ompi

Open MPI main development repository (by open-mpi)

Ompi Alternatives

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better ompi alternative or higher similarity.

ompi reviews and mentions

Posts with mentions or reviews of ompi. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-02-09.
  • Ask HN: Does anyone care about OpenPOWER?
    2 projects | news.ycombinator.com | 9 Feb 2024
    The commercial Linux world (see https://github.com/open-mpi/ompi/issues/4349) and other open source OSes (eg FreeBSD) seem to have lined up behind little-endian PowerPC. IBM still has a big-endian problem with AIX, IBM i, and Linux on Z.
  • Announcing Chapel 1.32
    6 projects | news.ycombinator.com | 9 Oct 2023
    Roughly, the sets of computational problems that people used (use?) MPI for. Things like numerical solvers for sparse matrices that are so big that you need to split them across your entire cluster. These still require a lot of node-to-node communication, and on top of it, the pattern is dependent on each problem (so easy solutions like map-reduce are effectively out). See eg https://www.open-mpi.org/, and https://courses.csail.mit.edu/18.337/2005/book/Lecture_08-Do... for the prototypical use case.
  • First True Exascale Supercomputer
    2 projects | news.ycombinator.com | 6 Jul 2022
    I have a bit of experience programming for a highly-parallel supercomputer, specifically in my case an IBM BlueGene/Q. In that case, the answer is a lot of message passing (we used Open MPI [0]). Since the nodes are discrete and don't have any shared memory, you end up with something kinda reminiscent of the actor model as popularized by Erlang and co -- but in C for number-crunching performance.

    That said, each of the nodes is itself composed of multiple cores with shared memory. So in cases where you really want to grind out performance, you actually end up using message passing to divvy up chunks of work, and then use classic pthreads to parallelize things further, with lower latency.

    Debugging is a bit of a nightmare, though, since some bugs inevitably only come up once you have a large number of nodes running the algorithm in parallel. But you'll probably be in a mainframe-style time-sharing setup, so you may have to wait hours or more to rerun things.

    This applies less to some of the newer supercomputers, which are more or less clusters of GPUs instead of clusters of CPUs. I imagine there's some commonality, but I haven't worked with any of them so I can't really say.

    [0] https://www.open-mpi.org/

  • MPI + CUDA Program for thermal conductivity problem
    2 projects | /r/CUDA | 4 May 2022
    I would suggest using OpenMPI because it's pretty easy to get started with. You can build OpenMPI with CUDA support, then you can pass device pointers directly to MPI_Send and MPI_Recv. Then you don't have to deal with transfers and synchronization issues.
  • Distributed Training Made Easy with PyTorch-Ignite
    7 projects | dev.to | 10 Aug 2021
    backends from native torch distributed configuration: nccl, gloo, mpi.
  • C Deep
    80 projects | dev.to | 27 Feb 2021
    OpenMPI - Message passing interface implementation. BSD-3-Clause
  • A note from our sponsor - InfluxDB
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    Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality. Learn more →

Stats

Basic ompi repo stats
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5 days ago

open-mpi/ompi is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.

The primary programming language of ompi is C.

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