单机多进程,每个进程多张卡 mpi nccl 程序设计检验

做了部分注释，比较乱

本示例结构：

1，源代码

#include <stdlib.h>
#include <stdio.h>
#include "cuda_runtime.h"
#include "nccl.h"
#include "mpi.h"
#include <unistd.h>
#include <stdint.h>
#include <sys/time.h>


#define MPI_CHECK(cmd) do {                          \
  int e = cmd;                                      \
  if( e != MPI_SUCCESS ) {                          \
    printf("Failed: MPI error %s:%d '%d'\n",        \
        __FILE__,__LINE__, e);   \
    exit(EXIT_FAILURE);                             \
  }                                                 \
} while(0)

#define CUDA_CHECK(cmd) do {                         \
  cudaError_t e = cmd;                              \
  if( e != cudaSuccess ) {                          \
    printf("Failed: Cuda error %s:%d '%s'\n",             \
        __FILE__,__LINE__,cudaGetErrorString(e));   \
    exit(EXIT_FAILURE);                             \
  }                                                 \
} while(0)

#define NCCL_CHECK(cmd) do {                         \
  ncclResult_t r = cmd;                             \
  if (r!= ncclSuccess) {                            \
    printf("Failed, NCCL error %s:%d '%s'\n",             \
        __FILE__,__LINE__,ncclGetErrorString(r));   \
    exit(EXIT_FAILURE);                             \
  }                                                 \
} while(0)

static uint64_t getHostHash(const char* string) {
  // Based on DJB2a, result = result * 33 ^ char
  uint64_t result = 5381;
  for (int c = 0; string[c] != '\0'; c++){
    result = ((result << 5) + result) ^ string[c];
  }
  return result;
}

static void getHostName(char* hostname, int maxlen) {
  gethostname(hostname, maxlen);
  for (int i=0; i< maxlen; i++) {
    if (hostname[i] == '.') {
        hostname[i] = '\0';
        return;
    }
  }
}


void print_vector(float* A, int n)
{
  for(int i=0; i<n; i++)
    printf("%.2f ", A[i]);
}

void init_dev_vectors(float* A_d, float* B_d, int n, int rank, long long seed, int dev_idx)
{
  float * A = (float*)malloc(n*sizeof(float));
  float * B = (float*)malloc(n*sizeof(float));
  //float * M = (float*)malloc(n*sizeof(float));//max[i] = max(A[i], B[i]);
  //float * S = (float*)malloc(n*sizeof(float));//sum[i] = sum(A[i], B[i]);
  srand(seed);

  for(int i=0; i<n; i++)
  {
    A[i] = (rand()%100)/100.0f;
    B[i] = (rand()%100)/100.0f;
  }

  printf("\nrank = %d, gpuid = %d, sendbuff =\n", rank, dev_idx);
  print_vector(A, n);
  printf("\n\n");
//  printf("\nrank = %d, Sum =\n", rank);  print_vector(S, n);

  cudaMemcpy(A_d, A, n*sizeof(float), cudaMemcpyHostToDevice);
  cudaMemcpy(B_d, B, n*sizeof(float), cudaMemcpyHostToDevice);

  free(A);
  free(B);
}

void get_seed(long long &seed)
{
  struct timeval tv;
  gettimeofday(&tv, NULL);
  seed = (long long)tv.tv_sec * 1000*1000 + tv.tv_usec;//only second and usecond;
  //printf("useconds:%lld\n", seed);
}

void fetch_dev_vector(float* A_d, int n, int rank, int dev_id)
{
  float* A = (float*)malloc(n*sizeof(float));
  cudaMemcpy(A, A_d, n*sizeof(float), cudaMemcpyDeviceToHost);
  printf("rank = %d,gpuid =%d recvbuff =\n", dev_id, rank);
  print_vector(A, n);
  printf("\n\n");

  free(A);
}

int main(int argc, char* argv[])
{
  int size = 16;//32*1024*1024;

  int myRank, nRanks, localRank = 0;

  //initializing MPI
  MPI_CHECK(MPI_Init(&argc, &argv));
  MPI_CHECK(MPI_Comm_rank(MPI_COMM_WORLD, &myRank));
  MPI_CHECK(MPI_Comm_size(MPI_COMM_WORLD, &nRanks));

  //calculating localRank which is used in selecting a GPU
  uint64_t hostHashs[nRanks];
  char hostname[1024];
  getHostName(hostname, 1024);
  hostHashs[myRank] = getHostHash(hostname);
  MPI_CHECK(MPI_Allgather(MPI_IN_PLACE, 0, MPI_DATATYPE_NULL, hostHashs, sizeof(uint64_t), MPI_BYTE, MPI_COMM_WORLD));
  for (int p=0; p<nRanks; p++) {
     if (p == myRank) break;
     if (hostHashs[p] == hostHashs[myRank]) localRank++;
  }

  //each process is using two GPUs
  int nDev = 2;

  float** sendbuff = (float**)malloc(nDev * sizeof(float*));
  float** recvbuff = (float**)malloc(nDev * sizeof(float*));
  cudaStream_t* s = (cudaStream_t*)malloc(sizeof(cudaStream_t)*nDev);

  //picking GPUs based on localRank
  for (int i = 0; i < nDev; ++i) {
    CUDA_CHECK(cudaSetDevice(localRank*nDev + i));
    CUDA_CHECK(cudaMalloc(sendbuff + i, size * sizeof(float)));
    CUDA_CHECK(cudaMalloc(recvbuff + i, size * sizeof(float)));
    CUDA_CHECK(cudaMemset(sendbuff[i], 1, size * sizeof(float)));
    CUDA_CHECK(cudaMemset(recvbuff[i], 0, size * sizeof(float)));
    CUDA_CHECK(cudaStreamCreate(s+i));

    long long seed = 0;
    get_seed(seed);
//void init_dev_vectors(float* A_d, float* B_d, int n, int rank, long long seed, int dev_idx)
    init_dev_vectors(sendbuff[i], recvbuff[i], size, myRank, seed, i);
  }

  ncclUniqueId id;
  ncclComm_t comms[nDev];

  //generating NCCL unique ID at one process and broadcasting it to all
  if (myRank == 0) ncclGetUniqueId(&id);
  MPI_CHECK(MPI_Bcast((void *)&id, sizeof(id), MPI_BYTE, 0, MPI_COMM_WORLD));

  //initializing NCCL, group API is required around ncclCommInitRank as it is
  //called across multiple GPUs in each thread/process
  NCCL_CHECK(ncclGroupStart());
  for (int i=0; i<nDev; i++) {
     CUDA_CHECK(cudaSetDevice(localRank*nDev + i));
     NCCL_CHECK(ncclCommInitRank(comms+i, nRanks*nDev, id, myRank*nDev + i));
  }
  NCCL_CHECK(ncclGroupEnd());

  //calling NCCL communication API. Group API is required when using
  //multiple devices per thread/process
  NCCL_CHECK(ncclGroupStart());
  for (int i=0; i<nDev; i++)
     NCCL_CHECK(ncclAllReduce((const void*)sendbuff[i], (void*)recvbuff[i], size, ncclFloat, ncclSum,
           comms[i], s[i]));
  NCCL_CHECK(ncclGroupEnd());

  //synchronizing on CUDA stream to complete NCCL communication
  for (int i=0; i<nDev; i++)
      CUDA_CHECK(cudaStreamSynchronize(s[i]));


  for(int i=0; i<nDev; i++)
    fetch_dev_vector(recvbuff[i], size, myRank, i);

  //freeing device memory
  for (int i=0; i<nDev; i++) {
     CUDA_CHECK(cudaFree(sendbuff[i]));
     CUDA_CHECK(cudaFree(recvbuff[i]));
  }

  //finalizing NCCL
  for (int i=0; i<nDev; i++) {
     ncclCommDestroy(comms[i]);
  }

  //finalizing MPI
  MPI_CHECK(MPI_Finalize());

  printf("[MPI Rank %d] Success \n", myRank);
  return 0;
}

2，构建

2.1 Makefile

LD_FLAGS := -lnccl -L/usr/local/cuda/lib64 -lcudart -I/usr/local/cuda/include

MPI_FLAGS := -I /home/hipper/ex_openmpi/local/include -L /home/hipper/ex_openmpi/local/lib -lmpi 
#-lmpi_cxx

EXE := ngpuPerProcess_mxnGPU_mProcess_oneServer
# multiProcess_multiDevice_oneServer_allreduce
# singleProcess_multiDevice_oneServer_allreduce
all: $(EXE)

ngpuPerProcess_mxnGPU_mProcess_oneServer: ngpuPerProcess_mxnGPU_mProcess_oneServer.cpp
	g++ -g $< -o $@ $(LD_FLAGS) $(MPI_FLAGS)

hello_comm: hello_comm.cpp
	g++ -g $< -o $@ $(LD_FLAGS)

.PHONY: clean
clean:
	-rm $(EXE)

2.2 构建

$ make

3，运行

$ ../../ex_openmpi/local/bin/mpirun -np 2 ./ngpuPerProcess_mxnGPU_mProcess_oneServer

4，效果

数学效果跟前文相同