Flexible Data Redistribution in a Task-Based Runtime System

Qinglei Cao, George Bosilca, Wei Wu, Dong Zhong, Aurelien Bouteiller, Jack Dongarra

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Data redistribution aims to reshuffle data to optimize some objective for an algorithm. The objective can be multi-dimensional, such as improving computational load balance or decreasing communication volume or cost, with the ultimate goal to increase the efficiency and therefore decrease the time-to-solution for the algorithm. The classical redistribution problem focuses on optimally scheduling communications when reshuffling data between two regular, usually block-cyclic, data distributions. Recently, task-based runtime systems have gained popularity as a potential candidate to address the programming complexity on the way to exascale. In addition to an increase in portability against complex hardware and software systems, task-based runtime systems have the potential to be able to more easily cope with less-regular data distribution, providing a more balanced computational load during the lifetime of the execution. In this scenario, it becomes paramount to develop a general redistribution algorithm for task-based runtime systems, which could support all types of regular and irregular data distributions. In this paper, we detail a flexible redistribution algorithm, capable of dealing with redistribution problems without constraints of data distribution and data size and implement it in a task-based runtime system, PaRSEC. Performance results show great capability compared to ScaLAPACK, and applications highlight an increased efficiency with little overhead in terms of data distribution and data size.
Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Cluster Computing (CLUSTER)
Number of pages5
ISBN (Print)9781728166773
StatePublished - Nov 2 2020
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-30
Acknowledgements: This research was supported in part by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration. The authors would like also to thank Cray Inc. and Intel in the context of the Cray Center of Excellence and Intel Parallel Computing Center awarded to the Extreme Computing Research Center at KAUST. For computer time, this research used the Shaheen-2 supercomputer hosted at the Supercomputing Laboratory at KAUST.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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