文件列表:

atomic_mulB_mulT.cu (908, 2023-03-22)
cuda_01.cu (1541, 2023-03-22)
mem_in_same_B.cu (998, 2023-03-22)
mulB_mulT.cu (1248, 2023-03-22)
mulB_sinT.cu (1028, 2023-03-22)
mulT_sinB.cu (1035, 2023-03-22)

# CUDA Programming ## Install CUDA and compile * [Install reference](https://shuuutin-cg.medium.com/ubuntu18-04%E5%AE%89%E8%A3%9Dcuda%E4%BB%A5%E5%8F%8A%E6%94%B9%E8%AE%8Acuda%E7%89%88%E6%9C%AC-b8ac917f880f ) * Compile Nvcc ```shell # Example: after we write a test.cu we can use following command to compile and run $ nvcc test.cu $ ./a.out ``` ## Hello World ```c #include // Run on GPU(when we see __global__ it means it run on GPU) // __global__ can be called by CPU, but __device__ only can be called by __global__ or __device__ __global__ void kernel() { printf("Hello World!"); } // Run on CPU int main() { // <<>> // * kernel function has variable_1 thread blocks // * In each thread blocks has variable_2 threads kernel<<<1, 1>>>(); return 0; } ``` > * Size: `Grid`(huge) > `Block` > `Thread`(small) > * Execution unit is `Block`. ## Data transmission between CPU and GPU ```c #include __global__ void add(int a, int b, int *c) { *c = a + b; } int main() { int cpu_c; int *gpu_c; // melloc on GPU // cudaError_t cudaMalloc (void **devPtr, size_t size) cudaMalloc((void**)&gpu_c, sizeof(int)); // calculate on GPU add<<<1, 1>>>(1, 2, gpu_c); // transmision the data from GPU to CPU // cudaError_t cudaMemcpy (void *dst, const void *src, size_t count, cudaMemcpyKind kind) cudaMemcpy(&cpu_c, gpu_c, sizeof(int), cudaMemcpyDeviceToHost); // cudaError_t cudaFree (void* devPtr) cudaFree(gpu_c); return 0; } ``` > Flow: cudaMelloc() -> cudaMemcpy() -> cudaFree()

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