mpi4py

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> #include "helloworld.h" void sayhello(MPI_Comm comm){ int size, rank; MPI_Comm_size(comm, &size); MPI_Comm

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) There's an old package of mine that is built on mpi4py which enables a functional parallel map for MPI jobs. It's not built for speed -- it was built to

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 command $mpirun -np 48 python codename.py Here "code" is the name of the python script and in the

i've recently encountered a problem trying to share large data among several processors using the command 'send' from the mpi4py-library. Even a 1000x3 numpy float array is too large to be sent. Any ideas how to overcome this problem? thx in advance. I've found a simple solution. Divide data into small enough chunks... I encountered this same problem with Isend (not with Send ). It appears that the problem was due to the sending process terminating before the receiver had received the data. I fixed this by including a comm.barrier() call at the end of each of the processes. Point-to-point send

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 MPI size = MPI.COMM_WORLD.Get_size() rank = MPI.COMM_WORLD.Get_rank() name = MPI.Get_processor_name()

I tried to make a library based on mpi4py, but I want to use it in serial python code. $ python serial_source.py but inside serial_source.py exists some function called parallel_bar from foo import parallel_bar # Can I to make this with mpi4py like a common python source code? result = parallel_bar(num_proc = 5) The motivation for this question is about finding the right way to use mpi4py to optimize programs in python which were not necessarily designed to be run completely in parallel. This is indeed possible and is in the documentation of mpi4py in the section Dynamic Process Management .

I want to use more processors to run my code to minimize the running time only. Though I have tried to do it but failed to get the desired result. My code is a very big one that's why I'm giving here a very small and simple code (though it does not need parallel job to run this code) just to know how can I do parallel job in python. Any comments/ suggestions will be highly appreciated. import numpy as np import matplotlib.pyplot as plt from scipy.integrate import odeint def solveit(n,y0): def exam(y, x): theta, omega = y dydx = [omega, - (2.0/x)*omega - theta**n] return dydx x = np.linspace(0

I am tring to write a MPI-based code to do some calculation using python and MPI4py. However, following the example, I CANNOT scatter a numpy vector into cores. Here is the code and errors, is there anyone can help me? Thanks. import numpy as np from mpi4py import MPI comm = MPI.COMM_WORLD size = comm.Get_size() rank = comm.Get_rank() n = 6 if rank == 0: d1 = np.arange(1, n+1) split = np.array_split(d1, size) split_size = [len(split[i]) for i in range(len(split))] split_disp = np.insert(np.cumsum(split_size), 0, 0)[0:-1] else: #Create variables on other cores d1 = None split = None split_size

Is there a way to have the equivalent of the Python try statement in Cython for the cimport? Something like that: try: cimport something except ImportError: pass I would need this to write a Cython extension that can be compiled with or without mpi4py. This is very standard in compiled languages where the mpi commands can be put between #ifdef and #endif preprocessor directives. How can we obtain the same result in Cython? I tried this but it does not work: try: from mpi4py import MPI from mpi4py cimport MPI from mpi4py.mpi_c cimport * except ImportError: rank = 0 nb_proc = 1 # solve a