CoreTSAR: Core Task-Size Adapting Runtime

Heterogeneity continues to increase at all levels of computing, with the rise of accelerators such as GPUs, FPGAs, and other co-processors into everything from desktops to supercomputers. As a consequence, efficiently managing such disparate resources has become increasingly complex. CoreTSAR seeks...

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Bibliographic Details
Published in:IEEE transactions on parallel and distributed systems 2014-10, Vol.26 (11)
Main Authors: Scogland, Thomas R. W., Feng, Wu-chun, Rountree, Barry, de Supinski, Bronis R.
Format: Article
Language:English
Subjects:
MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
Online Access:Get full text
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Summary:Heterogeneity continues to increase at all levels of computing, with the rise of accelerators such as GPUs, FPGAs, and other co-processors into everything from desktops to supercomputers. As a consequence, efficiently managing such disparate resources has become increasingly complex. CoreTSAR seeks to reduce this complexity by adaptively worksharing parallel-loop regions across compute resources without requiring any transformation of the code within the loop. Lastly, our results show performance improvements of up to three-fold over a current state-of-the-art heterogeneous task scheduler as well as linear performance scaling from a single GPU to four GPUs for many codes. In addition, CoreTSAR demonstrates a robust ability to adapt to both a variety of workloads and underlying system configurations.
ISSN:1045-9219
1558-2183