Ruby Association Grant selection result

  We are very happy to announce that the following four projects have been selected by the Ruby Association grant committee.
Accomplishments of the projects will be published around March 2017.

GPU-accelerated Libraries for Ruby to handle very large datasets.

Project Summary

In this project, I would like to take another step to strengthen the Ruby ecosystem for Scientific software. The RbCUDA gem that I intend to develop would provide more flexibility power and control to a programmers/researchers/scientists to harness and optimize their solutions for GPU computing and run across all CUDA powered hardwares using Ruby. RbCUDA interface with NMatrix, Numo::Narray and ArrayFire which will make it easily adoptable for Rubyists. The main objective of RbCUDA would be to map all of CUDA into Ruby with minimal overheads and provide ready-made on-GPU linear algebra, reduction, scan routines using cuBLAS, cuSolver libraries.

Applicant name

Prasun Anand

Implementation of Ruby/Cumo, a CUDA-aware version of Ruby/Numo

Project Summary

In this project, I will implement a CUDA-aware version of Ruby/Numo. By making its interface be compatible with Ruby/Numo, I aim to provide the benefit of speedup using GPU by replacing only a small piece of codes.

Applicant Name

Naotoshi Seo


Project Summary


Applicants Name

松本亮介、小田知央、近藤宇智朗、 笠原義晃、岡村耕二、 嶋吉隆夫、 金子晃介


Project Summary

This project aims to improve benchmark_driver.gem, which is built to compare performance of different Ruby binaries easily and precisely. It also aims to increase the number of benchmark test cases to cover more Ruby core features, and make it easier to optimize Ruby 3x faster by preparing an environment to continuously benchmark the tests with the tool.

Applicant Name

Takashi Kokubun