C++中如何使用CUDA进行高性能大规模矩阵乘法运算？| cublasSgemm for large matrix multiplication on gpu in C++

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cublasSgemm for large matrix multiplication on gpu in C++

Guide

code

demo.cu

#include <cuda_runtime.h>
#include <cublas.h>
#include <cublas_api.h>
#include <cublas_v2.h>

bool CompareFeatureMtoN_gpu(float * featureM, float * featureN, float * result, 
	int count_m, int count_n, int size, int gpu_id) {
	float *dev_featureM = 0;
	float *dev_featureN = 0;
	float *dev_result = 0;
	const float alpha = 1, beta = 0;
	cublasHandle_t handle;
	cudaError_t cudaStatus;

	cudaStatus = cudaSetDevice(gpu_id);
	if (cudaStatus != cudaSuccess) {
		printf("cudaSetDevice failed!  Do you have a CUDA-capable GPU installed?\n");
		goto out;
	}
	cublasCreate(&handle);

	cudaStatus = cudaMalloc((void**)&dev_featureM, count_m * size * sizeof(float));
	if (cudaStatus != cudaSuccess) {
		printf("%s, line %d, cudaMalloc failed!\n", __func__, __LINE__);
		goto out;
	}
	cudaStatus = cudaMalloc((void**)&dev_featureN, count_n * size * sizeof(float));
	if (cudaStatus != cudaSuccess) {
		printf("%s, line %d, cudaMalloc failed!\n", __func__, __LINE__);
		goto out;
	}
	cudaStatus = cudaMalloc((void**)&dev_result, count_m * count_n * sizeof(float));
	if (cudaStatus != cudaSuccess) {
		printf("%s, line %d, cudaMalloc failed!\n", __func__, __LINE__);
		goto out;
	}

	cudaStatus = cudaMemcpy(dev_featureM, featureM, count_m * size * sizeof(float), 
		cudaMemcpyHostToDevice);
	if (cudaStatus != cudaSuccess) {
		printf("%s, line %d, cudaMalloc failed!\n", __func__, __LINE__);
		goto out;
	}
	cudaStatus = cudaMemcpy(dev_featureN, featureN, count_n * size * sizeof(float), 
		cudaMemcpyHostToDevice);
	if (cudaStatus != cudaSuccess) {
		printf("%s, line %d, cudaMalloc failed!\n", __func__, __LINE__);
		goto out;
	}

	/*
	
	CUBLAS assumes that the matrix in the device is stored in column major:

	" where α and β are scalars, and A , B and C are matrices stored in column-major 
	format with dimensions op ( A ) m × k , op ( B ) k × n and C m × n , respectively. 
	 
	 Also, for matrix A


	 // Multiply the arrays A and B on GPU and save the result in C (coloum-major)
 	 // C(m,n) = A(m,k) * B(k,n)

	  cublasSgemm(handle, CUBLAS_OP_N, CUBLAS_OP_N, m, n, k, &alpha, A, lda, B, ldb, &beta, C, ldc);
	 */

	cublasSgemm(handle, CUBLAS_OP_T, CUBLAS_OP_N, count_n, count_m, size, 
		&alpha, dev_featureN, size, dev_featureM, size, &beta, dev_result, count_n);
	cudaStatus = cudaThreadSynchronize();

	cudaStatus = cudaMemcpy(result, dev_result, count_m * count_n  * sizeof(float), 
		cudaMemcpyDeviceToHost);
	if (cudaStatus != cudaSuccess) {
		printf("%s, line %d, cudaMemcpy failed!\n", __func__, __LINE__);
		goto out;
	}

out:
	if(dev_featureM) cudaFree(dev_featureM);
	if(dev_featureN) cudaFree(dev_featureN);
	if(dev_result) cudaFree(dev_result);
	cublasDestroy(handle);
	return cudaStatus == cudaSuccess;
}

usage

demo.cu

void test_feature_compare()
{
	/*

	[a1]
	[a2]
	[a3]  
		 * [b1]
		   [b2]

	[10,35]
	[10,35]
	[10,35]
	*/
	std::vector<float> f1{0,1,2,3,4,5,6,7,8,9};
	std::vector<float> f2{1,1,1,1,1,0,0,0,0,0},f22{0,0,0,0,0,1,1,1,1,1};

	std::vector<std::vector<float>> A,B;
	// 3*10
	A.push_back(f1);
	A.push_back(f1);
	A.push_back(f1);

	// 10 * 2
	B.push_back(f2);
	B.push_back(f22);


	int m = 3;
	int n = 2; 
	int dim = 10;
	int gpu_id = 0;

	float* feature_m = new float[ m*dim ];
	float* feature_n = new float[ n*dim ];
	auto tmp = feature_m;
	for (int i = 0; i < m; i++) {
		for (int j = 0; j < dim; j++)
			*tmp++ = A[i][j];
	}

	tmp = feature_n;
	for (int i = 0; i < n; i++) {
		for (int j = 0; j < dim; j++)
			*tmp++ = B[i][j];
	}

	printf("m = %d, n= %d, size= %d \n", m, n, dim); // 3, 2, 10

	//float* result = CompareFeatureMtoN(feature_m, m*dim, feature_n, n*dim, dim, gpu_id);

	float* result = new float[m*n];
	CompareFeatureMtoN_gpu(feature_m, feature_n, result, m, n, dim, gpu_id);

	tmp = result;
	for(int i=0;i<6;i++)
		printf("%f ", *(tmp++));

	delete []feature_m;
	delete []feature_n;
	delete []result;
}

output

m = 3, n= 2, size= 10 
10.000000 35.000000 10.000000 35.000000 10.000000 35.000000

Reference

History

  • 20191015: created.

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posted @ 2019-12-26 15:42  kezunlin  阅读(448)  评论(0)  编辑  收藏  举报