gpu-acceleration

Open-source projects categorized as gpu-acceleration
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Language: + C++ + Rust + Python + Java + TypeScript + Emacs Lisp + C# + SystemVerilog + Cuda
Topics: GPU gpu-computing Cuda gpu-programming Machine Learning
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Top 23 gpu-acceleration Open-Source Projects

  • tfjs

    A WebGL accelerated JavaScript library for training and deploying ML models.

    • Project mention: JavaScript Libraries for Implementing Trendy Technologies in Web Apps in 2024 | dev.to | 2024-04-09

    TensorFlow.js

  • TensorRT

    NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.

    • Project mention: AMD MI300X 30% higher performance than Nvidia H100, even with optimized stack | news.ycombinator.com | 2023-12-17

    > It's not rocket science to implement matrix multiplication in any GPU.

    You're right, it's harder. Saying this as someone who's done more work on the former than the latter. (I have, with a team, built a rocket engine. And not your school or backyard project size, but nozzle bigger than your face kind. I've also written CUDA kernels and boy is there a big learning curve to the latter that you gotta fundamentally rethink how you view a problem. It's unquestionable why CUDA devs are paid so much. Really it's only questionable why they aren't paid more)

    I know it is easy to think this problem is easy, it really looks that way. But there's an incredible amount of optimization that goes into all of this and that's what's really hard. You aren't going to get away with just N for loops for a tensor rank N. You got to chop the data up, be intelligent about it, manage memory, how you load memory, handle many data types, take into consideration different results for different FMA operations, and a whole lot more. There's a whole lot of non-obvious things that result in high optimization (maybe obvious __after__ the fact, but that's not truthfully "obvious"). The thing is, the space is so well researched and implemented that you can't get away with naive implementations, you have to be on the bleeding edge.

    Then you have to do that and make it reasonably usable for the programmer too, abstracting away all of that. Cuda also has a huge head start and momentum is not a force to be reckoned with (pun intended).

    Look at TensorRT[0]. The software isn't even complete and it still isn't going to cover all neural networks on all GPUs. I've had stuff work on a V100 and H100 but not an A100, then later get fixed. They even have the "Apple Advantage" in that they have control of the hardware. I'm not certain AMD will have the same advantage. We talk a lot about the difficulties of being first mover, but I think we can also recognize that momentum is an advantage of being first mover. And it isn't one to scoff at.

    [0] https://github.com/NVIDIA/TensorRT

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  • Rio

    A hardware-accelerated GPU terminal emulator focusing to run in desktops and browsers. (by raphamorim)

    • Project mention: Rio terminal released for MacOS, Linux, Windows and BSD | /r/programming | 2023-07-18

  • Anime4KCPP

    A high performance anime upscaler

  • emacs-ng

    A new approach to Emacs - Including TypeScript, Threading, Async I/O, and WebRender.

    • Project mention: Emacs-ng: A project to integrate Deno and WebRender into Emacs | news.ycombinator.com | 2023-11-17

  • dfdx

    Deep learning in Rust, with shape checked tensors and neural networks

    • Project mention: Shape Typing in Python | news.ycombinator.com | 2024-04-13

  • Emu

    The write-once-run-anywhere GPGPU library for Rust

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  • GenerativeAIExamples

    Generative AI reference workflows optimized for accelerated infrastructure and microservice architecture.

    • Project mention: FLaNK Weekly 18 Dec 2023 | dev.to | 2023-12-18

  • TornadoVM

    TornadoVM: A practical and efficient heterogeneous programming framework for managed languages

    • Project mention: Intel Gaudi 3 AI Accelerator | news.ycombinator.com | 2024-04-10

    You don't need to use C++ to interface with CUDA or even write it.

    A while ago NVIDIA and the GraalVM team demoed grCUDA which makes it easy to share memory with CUDA kernels and invoke them from any managed language that runs on GraalVM (which includes JIT compiled Python). Because it's integrated with the compiler the invocation overhead is low:

    https://developer.nvidia.com/blog/grcuda-a-polyglot-language...

    And TornadoVM lets you write kernels in JVM langs that are compiled through to CUDA:

    https://www.tornadovm.org

    There are similar technologies for other languages/runtimes too. So I don't think that will cause NVIDIA to lose ground.

  • stdgpu

    stdgpu: Efficient STL-like Data Structures on the GPU

  • TerraForge3D

    Cross Platform Professional Procedural Terrain Generation & Texturing Tool

  • opt_einsum

    ⚡️Optimizing einsum functions in NumPy, Tensorflow, Dask, and more with contraction order optimization.

  • cccl

    CUDA C++ Core Libraries

    • Project mention: GDlog: A GPU-Accelerated Deductive Engine | news.ycombinator.com | 2023-12-03

    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:

  • Cascade

    Node-based image editor with GPU-acceleration. (by ttddee)

  • PhotonCamera

    Android Camera that uses Enhanced image processing

  • DREAMPlace

    Deep learning toolkit-enabled VLSI placement

    • Project mention: A Simulated Annealing FPGA Placer in Rust | news.ycombinator.com | 2024-01-02

    Yes, see "DREAMPlace: DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement".[1] The technique in particular rather reformulates VLSI placement in terms of a non-linear optimization problem. Which is how ML frameworks (broadly) work, optimizing approximations to high-dimensional non-linear functions. So it's not like, shoving the netlist it into an LLM or an existing network or anything.

    Note that DREAMPlace is a global placer; it also comes with a detail placer but global placement is what it is targeted at. I don't know of an appropriate research analogue for the routing phase of the problem that follows placing, but maybe someone else does.

    [1] https://github.com/limbo018/DREAMPlace

  • NeuralNetwork.NET

    A TensorFlow-inspired neural network library built from scratch in C# 7.3 for .NET Standard 2.0, with GPU support through cuDNN

  • VeriGPU

    OpenSource GPU, in Verilog, loosely based on RISC-V ISA

  • MegBA

    MegBA: A GPU-Based Distributed Library for Large-Scale Bundle Adjustment

  • cudarc

    Safe rust wrapper around CUDA toolkit

    • Project mention: Rust Bindgen Issue (Struct _) | /r/rust | 2023-09-05

    Instead I'm trying to follow along with the structure of cudarc (https://github.com/coreylowman/cudarc) which has done bindings for other Nvidia libraries. Their methodology seems much more straight forward.

  • vuh

    Vulkan compute for people

  • ministark

    🏃‍♂️💨 GPU accelerated STARK prover built on @arkworks-rs

  • Gpufit

    GPU-accelerated Levenberg-Marquardt curve fitting in CUDA

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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).

gpu-acceleration related posts

Index

What are some of the best open-source gpu-acceleration projects? This list will help you:

Project Stars
1 tfjs 18,124
2 TensorRT 9,110
3 Rio 2,933
4 Anime4KCPP 1,746
5 emacs-ng 1,617
6 dfdx 1,607
7 Emu 1,590
8 GenerativeAIExamples 1,535
9 TornadoVM 1,108
10 stdgpu 1,085
11 TerraForge3D 906
12 opt_einsum 802
13 cccl 771
14 Cascade 698
15 PhotonCamera 671
16 DREAMPlace 621
17 NeuralNetwork.NET 536
18 VeriGPU 484
19 MegBA 431
20 cudarc 400
21 vuh 340
22 ministark 323
23 Gpufit 300

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