gloo
ompi
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gloo | ompi | |
---|---|---|
2 | 10 | |
1,140 | 2,008 | |
1.9% | 2.9% | |
8.0 | 9.7 | |
16 days ago | 7 days ago | |
C++ | C | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
gloo
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Releasing Gloo 0.4.0
These are two separate libraries that do very different things but share the same name. They are also written in two separate languages. That is a sizable gap between them, and reusing names happens often with libraries. Gloo (rust-wasm, this post) is also not new. Though, relative to Gloo (Go, solo-io), it is newer. But, there is also a Github repo even older than Gloo (solo-io): https://github.com/facebookincubator/gloo. As well, even if these were for some odd reason all about wasm, none of them are actually that popular. solo-io Gloo has the most stars (though that isn't the best metric of popularity, since it is relative to the community that actually uses it), but 3k simply isn't that much. There is certainly a good argument to look down on libraries that reuse popular library names, but this isn't really the case here. Both started not too long after each other (solo-io would not have most of the stars it currently has when Gloo-Rust started), are in separate languages (thus separate communities), and do very separate things.
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Distributed Training Made Easy with PyTorch-Ignite
backends from native torch distributed configuration: nccl, gloo, mpi.
ompi
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Ask HN: Does anyone care about OpenPOWER?
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.
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Announcing Chapel 1.32
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.
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How much are you meant to comment on a code?
One of the guys at the local LUG is one of the lead maintainers of Open MPI. He told us about a comment that ran into the hundreds of lines, all for a one-line change in the code.
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Which license to choose when you want credit
But it would be very inconvenient to have to keep crediting everyone who's ever worked on it. If you look at old projects, their licenses can have like 10-20 of those lines (here's one I was recently looking into).
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First True Exascale Supercomputer
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/
- Managing parallelism by process vs by machine
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MPI + CUDA Program for thermal conductivity problem
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.
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Distributed Training Made Easy with PyTorch-Ignite
backends from native torch distributed configuration: nccl, gloo, mpi.
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FEA computer simulation question
I use a linux ubuntu machine with MPI (https://www.open-mpi.org/). I had a question on making my computer simulations faster. Would be better to get an older AMD 9590 machine clocked at 4.7 ghz or continue using my Ryzen 7 1700 machine clocked at something like 3.5ghz?
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C Deep
OpenMPI - Message passing interface implementation. BSD-3-Clause
What are some alternatives?
NCCL - Optimized primitives for collective multi-GPU communication
Redis - Redis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes, Streams, HyperLogLogs, Bitmaps.