Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2015

Abstract

Using GPUs as general-purpose processors has revolutionized parallel computing by offering, for a large and growing set of algorithms, massive data-parallelization on desktop machines. An obstacle to widespread adoption, however, is the difficulty of programming them and the low-level control of the hardware required to achieve good performance. This paper suggests a programming library, SafeGPU, that aims at striking a balance between programmer productivity and performance, by making GPU data-parallel operations accessible from within a classical object-oriented programming language. The solution is integrated with the design-by-contract approach, which increases confidence in functional program correctness by embedding executable program specifications into the program text. We show that our library leads to modular and maintainable code that is accessible to GPGPU non-experts, while providing performance that is comparable with hand-written CUDA code. Furthermore, runtime contract checking turns out to be feasible, as the contracts can be executed on the GPU.

Keywords

GPGPU, parallel computing, runtime code generation, programming, object-orientation, design-by-contract, program correctness

Discipline

Programming Languages and Compilers | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 2015 ACM SIGPLAN International Conference on Generative Programming: Concepts and Experiences, Pittsburgh PA, USA, October 26-27

Volume

51

Issue

3

First Page

75

Last Page

84

Identifier

10.1145/2814204.2814216

Publisher

ACM

City or Country

Pittsburgh, PA, USA

Additional URL

https://doi.org/10.1145/2814204.2814216

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