Julia GPU

Open-source Julia projects categorized as GPU
Edit details
Topics: Julia Cuda HacktoberFest Machine Learning Parallel
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com
featured

Top 15 Julia GPU Projects

  • Makie.jl

    Interactive data visualizations and plotting in Julia

    • Project mention: Julia and Mojo (Modular) Mandelbrot Benchmark | news.ycombinator.com | 2023-09-08

  • CUDA.jl

    CUDA programming in Julia.

    • Project mention: Ask HN: Best way to learn GPU programming? | news.ycombinator.com | 2024-01-01

    It would also mean learning Julia, but you can write GPU kernels in Julia and then compile for NVidia CUDA, AMD ROCm or IBM oneAPI.

    https://juliagpu.org/

    I've written CUDA kernels and I knew nothing about it going in.

  • 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.

    InfluxDB logo

  • Oceananigans.jl

    🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs

    • Project mention: Julia 1.10 Released | news.ycombinator.com | 2023-12-27

    I think it’s also the design philosophy. JuMP and ForwardDiff are great success stories and are packages very light on dependencies. I like those.

    The DiffEq library seems to pull you towards the SciML ecosystem and that might not be agreeable to everyone.

    For instance a known Julia project that simulates diff equations seems to have implemented their own solver

    https://github.com/CliMA/Oceananigans.jl

  • TensorFlow.jl

    A Julia wrapper for TensorFlow

  • Metal.jl

    Metal programming in Julia

  • ParallelStencil.jl

    Package for writing high-level code for parallel high-performance stencil computations that can be deployed on both GPUs and CPUs

  • BifurcationKit.jl

    A Julia package to perform Bifurcation Analysis

    • Project mention: auto-07p VS BifurcationKit.jl - a user suggested alternative | libhunt.com/r/auto-07p | 2024-02-11

    A Julia alternative with methods for automatic bifurcation diagrams. I can work for very large systems.

  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

    SaaSHub logo

  • DiffEqGPU.jl

    GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem

    • Project mention: 2023 was the year that GPUs stood still | news.ycombinator.com | 2023-12-29

    Indeed, and this year we created a system for compiling ODE code not just optimized CUDA kernels but also OneAPI kernels, AMD GPU kernels, and Metal. Peer reviewed version is here (https://www.sciencedirect.com/science/article/abs/pii/S00457...), open access is here (https://arxiv.org/abs/2304.06835), and the open source code is at https://github.com/SciML/DiffEqGPU.jl. The key that the paper describes is that in this case kernel generation is about 20x-100x faster than PyTorch and Jax (see the Jax compilation in multiple ways in this notebook https://colab.research.google.com/drive/1d7G-O5JX31lHbg7jTzz..., extra overhead though from calling Julia from Python but still shows a 10x).

    The point really is that while deep learning libraries are amazing, at the end of the day they are DSL and really pull towards one specific way of computing and parallelization. It turns out that way of parallelizing is good for deep learning, but not for all things you may want to accelerate. Sometimes (i.e. cases that aren't dominated by large linear algebra) building problem-specific kernels is a major win, and it's over-extrapolating to see ML frameworks do well with GPUs and think that's the only thing that's required. There are many ways to parallelize a code, ML libraries hardcode a very specific way, and it's good for what they are used for but not every problem that can arise.

  • FiniteDiff.jl

    Fast non-allocating calculations of gradients, Jacobians, and Hessians with sparsity support

  • oneAPI.jl

    Julia support for the oneAPI programming toolkit.

  • ImplicitGlobalGrid.jl

    Almost trivial distributed parallelization of stencil-based GPU and CPU applications on a regular staggered grid

  • GPUCompiler.jl

    Reusable compiler infrastructure for Julia GPU backends.

  • Vulkan.jl

    Using Vulkan from Julia

  • FoldsCUDA.jl

    Data-parallelism on CUDA using Transducers.jl and for loops (FLoops.jl)

  • BoundaryValueDiffEq.jl

    Boundary value problem (BVP) solvers for scientific machine learning (SciML)

  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

    SaaSHub logo
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).

Julia GPU related posts

Index

What are some of the best open-source GPU projects in Julia? This list will help you:

Project Stars
1 Makie.jl 2,296
2 CUDA.jl 1,144
3 Oceananigans.jl 887
4 TensorFlow.jl 879
5 Metal.jl 331
6 ParallelStencil.jl 288
7 BifurcationKit.jl 285
8 DiffEqGPU.jl 270
9 FiniteDiff.jl 241
10 oneAPI.jl 177
11 ImplicitGlobalGrid.jl 153
12 GPUCompiler.jl 148
13 Vulkan.jl 106
14 FoldsCUDA.jl 54
15 BoundaryValueDiffEq.jl 41

Sponsored
SaaSHub - Software Alternatives and Reviews
SaaSHub helps you find the best software and product alternatives
www.saashub.com