CUDA matrixMulCUBLAS

 

///////////////////////////////////////////////////////////////////////////
//  matirxmulCUBLAS.cpp
//
// Copyright 1993-2013 NVIDIA Corporation.  All rights reserved.
//
// Please refer to the NVIDIA end user license agreement (EULA) associated
// with this source code for terms and conditions that govern your use of
// this software. Any use, reproduction, disclosure, or distribution of
// this software and related documentation outside the terms of the EULA
// is strictly prohibited.
// 
////////////////////////////////////////////////////////////////////////////

//
// 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 implict 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
// implict 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 (becuase of the implict 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 becuase of the implict 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 Superconducting (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 SDK samples

// CUDA runtime
#include <cuda_runtime.h>
#include <cublas_v2.h>

// CUDA and CUBLAS functions
#include <helper_functions.h>

#ifndef min
#define min(a,b) ((a < b) ? a : b)
#endif
#ifndef max
#define max(a,b) ((a > b) ? a : b)
#endif

typedef struct _matrixSize      // Optional Command-line multiplier for matrix sizes
{
    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;
        }
}

////////////////////////////////////////////////////////////////////////////////
// These are CUDA Helper functions (in addition to helper_cuda.h)

void inline checkError(cublasStatus_t status, const char *msg)
{
    if (status != CUBLAS_STATUS_SUCCESS)
    {
        printf("%s", msg);
        exit(EXIT_FAILURE);
    }
}
// end of CUDA Helper Functions

// 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;

    if (checkCmdLineFlag(argc, (const char **)argv, "device"))
    {
        devID = getCmdLineArgumentInt(argc, (const char **)argv, "device");
        error = cudaSetDevice(devID);

        if (error != cudaSuccess)
        {
            printf("cudaSetDevice returned error code %d, line(%d)\n", error, __LINE__);
            exit(EXIT_FAILURE);
        }
    }

    // get number of SMs on this GPU
    error = cudaGetDevice(&devID);

    if (error != cudaSuccess)
    {
        printf("cudaGetDevice returned error code %d, line(%d)\n", error, __LINE__);
        exit(EXIT_FAILURE);
    }


    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);

    // use a larger block size for Fermi and above
    int block_size = (deviceProp.major < 2) ? 16 : 32;

    matrix_size.uiWA = 2 * block_size * iSizeMultiple;
    matrix_size.uiHA = 4 * block_size * iSizeMultiple;
    matrix_size.uiWB = 2 * block_size * iSizeMultiple;
    matrix_size.uiHB = 4 * 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.uiWA, matrix_size.uiHA,
           matrix_size.uiWB, matrix_size.uiHB,
           matrix_size.uiWC, matrix_size.uiHC);
}

////////////////////////////////////////////////////////////////////////////////
//! Run a simple test matrix multiply using CUBLAS
////////////////////////////////////////////////////////////////////////////////
int matrixMultiply(int argc, char **argv, int devID, sMatrixSize &matrix_size)
{
    cudaDeviceProp deviceProp;
    cudaError_t error;

    error = cudaGetDeviceProperties(&deviceProp, devID);

    if (error != cudaSuccess)
    {
        printf("cudaGetDeviceProperties returned error code %d, line(%d)\n", error, __LINE__);
        exit(EXIT_FAILURE);
    }

    // use a larger block size for Fermi and above
    int block_size = (deviceProp.major < 2) ? 16 : 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);

    error = cudaMalloc((void **) &d_A, mem_size_A);

    if (error != cudaSuccess)
    {
        printf("cudaMalloc d_A returned error code %d, line(%d)\n", error, __LINE__);
        exit(EXIT_FAILURE);
    }

    error = cudaMalloc((void **) &d_B, mem_size_B);

    if (error != cudaSuccess)
    {
        printf("cudaMalloc d_B returned error code %d, line(%d)\n", error, __LINE__);
        exit(EXIT_FAILURE);
    }

    // copy host memory to device
    error = cudaMemcpy(d_A, h_A, mem_size_A, cudaMemcpyHostToDevice);

    if (error != cudaSuccess)
    {
        printf("cudaMemcpy d_A h_A returned error code %d, line(%d)\n", error, __LINE__);
        exit(EXIT_FAILURE);
    }

    error = cudaMemcpy(d_B, h_B, mem_size_B, cudaMemcpyHostToDevice);

    if (error != cudaSuccess)
    {
        printf("cudaMemcpy d_B h_B returned error code %d, line(%d)\n", error, __LINE__);
        exit(EXIT_FAILURE);
    }

    error = cudaMalloc((void **) &d_C, mem_size_C);

    if (error != cudaSuccess)
    {
        printf("cudaMalloc d_C returned error code %d, line(%d)\n", error, __LINE__);
        exit(EXIT_FAILURE);
    }

    // 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
    {
        cublasHandle_t handle;

        cublasStatus_t ret;

        ret = cublasCreate(&handle);

        if (ret != CUBLAS_STATUS_SUCCESS)
        {
            printf("cublasCreate returned error code %d, line(%d)\n", ret, __LINE__);
            exit(EXIT_FAILURE);
        }

        const float alpha = 1.0f;
        const float beta  = 0.0f;
        //Perform warmup operation with cublas
        ret = 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.uiWA);

        if (ret != CUBLAS_STATUS_SUCCESS)
        {
            printf("cublasSgemm returned error code %d, line(%d)\n", ret, __LINE__);
            exit(EXIT_FAILURE);
        }

        // Allocate CUDA events that we'll use for timing
        cudaEvent_t start;
        error = cudaEventCreate(&start);

        if (error != cudaSuccess)
        {
            fprintf(stderr, "Failed to create start event (error code %s)!\n", cudaGetErrorString(error));
            exit(EXIT_FAILURE);
        }

        cudaEvent_t stop;
        error = cudaEventCreate(&stop);

        if (error != cudaSuccess)
        {
            fprintf(stderr, "Failed to create stop event (error code %s)!\n", cudaGetErrorString(error));
            exit(EXIT_FAILURE);
        }

        // Record the start event
        error = cudaEventRecord(start, NULL);

        if (error != cudaSuccess)
        {
            fprintf(stderr, "Failed to record start event (error code %s)!\n", cudaGetErrorString(error));
            exit(EXIT_FAILURE);
        }

        for (int j = 0; j < nIter; j++)
        {
            //note cublas is column primary!
            //need to transpose the order
            ret = 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.uiWA);

            if (ret != CUBLAS_STATUS_SUCCESS)
            {
                printf("cublasSgemm returned error code %d, line(%d)\n", ret, __LINE__);
                exit(EXIT_FAILURE);
            }
        }

        printf("done.\n");

        // Record the stop event
        error = cudaEventRecord(stop, NULL);

        if (error != cudaSuccess)
        {
            fprintf(stderr, "Failed to record stop event (error code %s)!\n", cudaGetErrorString(error));
            exit(EXIT_FAILURE);
        }

        // Wait for the stop event to complete
        error = cudaEventSynchronize(stop);

        if (error != cudaSuccess)
        {
            fprintf(stderr, "Failed to synchronize on the stop event (error code %s)!\n", cudaGetErrorString(error));
            exit(EXIT_FAILURE);
        }

        float msecTotal = 0.0f;
        error = cudaEventElapsedTime(&msecTotal, start, stop);

        if (error != cudaSuccess)
        {
            fprintf(stderr, "Failed to get time elapsed between events (error code %s)!\n", cudaGetErrorString(error));
            exit(EXIT_FAILURE);
        }

        // Compute and print the performance
        float msecPerMatrixMul = msecTotal / nIter;
        double flopsPerMatrixMul = 2.0 * (double)matrix_size.uiWA * (double)matrix_size.uiHA * (double)matrix_size.uiWB;
        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
        error = cudaMemcpy(h_CUBLAS, d_C, mem_size_C, cudaMemcpyDeviceToHost);

        if (error != cudaSuccess)
        {
            printf("cudaMemcpy h_CUBLAS d_C returned error code %d, line(%d)\n", error, __LINE__);
            exit(EXIT_FAILURE);
        }

        checkError(cublasDestroy(handle), "cublasDestroy() error!\n");
    }

    // 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");

    // clean up memory
    free(h_A);
    free(h_B);
    free(h_C);
    free(reference);
    cudaFree(d_A);
    cudaFree(d_B);
    cudaFree(d_C);

    cudaDeviceReset();

    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);

    exit(matrix_result);
}

 

CUDA_PATH ?=/usr/local/cuda-7.0
NVCC      :=$(CUDA_PATH)/bin/nvcc -ccbin g++
INCLUDE   :=-I/usr/local/cuda/include/\
           -I/usr/local/cuda/samples/common/inc\
        -I/usr/include/c++\
        -I./

LIBRARIES :=-L/usr/local/cuda/lib64 -lcudart -lcublas
TARGETS   :=matrixMulCUBLAS
OBJECTS   :=$(addsuffix .o, $(TARGETS))

.SUFFIXES:.o .cu .cpp
.cu.o: 
    $(NVCC) -arch=sm_20  $(INCLUDE) -c -o $@  $< $(LIBRARIES)
.cpp.o:
    g++ $(INCLUDE) -c -o $@ $< $(LIBRARYIES)

all:$(OBJECTS)
    #sudo cp /usr/local/cuda/lib64/libcublas.so.7.0 /usr/lib
    ln -s libcudart.so.7.0  libcudart.so
    ln -s libcudart.so.7.0  libcudart.so.7
    ln -s libcublas.so.7.0 libcublas.so
    ln -s libcublas.so.7.0 libcublas.so.7
    g++    $(INCLUDE) -o $(TARGETS) $^ $(LIBRARIES)
run:
    ./$(TARGETS)
clean:
    rm -rf *.o libcudart.so libcudart.so.7\
        libcublas.so libcublas.so.7 $(TARGETS)

./matrixMulCUBLAS
[Matrix Multiply CUBLAS] - Starting...
GPU Device 0: "GeForce GTX 650 Ti" with compute capability 3.0

MatrixA(320,640), MatrixB(320,640), MatrixC(320,640)
Computing result using CUBLAS...done.
Performance= 612.46 GFlop/s, Time= 0.214 msec, Size= 131072000 Ops
Computing result using host CPU...done.
Comparing CUBLAS Matrix Multiply with CPU results: PASS