通过基于任务的运行时利用稀疏直接求解器的混合系统
Taking advantage of hybrid systems for sparse direct solvers via task-based runtimes
摘要：
正在进行的硬件演进表现出计算资源的数量以及异构性的升级。保持合理水平的性能和可移植性的压力迫使应用程序开发人员离开传统的编程范式，并探索替代解决方案。PaStiX是一个并行稀疏直接求解器，基于现代分层众核架构的动态调度器。在本文中，我们研究的好处和限制，更换高度专业化的内部调度程序的PaStiX求解器与两个通用的运行时系统：PaRSEC和StarPU。因子分解步骤的任务图可用于两个运行时，为它们提供处理和优化其遍历的机会，以便最大化目标硬件平台的算法效率。的PaStiX求解器的性能的比较研究，其本机内部调度，PaRSEC，和StarPU框架，在不同的执行环境上，进行。分析强调，这些通用的基于任务的运行时实现同类平台上的应用程序优化的嵌入式调度程序的结果。此外，它们能够通过利用加速器来显着加快异构环境中的求解器，同时向程序员隐藏其有效操作的复杂性。
英文摘要：
The ongoing hardware evolution exhibits an escalation in the number, as well as in the heterogeneity, of computing resources. The pressure to maintain reasonable levels of performance and portability forces application developers to leave the traditional programming paradigms and explore alternative solutions. PaStiX is a parallel sparse direct solver, based on a dynamic scheduler for modern hierarchical manycore architectures. In this paper, we study the benefits and limits of replacing the highly specialized internal scheduler of the PaStiX solver with two generic runtime systems: PaRSEC and StarPU. The tasks graph of the factorization step is made available to the two runtimes, providing them the opportunity to process and optimize its traversal in order to maximize the algorithm efficiency for the targeted hardware platform. A comparative study of the performance of the PaStiX solver on top of its native internal scheduler, PaRSEC, and StarPU frameworks, on different execution environments, is performed. The analysis highlights that these generic task-based runtimes achieve comparable results to the application-optimized embedded scheduler on homogeneous platforms. Furthermore, they are able to significantly speed up the solver on heterogeneous environments by taking advantage of the accelerators while hiding the complexity of their efficient manipulation from the programmer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
