mpi4py

问题 I'm trying to use mpi4py to assemble a very large sparse matrix in parallel. Each rank produces a sparse sub matrix (in scipy's dok format) that needs to be put in place in the very large matrix. So far I have succeeded if each rank produces a numpy array containing the indices and the values of the nonzero values (mimicking the coo format). After the gather procedure I can assemble the large matrix from the numpy arrays. The final matrix is to be written to disk as an mtx format file. What

问题 Again, trying to learn mpi4py on the local cluster. I hence wrote a hello_world program, and it JUST WON'T RUN! I have attached the .py code, and the error. Could someone point out what I am doing wrong? Thanks in advance. hello world: from mpi4py import MPI comm=MPI.COMM_WORLD print("Hello, World! My rank is: " + str(comm.rank)) error message: Traceback (most recent call last): File "./MPI_Hello_World.py", line 1, in <module> from mpi4py import MPI ImportError: libmpich.so.3: cannot open

问题 I use a NumPy function einsum to perform element-wise multiplication of two 2D NumPy arrays and sum. np.einsum('ij,ij',A,B) Each of these array A, B are sized around 10000 x 10000 . I notice this operation is the bottleneck in my code taking up ~85 % of the processing time. How do I quickly parallelize this operation? 来源： https://stackoverflow.com/questions/44888667/which-is-a-quick-way-to-parallelize-element-wise-multiplication-of-2d-numpy-arra

问题 I have one process running a program called t1.py which spawns 3 other processes, all of which run t2.py. I want to broadcast a value from the spawned process with a rank of 0 to the two other spawned processes. However, when bcast is called, the program blocks. Any idea why this happens? And how do I fix it? t1.py from mpi4py import MPI import sys sub_comm = MPI.COMM_SELF.Spawn(sys.executable, args=['t2.py'], maxprocs=3) print 'hi' t2.py from mpi4py import MPI comm = MPI.Comm.Get_Parent()

问题 I am trying to parallelise some operations on a large numpy array using mpi4py. I am currently using numpy.array_split to divide the array into chunks, followed by com.scatter to send the array to different cores and then comm.gather to collect the resulting arrays. A minimal (not) working example is below: import numpy as np from mpi4py import MPI comm = MPI.COMM_WORLD size = comm.Get_size() rank = comm.Get_rank() if rank == 0: test = np.random.rand(411,48,52,40) test_chunks = np.array_split

问题 I am trying to wrap a C function taking an MPI_Comm communicator handle as a parameter via cython. As a result, I want to be able to call the function from python, passing it an mpi4py.MPI.Comm object. What I am wondering is, how to make the conversion from mpi4py.MPI.Comm to MPI_Comm . To demonstrate, I use a simple "Hello World!"-type function: helloworld.h : #ifndef HELLOWORLD #define HELLOWORLD #include <mpi.h> void sayhello(MPI_Comm comm); #endif helloworld.c : #include <stdio.h>

问题 I am working a python code with MPI (mpi4py) and I want to implement my code across many nodes (each node has 16 processors) in a queue in a HPC cluster. My code is structured as below: from mpi4py import MPI comm = MPI.COMM_WORLD size = comm.Get_size() rank = comm.Get_rank() count = 0 for i in range(1, size): if rank == i: for j in range(5): res = some_function(some_argument) comm.send(res, dest=0, tag=count) I am able to run this code perfectly fine on the head node of the cluster using the

问题 I'm using multiprocessing Pool to run a parallelized simulation in Python and it works well in a computer with multiple cores. Now I want to execute the program on a cluster using several nodes. I suppose multiprocessing cannot apply on distributed memory. But mpi4py seems a good option. So what is the simplest mpi4py equivalence to these codes: from multiprocessing import Pool pool = Pool(processes=16) pool.map(functionName,parameters_list) 回答1: There's an old package of mine that is built

问题 I am using a sparse tensor array manipulation I built using dictionaries and Counters in Python. I would like to make it possible to use this array manipulation in parallel. The bottom line is that I have ended up having Counters on each node which I would like to add together using MPI.Allreduce (or another nice solution). For instance with Counters one can do this A = Counter({a:1, b:2, c:3}) B = Counter({b:1, c:2, d:3}) such that C = A+B = Counter({a:1, b:3, c:5, d:3}). I would like to do

问题 I want to do distributed programming with python using the mpi4py package. For testing reasons, I set up a 5-node cluster via Google container engine, and changed my code accordingly. But now, what are my next steps? How do I get my code running and working on all 5 VMs? I tried to just ssh-connect into one VM from my cluster and run the code, but it was obvious that the code was not getting distributed, but instead stayed on the same machine :( [see example below] . Code: from mpi4py import