Performance portable GPU code generation for matrix multiplication

Parallel accelerators such as GPUs are notoriously hard to program; exploiting their full performance potential is a job best left for ninja programmers. High-level programming languages coupled with optimizing compilers have been proposed to attempt to address this issue. However, they rely on device-specific heuristics or hard-coded library implementations to achieve good performance resulting in non-portable solutions that need to be re-optimized for every new device. Achieving performance portability is the holy grail of high-performance computing and has so far remained an open problem even for well studied applications like matrix multiplication. We argue that what is needed is a way to describe applications at a high-level without committing to particular implementations. To this end, we developed in a previous paper a functional data-parallel language which allows applications to be expressed in a device neutral way. We use a set of well-defined rewrite rules to automaticall...

• Type: Journal
• Year: 2016
• Where: PPOPP
• Authors: Toomas Remmelg, Thibaut Lutz, Michel Steuwer, Christophe Dubach
• Keywords: Distributed And Parallel Computing, PPOPP 2016