Declaring Lua data types for GPU code generation
Author: 
Paulo MottaAuthors Info & Claims
Paulo MottaAuthors Info & ClaimsPages 44 - 45
Abstract
Some effort has been employed to allow interpreted languages to be able to take advantage of the computing capabilities of GPUs. Using interpreted languages allows to abstract the hardware and its specificities away from the user application, making development less complicated. However, due to hardware dependencies, the code needs to be compiled before execution. We want to compile a Lua function into a GPU kernel as transparently as possible, allowing the user to access the underlying hardware, without the complexities related to the traditional GPU programming. This scenario presents a great challenge on how to infer the variables data types while interfering as little as possible on the user programming paradigm.
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Troels Blum, Mads R. B. Kristensen, and Brian Vinter. 2014. Transparent GPU Execution of NumPy Applications. In Proceedings of the 2014 IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW ’14) . IEEE Computer Society, Washington, DC, USA, 1002–1010.
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Roberto Ierusalimschy, Luiz Henrique, Figueiredo Waldemar, and Celes Filho. 1996. Lua - an extensible extension language. Software: Practice and Experience 26 (1996), 635–652.
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Andreas Klöckner, Nicolas Pinto, Yunsup Lee, Bryan Catanzaro, Paul Ivanov, and Ahmed Fasih. 2012. PyCUDA and PyOpenCL: A Scriptingbased Approach to GPU Run-time Code Generation. Parallel Comput. 38, 3 (March 2012), 157–174.
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- Declaring Lua data types for GPU code generation
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Published In
October 2017
47 pages
ISBN:9781450355179
DOI:10.1145/3141865
- General Chairs:
- Ali Jannesari,
- Pablo de Oliveira Castro,
- Yukinori Sato,
- Tim Mattson
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Published: 23 October 2017
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SPLASH '17
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SPLASH '17: Conference on Systems, Programming, Languages, and Applications: Software for Humanity
October 23, 2017
BC, Vancouver, Canada
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