.h> // helper for shared functions common to CUDA Samples
|
|
|
|
// CUDA runtime
|
|
#include <cuda_runtime.h>
|
|
#include <cublas_v2.h>
|
|
|
|
// CUDA and CUBLAS functions
|
|
#include <helper_functions.h>
|
|
#include <helper_cuda.h>
|
|
|
|
#ifndef min
|
|
#define min(a, b) ((a < b) ? a : b)
|
|
#endif
|
|
#ifndef max
|
|
#define max(a, b) ((a > b) ? a : b)
|
|
#endif
|
|
|
|
// Optional Command-line multiplier for matrix sizes
|
|
typedef struct _matrixSize {
|
|
unsigned int uiWA, uiHA, uiWB, uiHB, uiWC, uiHC;
|
|
} sMatrixSize;
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
//! Compute reference data set matrix multiply on CPU
|
|
//! C = A * B
|
|
//! @param C reference data, computed but preallocated
|
|
//! @param A matrix A as provided to device
|
|
//! @param B matrix B as provided to device
|
|
//! @param hA height of matrix A
|
|
//! @param wB width of matrix B
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
void matrixMulCPU(float *C, const float *A, const float *B, unsigned int hA,
|
|
unsigned int wA, unsigned int wB) {
|
|
for (unsigned int i = 0; i < hA; ++i)
|
|
for (unsigned int j = 0; j < wB; ++j) {
|
|
double sum = 0;
|
|
|
|
for (unsigned int k = 0; k < wA; ++k) {
|
|
double a = A[i * wA + k];
|
|
double b = B[k * wB + j];
|
|
sum += a * b;
|
|
}
|
|
|
|
C[i * wB + j] = (float)sum;
|
|
}
|
|
}
|
|
|
|
// Allocates a matrix with random float entries.
|
|
void randomInit(float *data, int size) {
|
|
for (int i = 0; i < size; ++i) data[i] = rand() / (float)RAND_MAX;
|
|
}
|
|
|
|
void printDiff(float *data1, float *data2, int width, int height,
|
|
int iListLength, float fListTol) {
|
|
printf("Listing first %d Differences > %.6f...\n", iListLength, fListTol);
|
|
int i, j, k;
|
|
int error_count = 0;
|
|
|
|
for (j = 0; j < height; j++) {
|
|
if (error_count < iListLength) {
|
|
printf("\n Row %d:\n", j);
|
|
}
|
|
|
|
for (i = 0; i < width; i++) {
|
|
k = j * width + i;
|
|
float fDiff = fabs(data1[k] - data2[k]);
|
|
|
|
if (fDiff > fListTol) {
|
|
if (error_count < iListLength) {
|
|
printf(" Loc(%d,%d)\tCPU=%.5f\tGPU=%.5f\tDiff=%.6f\n", i, j,
|
|
data1[k], data2[k], fDiff);
|
|
}
|
|
|
|
error_count++;
|
|
}
|
|
}
|
|
}
|
|
|
|
printf(" \n Total Errors = %d\n", error_count);
|
|
}
|
|
|
|
void initializeCUDA(int argc, char **argv, int &devID, int &iSizeMultiple,
|
|
sMatrixSize &matrix_size) {
|
|
// By default, we use device 0, otherwise we override the device ID based on
|
|
// what is provided at the command line
|
|
cudaError_t error;
|
|
devID = 0;
|
|
|
|
devID = findCudaDevice(argc, (const char **)argv);
|
|
|
|
if (checkCmdLineFlag(argc, (const char **)argv, "sizemult")) {
|
|
iSizeMultiple =
|
|
getCmdLineArgumentInt(argc, (const char **)argv, "sizemult");
|
|
}
|
|
|
|
iSizeMultiple = min(iSizeMultiple, 10);
|
|
iSizeMultiple = max(iSizeMultiple, 1);
|
|
|
|
cudaDeviceProp deviceProp;
|
|
|
|
error = cudaGetDeviceProperties(&deviceProp, devID);
|
|
|
|
if (error != cudaSuccess) {
|
|
printf("cudaGetDeviceProperties returned error code %d, line(%d)\n", error,
|
|
__LINE__);
|
|
exit(EXIT_FAILURE);
|
|
}
|
|
|
|
printf("GPU Device %d: \"%s\" with compute capability %d.%d\n\n", devID,
|
|
deviceProp.name, deviceProp.major, deviceProp.minor);
|
|
|
|
int block_size = 32;
|
|
|
|
matrix_size.uiWA = 3 * block_size * iSizeMultiple;
|
|
matrix_size.uiHA = 4 * block_size * iSizeMultiple;
|
|
matrix_size.uiWB = 2 * block_size * iSizeMultiple;
|
|
matrix_size.uiHB = 3 * block_size * iSizeMultiple;
|
|
matrix_size.uiWC = 2 * block_size * iSizeMultiple;
|
|
matrix_size.uiHC = 4 * block_size * iSizeMultiple;
|
|
|
|
printf("MatrixA(%u,%u), MatrixB(%u,%u), MatrixC(%u,%u)\n", matrix_size.uiHA,
|
|
matrix_size.uiWA, matrix_size.uiHB, matrix_size.uiWB, matrix_size.uiHC,
|
|
matrix_size.uiWC);
|
|
|
|
if (matrix_size.uiWA != matrix_size.uiHB ||
|
|
matrix_size.uiHA != matrix_size.uiHC ||
|
|
matrix_size.uiWB != matrix_size.uiWC) {
|
|
printf("ERROR: Matrix sizes do not match!\n");
|
|
exit(-1);
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
//! Run a simple test matrix multiply using CUBLAS
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
int matrixMultiply(int argc, char **argv, int devID, sMatrixSize &matrix_size) {
|
|
cudaDeviceProp deviceProp;
|
|
|
|
checkCudaErrors(cudaGetDeviceProperties(&deviceProp, devID));
|
|
|
|
int block_size = 32;
|
|
|
|
// set seed for rand()
|
|
srand(2006);
|
|
|
|
// allocate host memory for matrices A and B
|
|
unsigned int size_A = matrix_size.uiWA * matrix_size.uiHA;
|
|
unsigned int mem_size_A = sizeof(float) * size_A;
|
|
float *h_A = (float *)malloc(mem_size_A);
|
|
unsigned int size_B = matrix_size.uiWB * matrix_size.uiHB;
|
|
unsigned int mem_size_B = sizeof(float) * size_B;
|
|
float *h_B = (float *)malloc(mem_size_B);
|
|
|
|
// set seed for rand()
|
|
srand(2006);
|
|
|
|
// initialize host memory
|
|
randomInit(h_A, size_A);
|
|
randomInit(h_B, size_B);
|
|
|
|
// allocate device memory
|
|
float *d_A, *d_B, *d_C;
|
|
unsigned int size_C = matrix_size.uiWC * matrix_size.uiHC;
|
|
unsigned int mem_size_C = sizeof(float) * size_C;
|
|
|
|
// allocate host memory for the result
|
|
float *h_C = (float *)malloc(mem_size_C);
|
|
float *h_CUBLAS = (float *)malloc(mem_size_C);
|
|
|
|
checkCudaErrors(cudaMalloc((void **)&d_A, mem_size_A));
|
|
checkCudaErrors(cudaMalloc((void **)&d_B, mem_size_B));
|
|
checkCudaErrors(cudaMemcpy(d_A, h_A, mem_size_A, cudaMemcpyHostToDevice));
|
|
checkCudaErrors(cudaMemcpy(d_B, h_B, mem_size_B, cudaMemcpyHostToDevice));
|
|
checkCudaErrors(cudaMalloc((void **)&d_C, mem_size_C));
|
|
|
|
// setup execution parameters
|
|
dim3 threads(block_size, block_size);
|
|
dim3 grid(matrix_size.uiWC / threads.x, matrix_size.uiHC / threads.y);
|
|
|
|
// create and start timer
|
|
printf("Computing result using CUBLAS...");
|
|
|
|
// execute the kernel
|
|
int nIter = 30;
|
|
|
|
// CUBLAS version 2.0
|
|
{
|
|
const float alpha = 1.0f;
|
|
const float beta = 0.0f;
|
|
cublasHandle_t handle;
|
|
cudaEvent_t start, stop;
|
|
|
|
checkCudaErrors(cublasCreate(&handle));
|
|
|
|
// Perform warmup operation with cublas
|
|
checkCudaErrors(cublasSgemm(
|
|
handle, CUBLAS_OP_N, CUBLAS_OP_N, matrix_size.uiWB, matrix_size.uiHA,
|
|
matrix_size.uiWA, &alpha, d_B, matrix_size.uiWB, d_A, matrix_size.uiWA,
|
|
&beta, d_C, matrix_size.uiWB));
|
|
|
|
// Allocate CUDA events that we'll use for timing
|
|
checkCudaErrors(cudaEventCreate(&start));
|
|
checkCudaErrors(cudaEventCreate(&stop));
|
|
|
|
// Record the start event
|
|
checkCudaErrors(cudaEventRecord(start, NULL));
|
|
|
|
for (int j = 0; j < nIter; j++) {
|
|
// note cublas is column primary!
|
|
// need to transpose the order
|
|
checkCudaErrors(cublasSgemm(
|
|
handle, CUBLAS_OP_N, CUBLAS_OP_N, matrix_size.uiWB, matrix_size.uiHA,
|
|
matrix_size.uiWA, &alpha, d_B, matrix_size.uiWB, d_A,
|
|
matrix_size.uiWA, &beta, d_C, matrix_size.uiWB));
|
|
}
|
|
|
|
printf("done.\n");
|
|
|
|
// Record the stop event
|
|
checkCudaErrors(cudaEventRecord(stop, NULL));
|
|
|
|
// Wait for the stop event to complete
|
|
checkCudaErrors(cudaEventSynchronize(stop));
|
|
|
|
float msecTotal = 0.0f;
|
|
checkCudaErrors(cudaEventElapsedTime(&msecTotal, start, stop));
|
|
|
|
// Compute and print the performance
|
|
float msecPerMatrixMul = msecTotal / nIter;
|
|
double flopsPerMatrixMul = 2.0 * (double)matrix_size.uiHC *
|
|
(double)matrix_size.uiWC *
|
|
(double)matrix_size.uiHB;
|
|
double gigaFlops =
|
|
(flopsPerMatrixMul * 1.0e-9f) / (msecPerMatrixMul / 1000.0f);
|
|
printf("Performance= %.2f GFlop/s, Time= %.3f msec, Size= %.0f Ops\n",
|
|
gigaFlops, msecPerMatrixMul, flopsPerMatrixMul);
|
|
|
|
// copy result from device to host
|
|
checkCudaErrors(
|
|
cudaMemcpy(h_CUBLAS, d_C, mem_size_C, cudaMemcpyDeviceToHost));
|
|
|
|
// Destroy the handle
|
|
checkCudaErrors(cublasDestroy(handle));
|
|
}
|
|
|
|
// compute reference solution
|
|
printf("Computing result using host CPU...");
|
|
float *reference = (float *)malloc(mem_size_C);
|
|
matrixMulCPU(reference, h_A, h_B, matrix_size.uiHA, matrix_size.uiWA,
|
|
matrix_size.uiWB);
|
|
printf("done.\n");
|
|
|
|
// check result (CUBLAS)
|
|
bool resCUBLAS = sdkCompareL2fe(reference, h_CUBLAS, size_C, 1.0e-6f);
|
|
|
|
if (resCUBLAS != true) {
|
|
printDiff(reference, h_CUBLAS, matrix_size.uiWC, matrix_size.uiHC, 100,
|
|
1.0e-5f);
|
|
}
|
|
|
|
printf("Comparing CUBLAS Matrix Multiply with CPU results: %s\n",
|
|
(true == resCUBLAS) ? "PASS" : "FAIL");
|
|
|
|
printf(
|
|
"\nNOTE: The CUDA Samples are not meant for performance measurements. "
|
|
"Results may vary when GPU Boost is enabled.\n");
|
|
|
|
// clean up memory
|
|
free(h_A);
|
|
free(h_B);
|
|
free(h_C);
|
|
free(reference);
|
|
checkCudaErrors(cudaFree(d_A));
|
|
checkCudaErrors(cudaFree(d_B));
|
|
checkCudaErrors(cudaFree(d_C));
|
|
|
|
if (resCUBLAS == true) {
|
|
return EXIT_SUCCESS; // return value = 1
|
|
} else {
|
|
return EXIT_FAILURE; // return value = 0
|
|
}
|
|
}
|
|
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
// Program main
|
|
////////////////////////////////////////////////////////////////////////////////
|
|
int main(int argc, char **argv) {
|
|
printf("[Matrix Multiply CUBLAS] - Starting...\n");
|
|
|
|
int devID = 0, sizeMult = 5;
|
|
sMatrixSize matrix_size;
|
|
|
|
initializeCUDA(argc, argv, devID, sizeMult, matrix_size);
|
|
|
|
int matrix_result = matrixMultiply(argc, argv, devID, matrix_size);
|
|
|
|
return matrix_result;
|
|
}
|