python 并行进程 mpi4py

hello.py

from mpi4py import MPI comm = MPI.COMM_WORLD rank = comm.Get_rank() print("hello world from process ", rank)

C:> mpiexec -n 5 python helloWorld_MPI.py

jieguo

('hello world from process ', 1) ('hello world from process ', 0) ('hello world from process ', 2) ('hello world from process ', 3) ('hello world from process ', 4)

2、进程间通信 from mpi4py import MPI comm = MPI.COMM_WORLD rank = comm.rank print("my rank is : " , rank)

if rank == 0: data = 10000000 destination_process = 4 comm.send(data,dest=destination_process) print("sending data % s " % data + "to process % d" % destination_process)

if rank == 1: destination_process = 8 data = "hello" comm.send(data,dest=destination_process) print("sending data % s :" % data + "to process % d" % destination_process)

if rank == 4: data = comm.recv(source = 0) print("data received is = % s" % data)

if rank == 8: data1 = comm.recv(source = 1) print("data1 received is = % s" % data1)

mpiexec -n 9 python pointToPointCommunication.py

3、sendrecv 避免死锁 if rank==1: data_send= "a" destination_process = 5 source_process = 5 data_received=comm.sendrecv(data_send,dest=destination_process,source =source_process) if rank==5: data_send= "b" destination_process = 1 source_process = 1 data_received=comm.sendrecv(data_send,dest=destination_process, source=source_process)

4、共享变量 bcast from mpi4py import MPI comm = MPI.COMM_WORLD rank = comm.Get_rank() if rank == 0: variable_to_share = 100 else: variable_to_share = None variable_to_share = comm.bcast(variable_to_share, root=0) print("process = %d" %rank + " variable shared = %d " %variable_to_share)

C:>mpiexec -n 10 python broadcast.py process = 0 variable shared = 100 process = 8 variable shared = 100 process = 2 variable shared = 100 process = 3 variable shared = 100 process = 4 variable shared = 100 process = 5 variable shared = 100 process = 9 variable shared = 100 process = 6 variable shared = 100 process = 1 variable shared = 100 process = 7 variable shared = 100

5、scatter 发送不同数据 from mpi4py import MPI comm = MPI.COMM_WORLD rank = comm.Get_rank() if rank == 0: array_to_share = [1, 2, 3, 4 ,5 ,6 ,7, 8 ,9 ,10] else: array_to_share = None recvbuf = comm.scatter(array_to_share, root=0) print("process = %d" %rank + " recvbuf = %d " %recvbuf)

C:>mpiexec -n 10 python scatter.py process = 0 variable shared = 1 process = 4 variable shared = 5 process = 6 variable shared = 7 process = 2 variable shared = 3 process = 5 variable shared = 6 process = 3 variable shared = 4 process = 7 variable shared = 8 process = 1 variable shared = 2 process = 8 variable shared = 9 process = 9 variable shared = 10

6、gather反向scatter from mpi4py import MPI comm = MPI.COMM_WORLD size = comm.Get_size() rank = comm.Get_rank() data = (rank+1)2 data = comm.gather(data, root=0) if rank == 0: print ("rank = %s " %rank + "...receiving data to other process") for i in range(1, size): data[i] = (i+1)2 value = data[i] print(" process %s receiving %s from process %s" % (rank , value , i))

C:>mpiexec -n 5 python gather.py rank = 0 ...receiving data to other process process 0 receiving 4 from process 1 process 0 receiving 9 from process 2 process 0 receiving 16 from process 3 process 0 receiving 25 from process 4

7\ alltoall from mpi4py import MPI import numpy

comm = MPI.COMM_WORLD size = comm.Get_size() rank = comm.Get_rank() a_size = 1

senddata = (rank+1)numpy.arange(size,dtype=int) recvdata = numpy.empty(sizea_size,dtype=int)

comm.Alltoall(senddata,recvdata) print(" process %s sending %s receiving %s" % (rank , senddata , recvdata))

C:>mpiexec -n 5 python alltoall.py process 0 sending [0 1 2 3 4] receiving [0 0 0 0 0] process 1 sending [0 2 4 6 8] receiving [1 2 3 4 5] process 2 sending [0 3 6 9 12] receiving [2 4 6 8 10] process 3 sending [0 4 8 12 16] receiving [3 6 9 12 15] process 4 sending [0 5 10 15 20] receiving [4 8 12 16 20]

8\comm.reduce: comm.Reduce(sendbuf, recvbuf, rank_of_root_process, op =type_of_reduction_operation) // MPI.MAX : 返回最大的元素 MPI.MIN : 返回最小的元素 MPI.SUM : 对所有元素相加 MPI.PROD : 对所有元素相乘 MPI.LAND : 对所有元素进行逻辑操作 MPI.MAXLOC : 返回最大值，以及拥有它的进程 MPI.MINLOC : 返回最小值，以及拥有它的进程

import numpy import numpy as np from mpi4py import MPI comm = MPI.COMM_WORLD size = comm.size rank = comm.rank array_size = 3 recvdata = numpy.zeros(array_size, dtype=numpy.int) senddata = (rank+1)*numpy.arange(size,dtype=numpy.int) print("process %s sending %s " % (rank , senddata)) comm.Reduce(senddata, recvdata, root=0, op=MPI.SUM) print('on task', rank, 'after Reduce: data = ', recvdata)

C:>mpiexec -n 3 python reduction2.py process 2 sending [0 3 6] on task 2 after Reduce: data = [0 0 0] process 1 sending [0 2 4] on task 1 after Reduce: data = [0 0 0] process 0 sending [0 1 2] on task 0 after Reduce: data = [ 0 6 12]

9\comm.Create_cart??? https://python-parallel-programmning-cookbook.readthedocs.io/zh_CN/latest/chapter3/19_How_to_optimize_communication.html