On Machine Learning and Programming Languages
ModelingPredictive AnalyticsPythonRStatisticsToolsTools & LanguagesJuliaposted by Mike Innes December 13, 2017
This article was co-written by Mike Innes (Julia Computing), David Barber (UCL), Tim Besard (UGent), James Bradbury (Salesforce Research), Valentin Churavy (MIT), Simon Danisch (MIT), Alan Edelman (MIT), Stefan Karpinski (Julia Computing), Jon Malmaud (MIT), Jarrett Revels (MIT), Viral Shah (Julia Computing), Pontus Stenetorp (UCL) and Deniz Yuret (Koç... Read more
Graft.jl – General purpose graph analytics for Julia
BlogTool & LanguagesJuliaposted by Julia Computing August 7, 2017
This blog post describes my work on Graft.jl, a general purpose graph analysis package for Julia. For those unfamiliar with graph algorithms, a quick introduction might help. Proposal My proposal, titled ParallelGraphs, was to develop a parallelized/distributed graph algorithms library. However, in the first month or so, we decided to work towards a... Read more
Technical preview: Native GPU programming with CUDAnative.jl
BlogTool & LanguagesJuliaposted by Tim Besard July 28, 2017
After 2 years of slow but steady development, we would like to announce the first preview release of native GPU programming capabilities for Julia. You can now write your CUDA kernels in Julia, albeit with some restrictions, making it possible to use Julia’s high-level language features to write high-performance... Read more
Technical preview: Native GPU programming with CUDAnative.jl
PlatformsTools & LanguagesJuliaposted by Tim Besard April 20, 2017
After 2 years of slow but steady development, we would like to announce the first preview release of native GPU programming capabilities for Julia. You can now write your CUDA kernels in Julia, albeit with some restrictions, making it possible to use Julia’s high-level language features to write high-performance... Read more
