利用一种新的低阶PGAS模型重组不规则算法

M. Cason, P. Kogge
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引用次数: 0

摘要

本文给出了一个并行图遍历原语工具包的分析和仿真结果,该工具包使用一种新颖的低级分区全局地址空间(PGAS)编程模型构建。与高级HEC PGAS语言(UPC, Chapel, Fortress)不同,这种移动主观(MoS)模型不会隐藏编译器或运行时中的并行化或通信开销。与其他低级HEC语言(C/MPI)不同,该模型提供了1)细粒度同步功能,2)内存的PGAS视图,以及3)对象封装。本文展示了该编程模型是如何将研究得很好的最小生成森林(MSF)算法转化为一种新的MSF算法的,该算法允许在一种新的多线程架构上实现百万方式的良好并行。我们提供了分析来说明为什么朴素的MSF公式对于某些输入图是不可伸缩的。然后,我们提供了对MoS重新表述的分析,以展示如何通过确保任意输入图的数据和计算的良好分布来实现可扩展性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Recomposing an Irregular Algorithm Using a Novel Low-Level PGAS Model
This paper presents analysis and simulation results for a toolkit of parallel graph traversal primitives which were built using a novel, low-level partitioned global address space (PGAS) programming model. Unlike high-level HEC PGAS languages (UPC, Chapel, Fortress), this mobile-subjective (MoS) model does not hide parallelization or communication overhead in the compiler or runtime. Unlike other low-level HEC languages (C/MPI) this model provides 1) facilities for fine-grain synchronization, 2) PGAS view of memory, and 3) object encapsulation. This paper shows how this programming model facilitated the transformation of the well-studied minimum spanning forest (MSF) algorithm into a new MSF algorithm which allowed for million way well-behaved parallelism on a novel multithreaded architecture. We provide analysis to show why naive formulations of MSF are not scalable for certain input graphs. We then provide analysis of the MoS reformulation to show how scalability is achieved by ensuring a good distribution of data and computation for arbitrary input graphs.
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