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.
|Original language||English (US)|
|Number of pages||17|
|Journal||IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS|
|State||Published - Nov 30 2021|