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Efficient Abstractions for GPGPU Programming

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Abstract

General purpose (GP)GPU programming demands to couple highly parallel computing units with classic CPUs to obtain a high performance. Heterogenous systems lead to complex designs combining multiple paradigms and programming languages to manage each hardware architecture. In this paper, we present tools to harness GPGPU programming through the high-level OCaml programming language. We describe the SPOC library that allows to handle GPGPU subprograms (kernels) and data transfers between devices. We then present how SPOC expresses GPGPU kernel: through interoperability with common low-level extensions (from Cuda and OpenCL frameworks) but also via an embedded DSL for OCaml. Using simple benchmarks as well as a real world HPC software, we show that SPOC can offer a high performance while efficiently easing development. To allow better abstractions over tasks and data, we introduce some parallel skeletons built upon SPOC as well as composition constructs over those skeletons.

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Acknowledgments

The authors thank Stan Scott and the “High Performance and Distributed Computing” department of Queen’s University of Belfast (United Kingdom) for the 2DRMP library and the program PROP as well as Rachid Habel from Telecom SudParis for sharing his knowledge on PROP. This work is part of the OpenGPU project and is partially funded by SYSTEMATIC PARIS REGION SYSTEMS & ICT CLUSTER (http://opengpu.net/).

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  1. Laboratoire d’Informatique de Paris 6 (LIP6-UMR 7606), Université Pierre et Marie Curie (UPMC-Paris 6), Sorbonne Universités, 4 place Jussieu, 75005 , Paris, France

    Mathias Bourgoin, Emmanuel Chailloux & Jean-Luc Lamotte

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  1. Mathias Bourgoin
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  2. Emmanuel Chailloux
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  3. Jean-Luc Lamotte
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Correspondence to Mathias Bourgoin.

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Bourgoin, M., Chailloux, E. & Lamotte, JL. Efficient Abstractions for GPGPU Programming. Int J Parallel Prog 42, 583–600 (2014). https://doi.org/10.1007/s10766-013-0261-x

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