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Developers looking for a Component Library solution

About MPI for Python (mpi4py)

Over the last years, high performance computing has become an affordable resource to many more researchers in the scientific community than ever before. The conjunction of quality open source software and commodity hardware strongly influenced the now widespread popularity of Beowulf class clusters and cluster of workstations. Among many parallel computational models, message-passing has proven to be an effective one. This paradigm is specially suited for (but not limited to) distributed memory architectures and is used in today’s most demanding scientific and engineering application related to modeling, simulation, design, and signal processing. However, portable message-passing parallel programming used to be a nightmare in the past because of the many incompatible options developers were faced to. Fortunately, this situation definitely changed after the MPI Forum released its standard specification.

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Starting Price:
Free
Free Version:
Free Version available.

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Ratings/Reviews - 1 User Review

Overall 5.0 / 5
ease 2.0 / 5
features 5.0 / 5
design 5.0 / 5
support 4.0 / 5
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Company Information

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

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Product Details

Platforms Supported
Windows
Mac
Linux
Training
Documentation
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Online

MPI for Python (mpi4py) Frequently Asked Questions

Q: What kinds of users and organization types does MPI for Python (mpi4py) work with?
Q: What languages does MPI for Python (mpi4py) support in their product?
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MPI for Python (mpi4py) Product Features

MPI for Python (mpi4py) Reviews

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  • A MPI for Python (mpi4py) User
    Principal Software Engineer
    Used the software for: Less than 6 months
    Frequency of Use: Yearly
    User Role: User
    Company Size: 100 - 499
    Design
    Ease
    Features
    Pricing
    Support
    Probability You Would Recommend?
    1 2 3 4 5 6 7 8 9 10

    "Critical library for scientific research"

    Posted 2022-08-03

    Pros: - 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

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

    Overall: 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.

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