Add a basic saxpy example

.gitignore

@@ -4,3 +4,4 @@
 
 # binaries
 get_gpu_info
+saxpy

Makefile

 get_gpu_info: get_gpu_info.cu
-	nvcc get_gpu_info.cu -o get_gpu_info	
+	nvcc get_gpu_info.cu -o get_gpu_info
+
+saxpy: saxpy.cu
+	nvcc saxpy.cu -o saxpy	

saxpy.cu

+// This code performs a single precision a*X plus Y operation on the GPU.
+// Adapated from https://developer.nvidia.com/blog/easy-introduction-cuda-c-and-c/
+
+#include <stdio.h>
+
+// All CUDA kernels return void. To get data back to the CPU you have to copy it explicitly.
+__global__
+void saxpy(int n, float a, float *x, float *y) {
+    int const i = blockIdx.x * blockDim.x + threadIdx.x;
+    if (i < n) {
+        y[i] = a * x[i] + y[i];
+    }
+}
+
+int main() {
+    // We'll put 2^20 numbers in each vector.
+    int N = 1048576;
+
+    // Allocate host (CPU) memory.
+    float *h_x, *h_y;
+    h_x = (float*)malloc(N * sizeof(float));
+    h_y = (float*)malloc(N * sizeof(float));
+
+    // Allocate device (GPU) memory.
+    float *d_x, *d_y;
+    cudaMalloc(&d_x, N * sizeof(float));
+    cudaMalloc(&d_y, N * sizeof(float));
+
+    // Initialize data.
+    for (int i = 0; i < N; ++i) {
+        h_x[i] = 1.0f;
+        h_y[i] = 2.0f;
+    }
+
+    // Copy data to the GPU.
+    cudaMemcpy(d_x, h_x, N * sizeof(float), cudaMemcpyHostToDevice);
+    cudaMemcpy(d_y, h_y, N * sizeof(float), cudaMemcpyHostToDevice);
+
+    // Perform SAXPY on the data.
+    saxpy<<<(N+255)/256, 256>>>(N, 2.0f, d_x, d_y);
+
+    // Copy the result back to the host.
+    cudaMemcpy(h_y, d_y, N * sizeof(float), cudaMemcpyDeviceToHost);
+
+    float maxError = 0.0f;
+    for (int i = 0; i < N; i++)
+    maxError = max(maxError, abs(h_y[i]-4.0f));
+    printf("Max error: %f\n", maxError);
+
+    cudaFree(d_x);
+    cudaFree(d_y);
+    free(h_x);
+    free(h_y);
+
+    return 0;
+}