ompi
MessagePack
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ompi | MessagePack | |
---|---|---|
10 | 22 | |
2,016 | 1,378 | |
3.3% | 0.4% | |
9.7 | 8.1 | |
1 day ago | 6 days ago | |
C | Java | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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
MessagePack
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What is the fastest way to encode the arbitrary struct into bytes?
so appreciate such a detailed reply, thanks. btw, why did you choose tinylib/msgp from 4 available go-impls?
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Using Arduino as input to Rust project (help needed)
If you find you're running the serial connection at maximum speed and it's still not fast enough, try switching to a more compact binary encoding that has both Serde and Arduino implementations, like MsgPack... though I don't remember enough about its format off the top of my head to tell you the easiest way to put an unambiguous header on each packet/message to make the protocol self-synchronizing.
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Java Serialization with Protocol Buffers
The information can be stored in a database or as files, serialized in a standard format and with a schema agreed with your Data Engineering team. Depending on your information and requirements, it can be as simple as CSV, XML or JSON, or Big Data formats such as Parquet, Avro, ORC, Arrow, or message serialization formats like Protocol Buffers, FlatBuffers, MessagePack, Thrift, or Cap'n Proto.
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Multiplayer Networking Solutions
MessagePack Similar to JSONs, just more compact, although not as much as the ones above. Still, it's usefull to retain some readability in your messages.
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Sketch crashes with "Soft WDT reset" randomly (ArduinoJSON and HTTPClient)
I'll try that msgpack.org website.
- Unknown encryption method ?
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GitHub - realtimetech-solution/opack: Fast object or data serialize and deserialize library
First of all, you're comparing this to GSON and Kryo, how does it compare to Msgpack, fast-serialization, but also Elsa and I'm sure, many others? Are there any limitations and/or trade-offs?
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Optimal dispatcher for json messages ?
Upvote for msgpack, one of the great undervalued message protocols available.
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Rust is just as fast as C/C++
I have two suggestions Capnproto, MessagePack (those are only the two examples that came to mind first, i bet there are even one or two especially developed for rust). Both of these are better than json in nearly every way.
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msgspec - a fast & friendly JSON/MessagePack library
Encode messages as JSON or MessagePack.
What are some alternatives?
gloo - Collective communications library with various primitives for multi-machine training.
FlatBuffers - FlatBuffers: Memory Efficient Serialization Library
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.
Kryo - Java binary serialization and cloning: fast, efficient, automatic
NCCL - Optimized primitives for collective multi-GPU communication
Cap'n Proto - Cap'n Proto serialization/RPC system - core tools and C++ library
Protobuf - Protocol Buffers - Google's data interchange format
libvips - A fast image processing library with low memory needs.
protostuff - Java serialization library, proto compiler, code generator
SWIFT - Modern astrophysics and cosmology particle-based code. Mirror of gitlab developments at https://gitlab.cosma.dur.ac.uk/swift/swiftsim
ZLib - A massively spiffy yet delicately unobtrusive compression library.