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Top 23 C++ HPC Projects
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FluidX3D
The fastest and most memory efficient lattice Boltzmann CFD software, running on all GPUs via OpenCL.
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InfluxDB
Power Real-Time Data Analytics at Scale. 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.
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AdaptiveCpp
Implementation of SYCL and C++ standard parallelism for CPUs and GPUs from all vendors: The independent, community-driven compiler for C++-based heterogeneous programming models. Lets applications adapt themselves to all the hardware in the system - even at runtime!
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hlslib
A collection of extensions for Vitis and Intel FPGA OpenCL to improve developer quality of life.
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qmcpack
Main repository for QMCPACK, an open-source production level many-body ab initio Quantum Monte Carlo code for computing the electronic structure of atoms, molecules, and solids with full performance portable GPU support
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Project mention: [P] - VkFFT now supports quad precision (double-double) FFT computation on GPU | /r/MachineLearning | 2023-09-27Hello, I am the creator of the VkFFT - GPU Fast Fourier Transform library for Vulkan/CUDA/HIP/OpenCL/Level Zero and Metal. In the latest update, I have added support for quad-precision double-double emulation for FFT calculation on most modern GPUs. I understand that modern ML is going in the opposite low-precision direction, but I still think that it may be useful to have this functionality at least for some prototyping and development of concepts.
Project mention: An efficient C++17 GPU numerical computing library with Python-like syntax | /r/programming | 2023-10-05
https://github.com/topics/datalog?l=rust ... Cozo, Crepe
Crepe: https://github.com/ekzhang/crepe :
> Crepe is a library that allows you to write declarative logic programs in Rust, with a Datalog-like syntax. It provides a procedural macro that generates efficient, safe code and interoperates seamlessly with Rust programs.
Looks like there's not yet a Python grammar for the treeedb tree-sitter: https://github.com/langston-barrett/treeedb :
> Generate Soufflé Datalog types, relations, and facts that represent ASTs from a variety of programming languages.
Looks like roxi supports n3, which adds `=>` "implies" to the Turtle lightweight RDF representation: https://github.com/pbonte/roxi
FWIW rdflib/owl-rl: https://owl-rl.readthedocs.io/en/latest/owlrl.html :
> simple forward chaining rules are used to extend (recursively) the incoming graph with all triples that the rule sets permit (ie, the “deductive closure” of the graph is computed).
ForwardChainingStore and BackwardChainingStore implementations w/ rdflib in Python: https://github.com/RDFLib/FuXi/issues/15
Fast CUDA hashmaps
Gdlog is built on CuCollections.
GPU HashMap libs to benchmark: Warpcore, CuCollections,
https://github.com/NVIDIA/cuCollections
https://github.com/NVIDIA/cccl
https://github.com/sleeepyjack/warpcore
/? Rocm HashMap
DeMoriarty/DOKsparse:
Project mention: Portable and vendor neutral parallel programming on heterogeneous platforms | news.ycombinator.com | 2024-04-11
Project mention: What Every Developer Should Know About GPU Computing | news.ycombinator.com | 2023-10-21I thought I'd share something with my experience with HPC that applies to many areas, especially in the rise of GPUs.
The main bottleneck isn't compute, it is memory. If you go to talks you're gonna see lots of figures like this one[0] (typically also showing disk speeds, which are crazy small).
Compute is increasing so fast that at this point we finish our operations long faster than it takes to save those simulations or even create the visualizations and put on disk. There's a lot of research going into this, with a lot of things like in situ computing (asynchronous operations, often pushing to a different machine, but needing many things like flash buffers. See ADIOS[1] as an example software).
What I'm getting at here is that we're at a point where we have to think about that IO bottleneck, even for non-high performance systems. I work in ML now, which we typically think of as compute bound, but being in the generative space there are still many things where the IO bottlenecks. This can be loading batches into memory, writing results to disk, or communication between distributed processes. It's one beg reason we typically want to maximize memory usage (large batches).
There's a lot of low hanging fruit in these areas that aren't going to be generally publishable works but are going to have lots of high impact. Just look at things like LLaMA CPP[2], where in the process they've really decreased the compute time and memory load. There's also projects like TinyLLaMa[3] who are exploring training a 1B model and doing so on limited compute, and are getting pretty good results. But I'll tell you from personal experience, small models and limited compute experience doesn't make for good papers (my most cited work did this and has never been published, gotten many rejections for not competing with models 100x it's size, but is also quite popular in the general scientific community who work with limited compute). Wfiw, companies that are working on applications do value these things, but it is also noise in the community that's hard to parse. Idk how we can do better as a community to not get trapped in these hype cycles, because real engineering has a lot of these aspects too, and they should be (but aren't) really good areas for academics to be working in. Scale isn't everything in research, and there's a lot of different problems out there that are extremely important but many are blind to.
And one final comment, there's lots of code that is used over and over that are not remotely optimized and can be >100x faster. Just gotta slow down and write good code. The move fast and break things method is great for getting moving but the debt compounds. It's just debt is less visible, but there's so much money being wasted from writing bad code (and LLMs are only going to amplify this. They were trained on bad code after all)
[0] https://drivenets.com/wp-content/uploads/2023/05/blog-networ...
[1] https://github.com/ornladios/ADIOS2
[2] https://github.com/ggerganov/llama.cpp
[3] https://github.com/jzhang38/TinyLlama
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
C++ HPC related posts
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ChipStar: Run CUDA/Hip on SPIR-V via OpenCL/Level Zero
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An efficient C++17 GPU numerical computing library with Python-like syntax
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MatX: Efficient C++17 GPU numerical computing library with Python-like syntax
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Learn WebGPU
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Standard way of doing maths with arrays?
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Blaze: High Performance Mathematics In C++
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Sarus VS Podman: comparison of both technologies
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Index
What are some of the best open-source HPC projects in C++? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | ArrayFire | 4,430 |
| 2 | FluidX3D | 3,268 |
| 3 | mfem | 1,576 |
| 4 | Boost.Compute | 1,508 |
| 5 | VkFFT | 1,448 |
| 6 | MatX | 1,120 |
| 7 | AdaptiveCpp | 1,053 |
| 8 | RaftLib | 931 |
| 9 | eve | 862 |
| 10 | cccl | 832 |
| 11 | Fastor | 710 |
| 12 | oneMKL | 575 |
| 13 | relion | 431 |
| 14 | blitz | 398 |
| 15 | occa | 379 |
| 16 | ginkgo | 376 |
| 17 | alpaka | 325 |
| 18 | BabelStream | 311 |
| 19 | Umpire | 302 |
| 20 | hlslib | 290 |
| 21 | qmcpack | 282 |
| 22 | ADIOS2 | 256 |
| 23 | nekRS | 244 |
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