MPI for Python (mpi4py) Description

High performance computing has become a more affordable resource for researchers in the scientific community over the past years. The popularity of clusters of workstations and clusters of Beowulf classes has been strongly influenced by the combination of high quality open-source software and cheap hardware. Message-passing is one of the most effective parallel computational models. This paradigm is especially suited for distributed memory architectures. It is used in today’s most complex engineering and scientific applications related to modeling, simulation and design. Because of the incompatible options developers had to choose from, portable message-passing parallel programming used to be a nightmare. This situation has changed significantly since the MPI Forum published its standard specification.

Pricing

Pricing Starts At:
Free
Free Version:
Yes

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Company Details

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MPI for Python
Website:
mpi4py.readthedocs.io/en/stable/

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MPI for Python (mpi4py) Screenshot 1
MPI for Python (mpi4py) Screenshot 1
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Product Details

Platforms
Windows
Mac
Linux
Type of Training
Documentation
Customer Support
Online

MPI for Python (mpi4py) Features and Options

MPI for Python (mpi4py) User Reviews

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  • Name: Anonymous (Verified)
    Job Title: Principal Software Engineer
    Length of product use: Less than 6 months
    Used How Often?: Yearly
    Role: User
    Organization Size: 100 - 499
    Features
    Design
    Ease
    Pricing
    Support
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    Critical library for scientific research

    Date: Aug 03 2022

    Summary: MPI for Python is a critically important library used in many kinds of research applications where multiple computers are needed to solve a data analysis problem. It supports sharing computation across many GPU arrays, is performant, and is free.

    Positive: - Allows for parallel processing across a network of computers, for example, for scientific research on supercomputers
    - very performant
    - Standardized and portable system for communicating between members of a network
    - very in-depth documentation
    - supports GPU arrays
    - free

    Negative: - very steep learning curve, especially if you are new to data science

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