Evaluating Data Redistribution in PaRSEC
IEEE Transactions on Parallel and Distributed Systems(2022)
摘要
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 of increasing the efficiency and therefore reducing the time-to-solution for the algorithm. The classic redistribution problem focuses on optimally scheduling communications when reshuffling data between two regular, usually block-cyclic, data distributions. Besides distribution, data size is also a performance-critical parameter because it affects the reshuffling algorithm in terms of cache, communication efficiency, and potential parallelism. In addition, task-based runtime systems have gained popularity recently as a potential candidate to address the programming complexity on the way to exascale. In this scenario, it becomes paramount to develop a flexible redistribution algorithm for task-based runtime systems, which could support all types of regular and irregular data distributions and take data size into account. In this article, we detail a flexible redistribution algorithm and implement an efficient approach in a task-based runtime system,
PaRSEC
. Performance results show great capability compared to the theoretical
bound
and
ScaLAPACK
, and applications highlight an increased efficiency with little overhead in terms of data distribution, data size, and data format.
更多查看译文
关键词
Data redistribution,data distribution,data size,data format,task-based programming model,dynamic runtime system,high-performance computing
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要