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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,163 @@ | ||
| #include <iostream> | ||
| #include <math.h> | ||
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| struct Operation { | ||
| float *x; | ||
| float *y; | ||
| int n; | ||
| }; | ||
|
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| // CUDA kernel to add elements of two arrays | ||
| __global__ void add(Operation *op) { | ||
| int index = blockIdx.x * blockDim.x + threadIdx.x; | ||
| int stride = blockDim.x * gridDim.x; | ||
|
|
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| printf("The X is: %x\n", op->x[0]); | ||
| printf("The Y is: %x\n", op->y[0]); | ||
| for (int i = index; i < op->n; i += stride) | ||
| { | ||
| op->y[i] = op->x[i] + op->y[i]; | ||
| printf("The value is: %f\n", op->y[i]); | ||
| } | ||
| } | ||
|
|
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| int main(void) { | ||
| Operation host_op; // Host structure | ||
| Operation *device_op; // Device structure | ||
|
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| // Initialize array size | ||
| host_op.n = 100; | ||
|
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| // Allocate memory for device operation struct | ||
| cudaMalloc(&device_op, sizeof(Operation)); | ||
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| // Allocate memory for x and y arrays on the device | ||
| cudaMalloc(&host_op.x, host_op.n * sizeof(float)); | ||
| cudaMalloc(&host_op.y, host_op.n * sizeof(float)); | ||
|
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| // Initialize x and y arrays on the host | ||
| float *host_x = new float[host_op.n]; | ||
| float *host_y = new float[host_op.n]; | ||
| for (int i = 0; i < host_op.n; i++) { | ||
| host_x[i] = 1.0f; | ||
| host_y[i] = 2.0f; | ||
| } | ||
|
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| // Copy x and y arrays from host to device | ||
| cudaMemcpy(host_op.x, host_x, host_op.n * sizeof(float), cudaMemcpyHostToDevice); | ||
| cudaMemcpy(host_op.y, host_y, host_op.n * sizeof(float), cudaMemcpyHostToDevice); | ||
|
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| // Copy host operation struct to device | ||
| cudaMemcpy(device_op, &host_op, sizeof(Operation), cudaMemcpyHostToDevice); | ||
|
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| // Launch kernel | ||
| int blockSize = 256; | ||
| int numBlocks = (host_op.n + blockSize - 1) / blockSize; | ||
| add<<<numBlocks, blockSize>>>(device_op); | ||
|
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| // Wait for GPU to finish before accessing results | ||
| cudaDeviceSynchronize(); | ||
|
|
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| // Copy results from device to host | ||
| cudaMemcpy(host_y, host_op.y, host_op.n * sizeof(float), cudaMemcpyDeviceToHost); | ||
|
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| // Log results for debugging | ||
| std::cout << "Results (y = x + y):" << std::endl; | ||
| for (int i = 0; i < host_op.n; i++) { | ||
| std::cout << "y[" << i << "] = " << host_y[i] << " (expected: 3.0)" << std::endl; | ||
| } | ||
|
|
||
| // Check for errors (all values should be 3.0f) | ||
| float maxError = 0.0f; | ||
| for (int i = 0; i < host_op.n; i++) { | ||
| maxError = fmax(maxError, fabs(host_y[i] - 3.0f)); | ||
| } | ||
|
|
||
| // Free device memory | ||
| cudaFree(host_op.x); | ||
| cudaFree(host_op.y); | ||
| cudaFree(device_op); | ||
|
|
||
| // Free host memory | ||
| delete[] host_x; | ||
| delete[] host_y; | ||
|
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| return 0; | ||
| } | ||
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|
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| // // ******UNIFIED MEMORY EXAMPLE BELOW******* | ||
|
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|
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| // #include <iostream> | ||
| // #include <math.h> | ||
|
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| // struct Operation { | ||
| // float *x; | ||
| // float *y; | ||
| // int n; | ||
| // }; | ||
|
|
||
| // // CUDA kernel to add elements of two arrays | ||
| // __global__ void add(Operation *op) { | ||
| // int index = blockIdx.x * blockDim.x + threadIdx.x; | ||
| // int stride = blockDim.x * gridDim.x; | ||
|
|
||
| // printf("The X is: %x\n", op->x[0]); | ||
| // printf("The Y is: %x\n", op->y[0]); | ||
| // for (int i = index; i < op->n; i += stride) | ||
| // { | ||
| // op->y[i] = op->x[i] + op->y[i]; | ||
| // printf("The value is: %f\n", op->y[i]); | ||
| // } | ||
| // } | ||
|
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| // int main(void) { | ||
| // Operation *op; | ||
|
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| // // Allocate Unified Memory -- accessible from CPU or GPU | ||
| // cudaMallocManaged(&op, sizeof(Operation)); | ||
| // op->n = 100; | ||
|
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| // cudaMallocManaged(&op->x, op->n * sizeof(float)); | ||
| // cudaMallocManaged(&op->y, op->n * sizeof(float)); | ||
|
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| // // initialize x and y arrays on the host | ||
| // for (int i = 0; i < op->n; i++) { | ||
| // op->x[i] = 1.0f; | ||
| // op->y[i] = 2.0f; | ||
| // } | ||
|
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| // // Launch kernel on n elements on the GPU | ||
| // int blockSize = 256; | ||
| // int numBlocks = (op->n + blockSize - 1) / blockSize; | ||
|
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| // std::cout << "numBlocks: " << numBlocks << std::endl; | ||
| // std::cout << "N: " << op->n << std::endl; | ||
|
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| // add<<<numBlocks, blockSize>>>(op); | ||
|
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| // // Wait for GPU to finish before accessing on host | ||
| // cudaDeviceSynchronize(); | ||
|
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| // // Log results for debugging | ||
| // std::cout << "Results (y = x + y):" << std::endl; | ||
| // for (int i = 0; i < op->n; i++) { | ||
| // std::cout << "y[" << i << "] = " << op->y[i] << " (expected: 3.0)" << std::endl; | ||
| // } | ||
|
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| // // Check for errors (all values should be 3.0f) | ||
| // float maxError = 0.0f; | ||
| // for (int i = 0; i < op->n; i++) { | ||
| // maxError = fmax(maxError, fabs(op->y[i] - 3.0f)); | ||
| // } | ||
| // std::cout << "Max error: " << maxError << std::endl; | ||
|
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| // // Free memory | ||
| // cudaFree(op->x); | ||
| // cudaFree(op->y); | ||
| // cudaFree(op); | ||
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| // return 0; | ||
| // } |