Adding support for heterogeneous parallel architectures to Julia

Traditionally, using a GPGPU to accelerate arbitrary calculations has always been difficult. The common GPGPU platforms introduce new languages exclusively for programming the devices, runtime libraries and new tools that have to be integrated into the build process. That means that using them from any language is cumbersome, repetitive and error-prone. Obviously, there is a need for better integration of GPGPU platforms with programming languages. We provide that using the Julia language and the HSA computing platform. In a first step, we simplify using the HSA runtime library from within Julia. Improving on that, we introduce modifications to Julia that obviate the need for external tools and free the user from interacting directly with the runtime.

Level thesis:degree_level

master

Grantor dc:publisher

Universität Bayreuth

Year

2015

Author dc:creator

• Rollinger, Georg

Repository record source_url

https://epub.uni-bayreuth.de/id/eprint/2583/

OAI identifier oai:identifier

oai:epub.uni-bayreuth.de:2583