Flexible Data Redistribution in a Task-Based Runtime System

TitleFlexible Data Redistribution in a Task-Based Runtime System
Publication TypeConference Paper
Year of Publication2020
AuthorsCao, Q., G. Bosilca, W. Wu, D. Zhong, A. Bouteiller, and J. Dongarra
Conference NameIEEE International Conference on Cluster Computing (Cluster 2020)
Date Published2020-09
Conference LocationKobe, Japan

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.

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