cuda-samples/Samples/matrixMulCUBLAS/matrixMulCUBLAS.cpp

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/* Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
*
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* * Redistributions of source code must retain the above copyright
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* 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
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/*
* 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;
}