MPI+OpenACC编程中的GPU间通信
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【中文标题】MPI+OpenACC编程中的GPU间通信【英文标题】：Inter GPU communication in MPI+OpenACC programming 【发布时间】：2022-01-10 18:28:00 【问题描述】：我正在尝试学习如何使用以下玩具代码执行 GPU 间数据通信。该程序的任务是将 gpu-0 中的数组“a”数据发送到 gpu-1 的内存中。我采取了以下根来这样做，这涉及四个步骤：
在 gpu0 上初始化数组 'a' 后，
step1：将数据从gpu0发送到cpu0（使用!acc update self()子句）
step2：从cpu0向cpu1发送数据（使用MPI_SEND()）
step3：从cpu0接收数据到cpu1（使用MPI_RECV()）
step4：更新gpu1设备内存（使用!$acc update device()子句）
这工作得很好，但这看起来像是一条很长的路，我认为有更好的方法来做到这一点。我尝试阅读以下帖子中建议的!$acc host_data use_device 子句，但无法实施：
Getting started with OpenACC + MPI Fortran program
我想知道如何使用!$acc host_data use_device 以有效地执行下面显示的任务。
PROGRAM TOY_MPI_OpenACC
implicit none
include 'mpif.h'
integer :: rank, nprocs, ierr, i, dest_rank, tag, from
integer :: status(MPI_STATUS_SIZE)
integer, parameter :: N = 10000
double precision, dimension(N) :: a
call MPI_INIT(ierr)
call MPI_COMM_RANK(MPI_COMM_WORLD,rank,ierr)
call MPI_COMM_SIZE(MPI_COMM_WORLD,nprocs,ierr)
print*, 'Process ', rank, ' of', nprocs, ' is alive'
!$acc data create(a)
! initialize 'a' on gpu0 (not cpu0)
IF (rank == 0) THEN
!$acc parallel loop default(present)
DO i = 1,N
a(i) = 1
ENDDO
ENDIF
! step1: send data from gpu0 to cpu0
!$acc update self(a)
print*, 'a in rank', rank, ' before communication is ', a(N/2)
IF (rank == 0) THEN
! step2: send from cpu0
dest_rank = 1; tag = 1999
call MPI_SEND(a, N, MPI_DOUBLE_PRECISION, dest_rank, tag, MPI_COMM_WORLD, ierr)
ELSEIF (rank == 1) THEN
! step3: recieve into cpu1
from = MPI_ANY_SOURCE; tag = MPI_ANY_TAG;
call MPI_RECV(a, N, MPI_DOUBLE_PRECISION, from, tag, MPI_COMM_WORLD, status, ierr)
! step4: send data in to gpu1 from cpu1
!$acc update device(a)
ENDIF
call MPI_BARRIER(MPI_COMM_WORLD, ierr)
print*, 'a in rank', rank, ' after communication is ', a(N/2)
!$acc end data
call MPI_BARRIER(MPI_COMM_WORLD, ierr)
END
编译：mpif90 -acc -ta=tesla toycode.f90（来自 nvidia hpc-sdk 21.9 的 mpif90）
执行：mpirun -np 2 ./a.out
【问题讨论】：
【参考方案1】：这是一个例子。请注意，我还添加了一些样板代码来对设备分配进行本地节点排名。我也更喜欢使用非结构化数据区域，因为它们更适合更复杂的代码，但在这里它们在语义上等同于您在上面使用的结构化数据区域。我在 CUDA_AWARE_MPI 宏下保护了 host_data 结构，因为并非所有 MPI 都启用了 CUDA Aware 支持。对于这些，您需要在 MPI 调用之前/之后恢复在主机和设备之间复制数据。
% cat mpi_acc.F90
PROGRAM TOY_MPI_OpenACC
use mpi
#ifdef _OPENACC
use openacc
#endif
implicit none
integer :: rank, nprocs, ierr, i, dest_rank, tag, from
integer :: status(MPI_STATUS_SIZE)
integer, parameter :: N = 10000
double precision, dimension(N) :: a
#ifdef _OPENACC
integer :: dev, devNum, local_rank, local_comm
integer(acc_device_kind) :: devtype
#endif
call MPI_INIT(ierr)
call MPI_COMM_RANK(MPI_COMM_WORLD,rank,ierr)
call MPI_COMM_SIZE(MPI_COMM_WORLD,nprocs,ierr)
print*, 'Process ', rank, ' of', nprocs, ' is alive'
#ifdef _OPENACC
! set the MPI rank to device mapping
! 1) Get the local node's rank number
! 2) Get the number of devices on the node
! 3) Round-Robin assignment of rank to device
call MPI_Comm_split_type(MPI_COMM_WORLD, MPI_COMM_TYPE_SHARED, 0, &
MPI_INFO_NULL, local_comm,ierr)
call MPI_Comm_rank(local_comm, local_rank,ierr)
devtype = acc_get_device_type()
devNum = acc_get_num_devices(devtype)
dev = mod(local_rank,devNum)
call acc_set_device_num(dev, devtype)
print*, "Process ",rank," Using device ",dev
#endif
a = 0
!$acc enter data copyin(a)
! initialize 'a' on gpu0 (not cpu0)
IF (rank == 0) THEN
!$acc parallel loop default(present)
DO i = 1,N
a(i) = 1
ENDDO
!$acc update self(a)
ENDIF
! step1: send data from gpu0 to cpu0
print*, 'a in rank', rank, ' before communication is ', a(N/2)
IF (rank == 0) THEN
! step2: send from cpu0
dest_rank = 1; tag = 1999
#ifdef CUDA_AWARE_MPI
!$acc host_data use_device(a)
#endif
call MPI_SEND(a, N, MPI_DOUBLE_PRECISION, dest_rank, tag, MPI_COMM_WORLD, ierr)
#ifdef CUDA_AWARE_MPI
!$acc end host_data
#endif
ELSEIF (rank == 1) THEN
! step3: recieve into cpu1
from = MPI_ANY_SOURCE; tag = MPI_ANY_TAG;
#ifdef CUDA_AWARE_MPI
!$acc host_data use_device(a)
#endif
call MPI_RECV(a, N, MPI_DOUBLE_PRECISION, from, tag, MPI_COMM_WORLD, status, ierr)
#ifdef CUDA_AWARE_MPI
!$acc end host_data
#else
! step4: send data in to gpu1 from cpu1
!$acc update device(a)
#endif
ENDIF
call MPI_BARRIER(MPI_COMM_WORLD, ierr)
!$acc update self(a)
print*, 'a in rank', rank, ' after communication is ', a(N/2)
!$acc exit data delete(a)
call MPI_BARRIER(MPI_COMM_WORLD, ierr)
END
% which mpif90
/proj/nv/Linux_x86_64/21.9/comm_libs/mpi/bin//mpif90
% mpif90 -V
nvfortran 21.9-0 64-bit target on x86-64 Linux -tp skylake
NVIDIA Compilers and Tools
Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
% mpif90 -acc -Minfo=accel mpi_acc.F90
toy_mpi_openacc:
38, Generating enter data copyin(a(:))
42, Generating Tesla code
43, !$acc loop gang, vector(128) ! blockidx%x threadidx%x
42, Generating default present(a(:))
46, Generating update self(a(:))
76, Generating update device(a(:))
82, Generating update self(a(:))
85, Generating exit data delete(a(:))
% mpirun -np 2 ./a.out
Process 1 of 2 is alive
Process 0 of 2 is alive
Process 0 Using device 0
Process 1 Using device 1
a in rank 1 before communication is 0.000000000000000
a in rank 0 before communication is 1.000000000000000
a in rank 0 after communication is 1.000000000000000
a in rank 1 after communication is 1.000000000000000
% mpif90 -acc -Minfo=accel mpi_acc.F90 -DCUDA_AWARE_MPI=1
toy_mpi_openacc:
38, Generating enter data copyin(a(:))
42, Generating Tesla code
43, !$acc loop gang, vector(128) ! blockidx%x threadidx%x
42, Generating default present(a(:))
46, Generating update self(a(:))
82, Generating update self(a(:))
85, Generating exit data delete(a(:))
% mpirun -np 2 ./a.out
Process 0 of 2 is alive
Process 1 of 2 is alive
Process 1 Using device 1
Process 0 Using device 0
a in rank 1 before communication is 0.000000000000000
a in rank 0 before communication is 1.000000000000000
a in rank 1 after communication is 1.000000000000000
a in rank 0 after communication is 1.000000000000000
【讨论】：
只是想澄清以下几点：通过在 MPI_SEND 和 MPI_RECV 调用周围使用 !$acc host_data use_device(a)，数据直接从 gpu0 发送到 gpu1（避免在问题）因为该指令允许使用与设备对应的内存地址。我说的对吗？ "host_data" 声明变量的设备地址在区域内的主机上使用。所以它只是将“a”的设备地址传递给 MPI 调用。启用 CUDA Aware 的 MPI 可以检测到地址来自设备，并使用 GPU Direct RDMA 在 GPU 之间直接传递内存，包括节点内和节点之间。以上是关于MPI+OpenACC编程中的GPU间通信的主要内容，如果未能解决你的问题，请参考以下文章