Create matScale.cu

matScale.cu

@@ -0,0 +1,75 @@
+#include <stdio.h>
+#include <stdlib.h>
+
+// scale kernel
+__global__ void matScale(float *d_A, float *d_B, float scale, int N, int M) {
+	int row = blockIdx.y * blockDim.y + threadIdx.y;
+	int col = blockIdx.x * blockDim.x + threadIdx.x;
+
+	// scale matrix elements
+	if (row < N && col < M) {
+		d_B[row * M + col] = d_A[row * M + col] / scale;
+	}
+}
+
+int main() {
+  	// var declaration
+	int N = 3;
+	int M = 3;
+	float scale = 2.0f;
+	float *A, *B;
+	float *d_A, *d_B;
+
+	// allocate host memory
+	A = (float *)malloc(N * M * sizeof(float));
+	B = (float *)malloc(N * M * sizeof(float));
+
+	// allocate device memory
+	cudaMalloc(&d_A, N * M * sizeof(float));
+	cudaMalloc(&d_B, N * M * sizeof(float));
+
+	// initialize data
+	for (int i = 0; i < N * M; ++i) {
+		A[i] = i - 3;
+		B[i] = i;
+	}
+
+	// copy host data to device
+	cudaMemcpy(d_A, A, N * M * sizeof(float), cudaMemcpyHostToDevice);
+	cudaMemcpy(d_B, B, N * M * sizeof(float), cudaMemcpyHostToDevice);
+
+	// launch kernel instance
+	dim3 blockDim(16, 16);
+	dim3 gridDim((M + blockDim.x - 1)/blockDim.x, (N + blockDim.y - 1)/blockDim.y);
+	matScale<<<gridDim, blockDim>>>(d_A, d_B, scale, N, M);
+
+	// copy result back to host
+	cudaMemcpy(A, d_A, N * M * sizeof(float), cudaMemcpyDeviceToHost);
+	cudaMemcpy(B, d_B, N * M * sizeof(float), cudaMemcpyDeviceToHost);
+
+  	// display results
+	printf("Matrix A: \n");
+	printf("----------\n");
+	for (int i = 0; i < N; ++i) {
+		for (int j = 0; j < M; ++j) {
+			printf("A: %f ", A[i * M + j]);
+		}
+		printf("\n");
+	}
+
+	printf("----------\n");
+	printf("Matrix B: \n");
+	printf("----------\n");
+	for (int i = 0; i < N; ++i) {
+		for (int j = 0; j < M; ++j) {
+			printf("B: %f ", B[i * M + j]);
+		}
+		printf("\n");
+	}
+
+	// clean up data
+	free(A); free(B);
+	cudaFree(d_A); cudaFree(d_B);
+
+	return 0;
+}