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International Journal of Parallel Programming
Published in: International Journal of Parallel Programming 2/2017

13-05-2016

Automatic CPU/GPU Generation of Multi-versioned OpenCL Kernels for C++ Scientific Applications

Authors: Rafael Sotomayor, Luis Miguel Sanchez, Javier Garcia Blas, Javier Fernandez, J. Daniel Garcia

Published in: International Journal of Parallel Programming | Issue 2/2017

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Abstract

Parallelism has become one of the most extended paradigms used to improve performance. However, it forces software developers to adapt applications and coding mechanisms to exploit the available computing devices. Legacy source code needs to be re-written to take advantage of multi- core and many-core computing devices. Writing parallel applications in a traditional way is hard, expensive, and time consuming. Furthermore, there is often more than one possible transformation or optimization that can be applied to a single piece of legacy code. Therefore many parallel versions of the same original sequential code need to be considered. In this paper, we describe an automatic parallel source code generation workflow (REWORK) for parallel heterogeneous platforms. REWORK automatically identifies promising kernels on legacy C++ source code and generates multiple specific versions of kernels for improving C++ applications, selecting the most adequate version based on both static source code and target platform characteristics.
previous article High Level Data Structures for GPGPU Programming in a Statically Typed Language
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Metadata
Title
Automatic CPU/GPU Generation of Multi-versioned OpenCL Kernels for C++ Scientific Applications
Authors
Rafael Sotomayor
Luis Miguel Sanchez
Javier Garcia Blas
Javier Fernandez
J. Daniel Garcia
Publication date
13-05-2016
Publisher
Springer US
Published in
International Journal of Parallel Programming / Issue 2/2017
Print ISSN: 0885-7458
Electronic ISSN: 1573-7640
DOI
https://doi.org/10.1007/s10766-016-0425-6

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