Implementing a Persistent Offline Cache Improving Time to First Execution (TTFX) of GPU Code in Julia

GPU’s allow users the ability to run code with high data parallelism efficiently on specialized hardware. GPUCompiler.jl provides a GPU compilation process to Julia allowing users to write highly efficient vector operations common in scientific computing. GPUCompiler.jl does not support the same level of persistent offline caching that is available in the core Julia compiler. This increases the time to first execution (TTFX) as programs need to recompile GPU code on every package reload regardless of if any code was changed. In this thesis we implement a persistent offline cache that is capable of storing both type inferred and native code drastically reducing the TTFX on precompiled GPU code. We demonstrate that by caching native code, execution can be sped up 2-3x while reducing compilation storage costs by 3-40x when compared to the current GPU compilation process.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2023

Author dc:creator

• Warner, Collin

Advisor dc:contributor.advisor

• Edelman, Alan

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/151406

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/151406