mpi

来源： https://stackoverflow.com/questions/39041871/missing-function-from-gprof-output

来源： https://stackoverflow.com/questions/57219771/how-can-i-call-an-mpi-so-file-from-python

来源： https://stackoverflow.com/questions/57219771/how-can-i-call-an-mpi-so-file-from-python

来源： https://stackoverflow.com/questions/63854858/why-does-the-mpi-alltoallv-function-interface-forces-data-to-belong-to-only-one

来源： https://stackoverflow.com/questions/62160745/debugging-mpi-remotely-using-gdb

来源： https://stackoverflow.com/questions/63919764/mpi-distributed-3d-matrix-and-conjoined-2d-matrix

来源： https://stackoverflow.com/questions/63919764/mpi-distributed-3d-matrix-and-conjoined-2d-matrix

来源： https://stackoverflow.com/questions/63919764/mpi-distributed-3d-matrix-and-conjoined-2d-matrix

问题 I am currently building a Monte Carlo application in C++ and I have a question regarding parallelization with MPI. The process I want to parallelize is the MC generation of data. To have good precision in my final results, I specify the goal number of data points. Each data point is generated independently, but might require vastly differing amounts of time. How do I organize the parallelization and workload distribution of the data generation most efficiently? What I have done so far So far

问题 I want to launch parallel processes from a python script (and, for testing, interactively, but not from ipython), across two different versions of python, and have started out with mpi4py. The two versions are (for 2 and 8 cores respectively): Python 2.7.2 |EPD 7.2-2 (64-bit)| (default, Sep 7 2011, 16:31:15) [GCC 4.0.1 (Apple Inc. build 5493)] on darwin and Python 2.6.5 (r265:79063, Apr 16 2010, 13:57:41) [GCC 4.4.3] on linux2 On the first one (to learn the ropes), interactively I get: from