C++ is making me depressed / CUDA question

This page summarizes the projects mentioned and recommended in the original post on reddit.com/r/rust

Our great sponsors
  • InfluxDB - Build time-series-based applications quickly and at scale.
  • Zigi - Workflow assistant built for devs & their teams
  • SonarLint - Clean code begins in your IDE with SonarLint
  • Scout APM - Truly a developer’s best friend
  • Rust-CUDA

    Ecosystem of libraries and tools for writing and executing fast GPU code fully in Rust.

    And here's an example on how to add two floats using Rust-CUDA: https://github.com/Rust-GPU/Rust-CUDA/blob/master/examples/cuda/gpu/add_gpu/src/lib.rs

  • toast

    Time Ordered Astrophysics Scalable Tools (by hpc4cmb)

    If you just want to do a matrix multiplication with CUDA (and not inside some CUDA code), you should use cuBLAS rather than CUTLASS (here is some wrapper code I wrote and the corresponding helper functions if your difficulty is using the library rather than linking it / building), it is a fairly straightforward BLAS replacement (it can be a pain to install but that is life with C++/nvidia).

  • InfluxDB

    Build time-series-based applications quickly and at scale.. InfluxDB is the Time Series Data Platform where developers build real-time applications for analytics, IoT and cloud-native services in less time with less code.

  • jax

    Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

    If you just want to do some numerical code that requires linear algebra and GPU, your best bet would be Julia or Python+JAX.

  • spack

    A flexible package manager that supports multiple versions, configurations, platforms, and compilers.

    Trilinos is a pain to install and get working, I recommend using Spack or a similar tool to deal with it.

  • nalgebra

    Linear algebra library for Rust.

    If you do not need GPU then I would recommend looking into Eigen in C++, nalgebra in Rust (with a BLAS in both cases for improved performance) or one of the above options (Julia / Python+JAX).

  • CUDA.jl

    CUDA programming in Julia.

    If you just want to do some numerical code that requires linear algebra and GPU, your best bet would be Julia or Python+JAX.

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a more popular project.

Suggest a related project

Related posts