ompi
ck
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ompi | ck | |
---|---|---|
10 | 7 | |
2,016 | 2,293 | |
3.3% | 0.9% | |
9.7 | 6.9 | |
1 day ago | 13 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.
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
ck
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Falsehoods programmers believe about undefined behavior
Maybe I'm missing something, but x is not volatile and the compiler is free to assume that it is not modified concurrently outside the bounds of C's memory model. Compilers can and do hoist out loop invariants, and https://github.com/concurrencykit/ck/commit/b54ae5c4ace9b94442bbb46858449069f566d269 seems like an example of compilers doing what you say they don't. What am I missing?
- Concurrency Kit
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A portable, license-free, lock-free data structure library written in C.
Recommend checking out http://concurrencykit.org instead.
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Does a thread have a better chance of acquiring a mutex if it's just in time? Or if it's been in the queue? Neither?
If you're interested in how other approaches work, or how one achieves concurrency on shared mutable state without mutual exclusion, would recommend checking out concurrency kit.
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Libdill: Structured Concurrency for C (2016)
There are plenty of practical solutions to the safe memory reclamation problem in C. The language just doesn't force one on you.
From epoch-based reclamation (https://github.com/concurrencykit/ck/blob/master/include/ck_..., especially with the multiplexing extension to Fraser's classic scheme), to quiescence schemes (https://liburcu.org/), or hazard pointers (https://github.com/facebook/folly/blob/master/folly/synchron..., or https://pvk.ca/Blog/2020/07/07/flatter-wait-free-hazard-poin...)... or even simple using a type-stable (https://www.usenix.org/legacy/publications/library/proceedin...) memory allocator.
In my experience, it's easier to write code that is resilient to hiccups in C than in Java. Solving SMR with GC only offers something close to lock-freedom when you can guarantee global GC pauses are short enough... and common techniques to bound pauses, like explicitly managed freelists land you back in the same problem space as C.
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C Deep
ck - Concurrency primitives, safe memory reclamation mechanisms and non-blocking data structures. BSD-2-Clause
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Super-expressive – Write regex in natural language
Indeed they do, https://github.com/concurrencykit/ck
What are some alternatives?
gloo - Collective communications library with various primitives for multi-machine training.
libcds - A C++ library of Concurrent Data Structures
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.
libdill - Structured concurrency in C
NCCL - Optimized primitives for collective multi-GPU communication
moodycamel - A fast multi-producer, multi-consumer lock-free concurrent queue for C++11
FlatBuffers - FlatBuffers: Memory Efficient Serialization Library
Thrust - [ARCHIVED] The C++ parallel algorithms library. See https://github.com/NVIDIA/cccl
libvips - A fast image processing library with low memory needs.
HPX - The C++ Standard Library for Parallelism and Concurrency
SWIFT - Modern astrophysics and cosmology particle-based code. Mirror of gitlab developments at https://gitlab.cosma.dur.ac.uk/swift/swiftsim
CUB - THIS REPOSITORY HAS MOVED TO github.com/nvidia/cub, WHICH IS AUTOMATICALLY MIRRORED HERE.