嵌套循环 MPI 中的死锁 (Python mpi4py)

Deadlock in nested loop MPI (Python mpi4py)

我不明白为什么这个嵌套循环 MPI 不会停止(即死锁)。我知道大多数 MPI 用户都是基于 C++ / C / Fortran 的,我在这里使用 Python 的 mpi4py 包,但我怀疑这不是编程语言的问题而是我的误解MPI 本身。

代码

#!/usr/bin/env python3
# simple_mpi_run.py

from mpi4py import MPI 
import numpy as np 

comm = MPI.COMM_WORLD 
rank = comm.Get_rank() 
size = comm.Get_size() 
root_ = 0 

# Define some tags for MPI  
TAG_BLOCK_IDX = 1

num_big_blocks = 5

for big_block_idx in np.arange(num_big_blocks): 

    for worker_idx in (1+np.arange(size-1)): 
        if rank==root_: 
            # send to workers 
            comm.send(big_block_idx,
                    dest = worker_idx, 
                    tag = TAG_BLOCK_IDX) 
            print("This is big block", big_block_idx, 
                    "and sending to worker rank", worker_idx) 

        else:
            # receive from root_ 
            local_block_idx = comm.recv(source=root_, tag=TAG_BLOCK_IDX) 
            print("This is rank", rank, "on big block", local_block_idx) 

批量作业脚本

上面运行的SGE批处理作业脚本。出于说明目的,我使用 -np 3 仅将三个进程分配给 mpirun。在实际应用中,我会用到的远不止三个。

#!/bin/bash

# batch_job.sh

#$ -S /bin/bash 
#$ -pe mpi 3
#$ -cwd
#$ -e error.log
#$ -o stdout.log
#$ -R y

MPIPATH=/usr/lib64/openmpi/bin/

PYTHONPATH=$PYTHONPATH:/usr/local/lib/python3.6/site-packages/:/usr/bin/
export PYTHONPATH

PATH=$PATH:$MPIPATH
export PATH

LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/lib/:/usr/lib64/ 
export LD_LIBRARY_PATH

mpirun -v -np 3 python3 simple_mpi_run.py

输出

stdout.log 开始,我在 运行ning qsub batch_job.sh 后看到以下输出:

This is big block 0 and sending to worker rank 1
This is rank 1 on big block 0
This is big block 0 and sending to worker rank 2
This is big block 1 and sending to worker rank 1
This is rank 1 on big block 1
This is big block 1 and sending to worker rank 2
This is big block 2 and sending to worker rank 1
This is rank 1 on big block 2
This is big block 2 and sending to worker rank 2
This is big block 3 and sending to worker rank 1
This is rank 1 on big block 3
This is big block 3 and sending to worker rank 2
This is big block 4 and sending to worker rank 1
This is rank 1 on big block 4
This is big block 4 and sending to worker rank 2
This is rank 2 on big block 0
This is rank 2 on big block 1
This is rank 2 on big block 2
This is rank 2 on big block 3
This is rank 2 on big block 4

问题

据我所知,这是我预期的 正确 输出。但是,当我 运行 qstat 时,我可以看到作业状态保持在 r,表明作业未完成,即使我有我想要的输出。因此,我怀疑这是一个 MPI 死锁问题,但尽管在这里和那里进行了数小时的修补,但我仍然看不到死锁问题。任何帮助将非常感激!


编辑

删除了代码中与手头的死锁问题无关的一些注释块。

挂起的根本原因是您交换了第二个 for 循环和 if 子句:非根级别应该只从主服务器接收一次。

也就是说,您宁愿使用 MPI 集体 MPI_Bcast() 而不是重新发明轮子。

这是您程序的重写版本

#!/usr/bin/env python3
# simple_mpi_run.py

from mpi4py import MPI 
import numpy as np 

comm = MPI.COMM_WORLD 
rank = comm.Get_rank() 
size = comm.Get_size() 
root_ = 0 

# Define some tags for MPI  
TAG_BLOCK_IDX = 1

num_big_blocks = 5

for big_block_idx in np.arange(num_big_blocks): 

    if rank==root_: 
        for worker_idx in (1+np.arange(size-1)): 
            # send to workers 
            comm.send(big_block_idx,
                    dest = worker_idx, 
                    tag = TAG_BLOCK_IDX) 
            print("This is big block", big_block_idx, 
                    "and sending to worker rank", worker_idx) 

    else:
        # receive from root_ 
        local_block_idx = comm.recv(source=root_, tag=TAG_BLOCK_IDX) 
        print("This is rank", rank, "on big block", local_block_idx) 

这里是一个使用 MPI_Bcast()

的更像 MPI 的版本
#!/usr/bin/env python3
# simple_mpi_run.py

from mpi4py import MPI 
import numpy as np 

comm = MPI.COMM_WORLD 
rank = comm.Get_rank() 
root_ = 0 

num_big_blocks = 5

for big_block_idx in np.arange(num_big_blocks): 

    local_block_idx = comm.bcast(big_block_idx, root=root_)

    if rank==root_: 
            print("This is big block", big_block_idx, 
                    "and broadcasting to all worker ranks")
    else:
        print("This is rank", rank, "on big block", local_block_idx)