CUDA programmatio - asyncAPI discendi record

CUDA programmatio - asyncAPI discendi

2024-07-12

asyncAPI

1. The code
2. Analysis magnarum partium

1. The code

#include <stdio.h>

// includes CUDA Runtime
#include <cuda_runtime.h>
#include <cuda_profiler_api.h>

// includes, project
#include <helper_cuda.h>
#include <helper_functions.h>  // helper utility functions

__global__ void increment_kernel(int *g_data, int inc_value) {
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  g_data[idx] = g_data[idx] + inc_value;
}

bool correct_output(int *data, const int n, const int x) {
  for (int i = 0; i < n; i++)
    if (data[i] != x) {
      printf("Error! data[%d] = %d, ref = %dn", i, data[i], x);
      return false;
    }

  return true;
}

int main(int argc, char *argv[]) {
  int devID;
  cudaDeviceProp deviceProps;

  printf("[%s] - Starting...n", argv[0]);

  // This will pick the best possible CUDA capable device
  devID = findCudaDevice(argc, (const char **)argv);

  // get device name
  checkCudaErrors(cudaGetDeviceProperties(&deviceProps, devID));
  printf("CUDA device [%s]n", deviceProps.name);

  int n = 16 * 1024 * 1024;
  int nbytes = n * sizeof(int);
  int value = 26;

  // allocate host memory
  int *a = 0;
  checkCudaErrors(cudaMallocHost((void **)&a, nbytes));
  memset(a, 0, nbytes);

  // allocate device memory
  int *d_a = 0;
  checkCudaErrors(cudaMalloc((void **)&d_a, nbytes));
  checkCudaErrors(cudaMemset(d_a, 255, nbytes));

  // set kernel launch configuration
  dim3 threads = dim3(512, 1);
  dim3 blocks = dim3(n / threads.x, 1);

  // create cuda event handles
  cudaEvent_t start, stop;
  checkCudaErrors(cudaEventCreate(&start));
  checkCudaErrors(cudaEventCreate(&stop));

  StopWatchInterface *timer = NULL;
  sdkCreateTimer(&timer);
  sdkResetTimer(&timer);

  checkCudaErrors(cudaDeviceSynchronize());
  float gpu_time = 0.0f;

  // asynchronously issue work to the GPU (all to stream 0)
  checkCudaErrors(cudaProfilerStart());
  sdkStartTimer(&timer);
  cudaEventRecord(start, 0);
  cudaMemcpyAsync(d_a, a, nbytes, cudaMemcpyHostToDevice, 0);
  increment_kernel<<<blocks, threads, 0, 0>>>(d_a, value);
  cudaMemcpyAsync(a, d_a, nbytes, cudaMemcpyDeviceToHost, 0);
  cudaEventRecord(stop, 0);
  sdkStopTimer(&timer);
  checkCudaErrors(cudaProfilerStop());

  // have CPU do some work while waiting for stage 1 to finish
  unsigned long int counter = 0;

  while (cudaEventQuery(stop) == cudaErrorNotReady) {
    counter++;
  }

  checkCudaErrors(cudaEventElapsedTime(&gpu_time, start, stop));

  // print the cpu and gpu times
  printf("time spent executing by the GPU: %.2fn", gpu_time);
  printf("time spent by CPU in CUDA calls: %.2fn", sdkGetTimerValue(&timer));
  printf("CPU executed %lu iterations while waiting for GPU to finishn",
         counter);

  // check the output for correctness
  bool bFinalResults = correct_output(a, n, value);

  // release resources
  checkCudaErrors(cudaEventDestroy(start));
  checkCudaErrors(cudaEventDestroy(stop));
  checkCudaErrors(cudaFreeHost(a));
  checkCudaErrors(cudaFree(d_a));

  exit(bFinalResults ? EXIT_SUCCESS : EXIT_FAILURE);
}
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2. Analysis magnarum partium

armoruminitialization ": Munus findCudaDevice usus est eligere optimum CUDA fabrica ac fabrica ID redire.

devID = findCudaDevice(argc, (const char **)argv);

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Proprietates machinae posside: Munus CudaGetDeviceProperties obtinet proprietates certae machinae, quae nomen et alia informationes includunt.

checkCudaErrors(cudaGetDeviceProperties(&deviceProps, devID));

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Memoriae destinatio: Usus cudaMallocHost ad memoriam paginam clausam in CPU pervia collocare, et cudaMalloc ad memoriam de fabrica collocare.

int *a = 0;
checkCudaErrors(cudaMallocHost((void **)&a, nbytes));

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Stipes stamina et craticula constitue: Hic clausus stamina ad 512 stamina apponitur, et magnitudo craticulae dynamice calculata fundatur in magnitudine data.

dim3 threads = dim3(512, 1);
dim3 blocks = dim3(n / threads.x, 1);

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CUDA eventus crea et timentes: CUDA eventus recordari solent, et timetores adhibentur ut tempus executionis CPU metirentur.

cudaEvent_t start, stop;
checkCudaErrors(cudaEventCreate(&start));
checkCudaErrors(cudaEventCreate(&stop));

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CUDA amnis processus: usus cudaMemcpyAsync pro asynchrono exemplum memoriae <<<blocks, threads> >> Syntaxis incipit simul CUDA nuclei functionis increment_kernel exsecutus.

cudaMemcpyAsync(d_a, a, nbytes, cudaMemcpyHostToDevice, 0);
increment_kernel<<<blocks, threads, 0, 0>>>(d_a, value);
cudaMemcpyAsync(a, d_a, nbytes, cudaMemcpyDeviceToHost, 0);

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Timing et exspectans: cudaEventRecord gestarum monumenta et tempus exsecutionis GPU computare adhibetur. Exspecta GPU operationem ut perficiat per cudaEventQuery(stop).

cudaEventRecord(start, 0);
// ...
cudaEventRecord(stop, 0);

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Proventus verificationis: Munus correct_output utere ad verificandum rectitudinem proventus calculi GPU.

bool bFinalResults = correct_output(a, n, value);

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Resource Dimitte: datam memoriam dimitte et rerum CUDA.

checkCudaErrors(cudaEventDestroy(start));
checkCudaErrors(cudaEventDestroy(stop));
checkCudaErrors(cudaFreeHost(a));
checkCudaErrors(cudaFree(d_a));

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CUDA nuclei munus increment_kernel:

Simplex haec functionis CUDA kernel adhibetur, ut unumquodque elementum in acie crescat per valorem determinatum inc_value. blockIdx.x et threadIdx.x adhibentur calculare index globalis idx singulorum filorum ac deinde operationem additionis perficiendi.

__global__ void increment_kernel(int *g_data, int inc_value) {
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  g_data[idx] = g_data[idx] + inc_value;
}

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Alia munera auxilia
checkCudaErrorsreprehendo CUDASit an error munere vocent.
sdkCreateTimer et sdkResetTimer: usus creare et reset timers.
sdkStartTimer et sdkStopTimer: incipere et desinere solebant timers ac tempus exsecutionis CPU recordare.

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