rocm-systems/projects/hip/tests/src/runtimeApi/stream/hipStreamACb_MStrm_Mgpu.cpp

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// Testcase Description: Streams are launched in individual GPUs with different
// kernel. Verify that all the kernels queued are executed before the callback.

/* HIT_START
 * BUILD: %t %s ../../test_common.cpp NVCC_OPTIONS -std=c++11 EXCLUDE_HIP_PLATFORM nvidia
 * TEST: %t
 * HIT_END
 */

#include <stdio.h>
#include <unistd.h>
#include <thread>
#include <chrono>
#include "hip/hip_runtime.h"
#include "test_common.h"

#ifdef __HIP_PLATFORM_HCC__
#define HIPRT_CB
#endif


size_t N_ELMTS = 4096;

// Data structure for holding and validating data
struct gpu_data {
  int *int_ptr = NULL;
  int gpu;
  int acknowledge;
};

enum {
  SUCCESS = 0,
  KERNEL_EXECUTION_MISMATCH,
  KERNEL_COMPUTATION_MISMATCH
};

__global__ void Add_Data(int* A_d, size_t N_ELMTS) {
  size_t offset = (blockIdx.x * blockDim.x + threadIdx.x);
  size_t stride = blockDim.x * gridDim.x;
  for (size_t i = offset; i < N_ELMTS; i += stride) {
    // Increment the value of A_d[i] by 1
    A_d[i] = A_d[i] + 1;
  }
}

// below kernel is just to load the gpu with multiple jobs
__global__ void Square_plus_one(int* A_d, int* C_d, size_t N_ELMTS) {
  size_t offset = (blockIdx.x * blockDim.x + threadIdx.x);
  size_t stride = blockDim.x * gridDim.x;
  for (size_t i = offset; i < N_ELMTS; i += stride) {
    C_d[i] = A_d[i]*A_d[i] + 1;
  }
}

static void HIPRT_CB Stream_Callback(hipStream_t stream, hipError_t status,
                                     void* userData) {
  gpu_data *ptr = reinterpret_cast<gpu_data *>(userData);

  // int_ptr in the passed userData will contain the data copied from device to
  // host.  Expected data in this field is the gpu ordinal.
  if (*((*ptr).int_ptr) != (*ptr).gpu + 1) {
    (*ptr).acknowledge = 100;   // Assign unexpected value to indicate fail
  } else {
    (*ptr).acknowledge = (*ptr).gpu;  // Assign the gpu ordinal received
  }
}

void launch_gpu(int gpu_ordinal) {
  HIPCHECK(hipSetDevice(gpu_ordinal));
  int *A_d, *A_h, *C_h, *C_d;
  size_t Nbytes = N_ELMTS * sizeof(int), Data_mismatch = 0;
  bool cb = false;
  A_h = (int *)malloc(Nbytes);
  HIPCHECK(A_h == 0 ? hipErrorOutOfMemory : hipSuccess);
  C_h = (int *)malloc(Nbytes);
  HIPCHECK(C_h == 0 ? hipErrorOutOfMemory : hipSuccess);

  // Fill with 0
  for (size_t i = 0; i < N_ELMTS; i++) {
    A_h[i] = 0;
  }

  // setting gpu value in the struct object
  gpu_data *ptr = new gpu_data;
  ptr->int_ptr = C_h;
  ptr->gpu = gpu_ordinal;
  ptr->acknowledge = 100;

  HIPCHECK(hipMalloc(&A_d, Nbytes));
  HIPCHECK(hipMalloc(&C_d, Nbytes));

  hipStream_t mystream;
  HIPCHECK(hipStreamCreateWithFlags(&mystream, hipStreamNonBlocking));

  HIPCHECK(hipMemcpyAsync(A_d, A_h, Nbytes, hipMemcpyHostToDevice, mystream));

  const unsigned threadsPerBlock = 256;
  const unsigned blocks = (N_ELMTS + 255)/threadsPerBlock;

  // A_d is initialized to 0.  Add_Data kernel does A_d = A_d + 1
  // The Add_data kernel is called 1 time for gpu0, 2 times for gpu1 etc.
  // At the end of the loop, A_d should have the gpu_ordinal number
  for (int i = 0; i < gpu_ordinal + 1; i++) {
    hipLaunchKernelGGL(Add_Data, dim3(blocks), dim3(threadsPerBlock), 0,
                       mystream, A_d, N_ELMTS);
    hipLaunchKernelGGL(Square_plus_one, 1, 1, 0, mystream, A_d, C_d, N_ELMTS);
  }
  HIPCHECK(hipMemcpyAsync(C_h, A_d, Nbytes, hipMemcpyDeviceToHost, mystream));

  // Pass the ptr as user data which contains the gpu_ordinal, default value
  // for ack and the data that is copied to host
  HIPCHECK(hipStreamAddCallback(mystream, Stream_Callback,
                                reinterpret_cast<void *>(ptr), 0));
  HIPCHECK(hipStreamSynchronize(mystream));

  HIPCHECK(hipFree(A_d));
  HIPCHECK(hipFree(C_d));
  HIPCHECK(hipStreamDestroy(mystream));

  int result = SUCCESS;
  if (C_h[0] != gpu_ordinal + 1) {
    result = KERNEL_EXECUTION_MISMATCH;
  }

  if (ptr->gpu != ptr->acknowledge) {
    result = KERNEL_COMPUTATION_MISMATCH;
  }

  free(A_h);
  free(C_h);
  free(ptr);

  if (result == KERNEL_EXECUTION_MISMATCH) {
    failed("Number of kernels expected to be executed does not match");
  } else if (result == KERNEL_COMPUTATION_MISMATCH) {
    failed("Mismatch found in the result of the computation!");
  }
}


int main() {
  int gpu_cnt = 0;

  HIPCHECK(hipGetDeviceCount(&gpu_cnt));
  if (gpu_cnt < 2) {
    printf("Minimum of 2 gpus are needed for this test, skipping the test\n");
    passed();
  }

  std::thread T[gpu_cnt];

  // Launching threads for each GPU
  for (int i = 0; i < gpu_cnt; i++) {
    T[i] = std::thread(launch_gpu, i);
  }

  for (int i=0; i < gpu_cnt; i++) {
    T[i].join();
  }
  passed();
}