All Projects → SciRuby → rbcuda

SciRuby / rbcuda

Licence: BSD-3-Clause License
CUDA bindings for Ruby

Programming Languages

50402 projects - #5 most used programming language
36898 projects - #4 most used programming language

Projects that are alternatives of or similar to rbcuda

High-performance Bayesian Data Analysis on the GPU in Clojure
Stars: ✭ 342 (+500%)
Mutual labels:  cuda, high-performance-computing, gpu-acceleration, gpu-computing
Clojure library for CUDA development
Stars: ✭ 158 (+177.19%)
Mutual labels:  cuda, gpu-acceleration, gpu-computing
Stars: ✭ 135 (+136.84%)
Mutual labels:  cuda, high-performance-computing, gpu-computing
Vulkan compute for people
Stars: ✭ 264 (+363.16%)
Mutual labels:  high-performance-computing, gpu-acceleration, gpu-computing
Implementation of SYCL for CPUs, AMD GPUs, NVIDIA GPUs
Stars: ✭ 377 (+561.4%)
Mutual labels:  cuda, high-performance-computing, gpu-computing
An ultra-fast, GPU-based large graph embedding algorithm utilizing a novel coarsening algorithm requiring not more than a single GPU.
Stars: ✭ 12 (-78.95%)
Mutual labels:  cuda, high-performance-computing, gpu-computing
stdgpu: Efficient STL-like Data Structures on the GPU
Stars: ✭ 531 (+831.58%)
Mutual labels:  cuda, gpu-acceleration, gpu-computing
Fast Clojure Matrix Library
Stars: ✭ 927 (+1526.32%)
Mutual labels:  cuda, high-performance-computing, gpu-computing
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
Stars: ✭ 793 (+1291.23%)
Mutual labels:  cuda, high-performance-computing, gpu-computing
Concurrent CPU-GPU Programming using Task Models
Stars: ✭ 57 (+0%)
Mutual labels:  cuda, gpu-acceleration, gpu-computing
Deep.Net machine learning framework for F#
Stars: ✭ 99 (+73.68%)
Mutual labels:  cuda, gpu-acceleration, gpu-computing
Marian Dev
Fast Neural Machine Translation in C++ - development repository
Stars: ✭ 136 (+138.6%)
Mutual labels:  cuda, gpu-acceleration
Python library for Room Impulse Response (RIR) simulation with GPU acceleration
Stars: ✭ 145 (+154.39%)
Mutual labels:  cuda, gpu-acceleration
Numerical linear algebra software package
Stars: ✭ 149 (+161.4%)
Mutual labels:  cuda, gpu-computing
A stream processing framework for high-throughput applications.
Stars: ✭ 48 (-15.79%)
Mutual labels:  cuda, high-performance-computing
Accelerate Llvm
LLVM backend for Accelerate
Stars: ✭ 134 (+135.09%)
Mutual labels:  cuda, gpu-computing
Image-processing software for cryo-electron microscopy
Stars: ✭ 219 (+284.21%)
Mutual labels:  cuda, high-performance-computing
A framework for the automated derivation and parallel execution of finite difference solvers on a range of computer architectures.
Stars: ✭ 56 (-1.75%)
Mutual labels:  high-performance-computing, gpu-computing
DaCe - Data Centric Parallel Programming
Stars: ✭ 106 (+85.96%)
Mutual labels:  cuda, high-performance-computing
Automatic parallelization of Python/NumPy, C, and C++ codes on Linux and MacOSX
Stars: ✭ 209 (+266.67%)
Mutual labels:  cuda, gpu-acceleration


The main objectives of RbCUDA are:

  1. Map all of CUDA into Ruby
  2. Ready-made on-GPU linear algebra, reduction, scan using cuBLAS, cuMath, cuSolver libraries.
  3. Random Numer generator using cuRand
  4. Near-zero wrapping overhead.
  5. CUDA profiler for Ruby.

In the near future:

  1. fast-fourier transform(cuFFT)
  2. Parallel Primitives and Data Structures(Thrust)
  3. Image processing (NVIDIA Performance Primitives Library).


Add this line to your application's Gemfile:

git clone
bundle install
rake compile
rake test


TODO: Write usage instructions here


After checking out the repo, run bin/setup to install dependencies. Then, run rake test to run the tests. You can also run bin/console for an interactive prompt that will allow you to experiment.

To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and tags, and push the .gem file to


Bug reports and pull requests are welcome on GitHub at This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.

Code of Conduct

Everyone interacting in the Rbcuda project’s codebases, issue trackers, chat rooms and mailing lists is expected to follow the code of conduct.


  • Ruby Association (Japan) for providing the initial funding for this project through the Ruby Association Grant 2017
  • Special Thanks to Kenta Murata (@mrkn) for his support and mentorship
  • Fukuoka Ruby Award 2018


This software is distributed under the BSD 3-Clause License.

Copyright © 2017, Prasun Anand

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].