ompi
FlatBuffers
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ompi | FlatBuffers | |
---|---|---|
10 | 48 | |
2,016 | 22,048 | |
3.3% | 1.1% | |
9.7 | 8.7 | |
1 day ago | 6 days ago | |
C | C++ | |
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
FlatBuffers
- FlatBuffers – an efficient cross platform serialization library for many langs
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Cap'n Proto 1.0
I don't work at Cloudflare but follow their work and occasionally work on performance sensitive projects.
If I had to guess, they looked at the landscape a bit like I do and regarded Cap'n Proto, flatbuffers, SBE, etc. as being in one category apart from other data formats like Avro, protobuf, and the like.
So once you're committed to record'ish shaped (rather than columnar like Parquet) data that has an upfront parse time of zero (nominally, there could be marshalling if you transmogrify the field values on read), the list gets pretty short.
https://capnproto.org/news/2014-06-17-capnproto-flatbuffers-... goes into some of the trade-offs here.
Cap'n Proto was originally made for https://sandstorm.io/. That work (which Kenton has presumably done at Cloudflare since he's been employed there) eventually turned into Cloudflare workers.
Another consideration: https://github.com/google/flatbuffers/issues/2#issuecomment-...
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Anyone has experience with reverse engineering flatbuffers?
Much more in the discussion of this particular issue onGitHub: flatbuffers:Reverse engineering #4258
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Flatty - flat message buffers with direct mapping to Rust types without packing/unpacking
Related but not Rust-specific: FlatBuffers, Cap'n Proto.
- flatbuffers - FlatBuffers: Memory Efficient Serialization Library
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How do AAA studios make update-compatible save systems?
If json files are a concern because of space, you can always look into something like protobuffers or flatbuffers. But whatever you use, you should try to find a solution where you don't have to think about the actual serialization/deserialization of your objects, and can just concentrate on the data.
- QuickBuffers 1.1 released
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Choosing a protocol for communication between multiple microcontrollers
Or, as an alternative to protobuffers, there's also flatbuffers, which is lighter weight and needs less memory: https://google.github.io/flatbuffers/
- FlatBuffers: FlatBuffers
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Is using Flatbuffers to parse sensor data a bad application of Flatbuffers?
As the title suggests, I am considering using Flatbuffers as a way to parse sensor data that has been stored in local datafiles. The project language is python.
What are some alternatives?
gloo - Collective communications library with various primitives for multi-machine training.
Protobuf - Protocol Buffers - Google's data interchange format
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.
MessagePack - MessagePack implementation for C and C++ / msgpack.org[C/C++]
NCCL - Optimized primitives for collective multi-GPU communication
MessagePack - MessagePack serializer implementation for Java / msgpack.org[Java]
libvips - A fast image processing library with low memory needs.
Cap'n Proto - Cap'n Proto serialization/RPC system - core tools and C++ library
SWIFT - Modern astrophysics and cosmology particle-based code. Mirror of gitlab developments at https://gitlab.cosma.dur.ac.uk/swift/swiftsim
cereal - A C++11 library for serialization
xla - Enabling PyTorch on XLA Devices (e.g. Google TPU)
Kryo - Java binary serialization and cloning: fast, efficient, automatic