C++ high-performance-computing

Open-source C++ projects categorized as high-performance-computing
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Topics: HPC parallel-computing GPU C++ Scientific Computing
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Top 22 C++ high-performance-computing Projects

  • Taskflow

    A General-purpose Parallel and Heterogeneous Task Programming System

    • Project mention: Improvements of Clojure in his time | /r/Clojure | 2023-06-16

    For parallel programming nowadays, personally I reach for C++ Taskflow when I really care about performance, or a mix of core.async and running multiple load balanced instances when I’m doing more traditional web backend stuff in Clojure.

  • FluidX3D

    The fastest and most memory efficient lattice Boltzmann CFD software, running on all GPUs via OpenCL.

    • Project mention: FluidX3D | news.ycombinator.com | 2024-03-24

  • WorkOS

    The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.

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  • kokkos

    Kokkos C++ Performance Portability Programming Ecosystem: The Programming Model - Parallel Execution and Memory Abstraction

  • mfem

    Lightweight, general, scalable C++ library for finite element methods

  • 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!

    • Project mention: What Every Developer Should Know About GPU Computing | news.ycombinator.com | 2023-10-21

    Sapphire Rapids is a CPU.

    AMD's primary focus for a GPU software ecosystem these days seems to be implementing CUDA with s/cuda/hip, so AMD directly supports and encourages running GPU software written in CUDA on AMD GPUs.

    The only implementation for sycl on AMD GPUs that I can find is a hobby project that apparently is not allowed to use either the 'hip' or 'sycl' names. https://github.com/AdaptiveCpp/AdaptiveCpp

  • envpool

    C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.

    • Project mention: How do I improve my SB3 PPO on an EnvPool environment | /r/reinforcementlearning | 2023-09-21

    I am looking to improve the overall performance as well as optimize the wall clock time. I slightly modified the code to develop a SB3 wrapper for envpool from here.

  • preCICE

    A coupling library for partitioned multi-physics simulations, including, but not restricted to fluid-structure interaction and conjugate heat transfer simulations.

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  • relion

    Image-processing software for cryo-electron microscopy

  • aphros

    Finite volume solver for incompressible multiphase flows with surface tension. Foaming flows in complex geometries.

  • thread-pool

    A modern, fast, lightweight thread pool library based on C++20 (by DeveloperPaul123)

    • Project mention: C++ Show and Tell - June 2023 | /r/cpp | 2023-06-01

    I just released version 0.6.0 of my C++20 thread_pool library.

  • vuh

    Vulkan compute for people

  • feelpp

    :gem: Feel++: Finite Element Embedded Language and Library in C++

  • 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

  • ADIOS2

    Next generation of ADIOS developed in the Exascale Computing Program

    • Project mention: What Every Developer Should Know About GPU Computing | news.ycombinator.com | 2023-10-21

    I 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

  • intel-qs

    High-performance simulator of quantum circuits

  • mpl

    A C++17 message passing library based on MPI (by rabauke)

  • librapid

    A highly optimised C++ library for mathematical applications and neural networks.

    • Project mention: Huge LibRapid Update -- New Features, Performance Improvements and Bug Fixes | /r/cpp | 2023-08-04

    In addition to all of these changes, I used LibRapid and Surge to write a simple Flappy Bird genetic AI as a proof of concept. The code runs well on all platforms and can simulate over 20,000 birds without too much trouble. If you want to check it out, here's the GitHub page: https://github.com/Pencilcaseman/FlappyBirdAI

  • vpic

    Vector Particle-In-Cell (VPIC) Project

  • OpenCL-Benchmark

    A small OpenCL benchmark program to measure peak GPU/CPU performance.

    • Project mention: I have open-sourced my OpenCL-Benchmark utility | /r/OpenCL | 2023-04-30

  • dftfe

    DFT-FE: Real-space DFT calculations using Finite Elements

    • Project mention: MATLAB Kernel for Jupyter Now Works on Windows | /r/matlab | 2023-06-27

    For instance, I've got tons of open-source tools ( MOOSE, Dakota, Uintah, Firedrake, MUESLI, LAMMPS, DFT-FE ) that I use on my WSL that aren't really compatible with Windows. So I'd prefer (need?) to stay in WSL to produce the Jupyter notebooks with Matlab. I suppose I could probably install a Linux distro of Matlab on my WSL, but then I've got two installations of Matlab eating up disk-space.

  • Bulk

    A modern interface for implementing bulk-synchronous parallel programs.

  • kmer-signatures

    High-performance kmer-signatures

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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++ high-performance-computing related posts

Index

What are some of the best open-source high-performance-computing projects in C++? This list will help you:

Project Stars
1 Taskflow 9,552
2 FluidX3D 3,193
3 kokkos 1,718
4 mfem 1,537
5 AdaptiveCpp 1,040
6 envpool 1,012
7 preCICE 669
8 relion 423
9 aphros 370
10 thread-pool 358
11 vuh 340
12 feelpp 293
13 qmcpack 281
14 ADIOS2 253
15 intel-qs 204
16 mpl 161
17 librapid 152
18 vpic 144
19 OpenCL-Benchmark 102
20 dftfe 99
21 Bulk 92
22 kmer-signatures 0

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