The main objectives of RbCUDA are:
- Map all of CUDA into Ruby
- Ready-made on-GPU linear algebra, reduction, scan using cuBLAS, cuMath, cuSolver libraries.
- Random Numer generator using cuRand
- Near-zero wrapping overhead.
- CUDA profiler for Ruby.
In the near future:
- fast-fourier transform(cuFFT)
- Parallel Primitives and Data Structures(Thrust)
- Image processing (NVIDIA Performance Primitives Library).
Add this line to your application's Gemfile:
git clone https://github.com/prasunanand/rbcuda 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 rubygems.org.
Bug reports and pull requests are welcome on GitHub at https://github.com/prasunanand/rbcuda. 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