Low-Level Functional GPU Programming for Parallel Algorithms
Paper in proceeding, 2016

We present a Functional Compute Language (FCL) for low-level GPU programming. FCL is functional in style, which allows for easy composition of program fragments and thus easy prototyping and a high degree of code reuse. In contrast with projects such as Futhark, Accelerate, Harlan, Nessie and Delite, the intention is not to develop a language providing fully automatic optimizations, but instead to provide a platform that supports absolute control of the GPU computation and memory hierarchies. The developer is thus required to have an intimate knowledge of the target platform, as is also required when using CUDA/OpenCL directly. FCL is heavily inspired by Obsidian. However, instead of relying on a multi-staged meta-programming approach for kernel generation using Haskell as meta-language, FCL is completely selfcontained, and we intend it to be suitable as an intermediate language for data-parallel languages, including data-parallel parts of high-level array languages, such as R, Matlab, and APL. We present a type-system and a dynamic semantics suitable for understanding the performance characteristics of both FCL and Obsidian-style programs. Our aim is that FCL will be useful as a platform for developing new parallel algorithms, as well as a target language for various code-generators targeting GPU hardware.

Author

Martin Dybdal

University of Copenhagen

Martin Elsman

University of Copenhagen

Joel Bo Svensson

Chalmers, Computer Science and Engineering (Chalmers), Software Technology (Chalmers)

Mary Sheeran

Chalmers, Computer Science and Engineering (Chalmers), Software Technology (Chalmers)

FHPC 2016- Proceedings of the 5th International Workshop on Functional High-Performance Computing

31-37

5th ACM International Workshop on Functional High Performance Computing, FHPC 2016, co-located with ICFP 2016
Nara, ,

Areas of Advance

Information and Communication Technology

Subject Categories

Computer and Information Science

Software Engineering

Computer Science

DOI

10.1145/2975991.2975996

More information

Latest update

9/24/2021