matmul3v2.cu

// System includes
#include <stdio.h>
#include <stdlib.h>
#include <assert.h>

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

/**
* Matrix multiplication (CUDA Kernel) on the device: C = A * B
*/
#define TILE_WIDTH 16
#define BLOCK_SIZE 16
#define N 2048

__global__ void
matrixMulCUDA(float *C, float *A, float *B, int n)
{

	__shared__ float s_a[TILE_WIDTH][TILE_WIDTH];
	__shared__ float s_b[TILE_WIDTH][TILE_WIDTH];

	int start_row = blockDim.y * blockIdx.y + threadIdx.y * TILE_WIDTH;
	int end_row = start_row + TILE_WIDTH;
	int start_col = blockDim.x * blockIdx.x + threadIdx.x * TILE_WIDTH;
	int end_col = start_col + TILE_WIDTH;

	int tx = threadIdx.x;
	int ty = threadIdx.y;

	for (int row = start_row; row < end_row; row++)
	{
		for (int col = start_col; col < end_col; col++)
		{
			float C_val = 0;
			for (int i = 0; i < n / (TILE_WIDTH * BLOCK_SIZE); i++)
			{
				for (int j = 0; j < TILE_WIDTH; j++)
				{
					s_a[ty][tx] = A[(row * n) + (i * TILE_WIDTH * BLOCK_SIZE) + (j * TILE_WIDTH) + tx];
					s_b[ty][tx] = B[( (i * TILE_WIDTH * BLOCK_SIZE) + (j * TILE_WIDTH) + ty ) * N + col];
					
					__syncthreads();

					for(int p = 0; p < TILE_WIDTH;p++)
					{
						C_val += s_a[ty][p] * s_b[p][tx];
					}
					__syncthreads();
				}
			}
			C[row * n + col] = C_val;
		}
	}
}

void constantInit(float *data, int size, float val)
{
	for (int i = 0; i < size; ++i)
	{
		data[i] = val;
	}
}

/**
* Run a simple test of matrix multiplication using CUDA
*/
int matrixMultiply(int argc, char **argv, int n)
{
	// Allocate host memory for matrices A and B
	unsigned int size_A = n * n;
	unsigned int mem_size_A = sizeof(float) * size_A;
	float *h_A = (float *)malloc(mem_size_A);
	unsigned int size_B = n * n;
	unsigned int mem_size_B = sizeof(float) * size_B;
	float *h_B = (float *)malloc(mem_size_B);

	// Initialize host memory
	const float valB = 0.01f;
	constantInit(h_A, size_A, 1.0f);
	constantInit(h_B, size_B, valB);

	// Allocate device memory
	float *d_A, *d_B, *d_C;

	// Allocate host matrix C
	unsigned int mem_size_C = n * n * sizeof(float);
	float *h_C = (float *)malloc(mem_size_C);

	if (h_C == NULL)
	{
		fprintf(stderr, "Failed to allocate host matrix C!\n");
		exit(EXIT_FAILURE);
	}

	cudaError_t error;

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

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

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

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

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

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

	// Setup execution parameters
	dim3 threads(BLOCK_SIZE, BLOCK_SIZE, 1);
	dim3 grid((((n - 1) / BLOCK_SIZE + 1) - 1) / TILE_WIDTH + 1, (((n - 1) / BLOCK_SIZE + 1) - 1) / TILE_WIDTH + 1, 1);

	// Create and start timer
	printf("Computing result using CUDA Kernel...\n");

	// 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);
	}

	// Execute the kernel
	matrixMulCUDA<<<grid, threads>>>(d_C, d_A, d_B, n);

	error = cudaGetLastError();
	if (error != cudaSuccess)
	{
		fprintf(stderr, "Failed to launch kernel!\n", cudaGetErrorString(error));
		exit(EXIT_FAILURE);
	}

	// 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);

	printf("Elapsed time in msec = %f\n", msecTotal);

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

	// Copy result from device to host
	error = cudaMemcpy(h_C, d_C, mem_size_C, cudaMemcpyDeviceToHost);

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

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

	return EXIT_SUCCESS;
}

/**
* Program main
*/
int main(int argc, char **argv)
{
	printf("[Matrix Multiply Using CUDA] - Starting...\n");

	// By default, we use device 0
	int devID = 0;
	cudaSetDevice(devID);

	cudaError_t error;
	cudaDeviceProp deviceProp;
	error = cudaGetDevice(&devID);

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

	error = cudaGetDeviceProperties(&deviceProp, devID);

	if (deviceProp.computeMode == cudaComputeModeProhibited)
	{
		fprintf(stderr, "Error: device is running in <Compute Mode Prohibited>, no threads can use ::cudaSetDevice().\n");
		exit(EXIT_SUCCESS);
	}

	if (error != cudaSuccess)
	{
		printf("cudaGetDeviceProperties returned error %s (code %d), line(%d)\n", cudaGetErrorString(error), error, __LINE__);
	}
	else
	{
		printf("GPU Device %d: \"%s\" with compute capability %d.%d\n\n", devID, deviceProp.name, deviceProp.major, deviceProp.minor);
	}

	// Size of square matrices
	size_t n = N;
	// printf("[-] N = ");
	// scanf("%u", &n);

	printf("MatrixA(%d,%d), MatrixB(%d,%d)\n", n, n, n, n);

	int matrix_result = matrixMultiply(argc, argv, n);

	exit(matrix_result);
}