Harnessing parallelism in multicore clusters with the all-pairs and wavefront abstractions
Harnessing parallelism in multicore clusters with the all-pairs and wavefront abstractions
复制标题
通过全对和波前抽象利用多核集群中的并行性
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
D. Thain
中科院分区：
文献类型：
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作者：
Li Yi;C. Moretti;S. Emrich;Kenneth Judd;D. Thain
分布式系统和多核算计算机都是困难的编程环境。尽管专家程序员可能能够调整分布式和多计算机来实现高性能，但非专家可能难以实现甚至可以正常运行的程序。
我们认为，高级抽象是使非专家可访问并行计算的有效方法。抽象是一个定期结构化的框架，用户可以插入简单的顺序程序以创建非常大的并行程序。借助规则的结构和声明性规范，可以在分布式，多项和分布式的多核心系统上实现抽象，并在各种问题范围内具有强大的性能。在以前的工作中，我们介绍了用于计算单个CPU分布式系统的全对抽象。在本文中，我们将全配对扩展到多核心系统，并引入Wavefront，这代表了经济学和生物信息学的许多问题。我们在一台机器上表现出了良好的缩放，这两个抽象在分布式系统中的数百个核心上都有良好的比例。
Both distributed systems and multicore computers are difficult programming environments. Although the expert programmer may be able to tune distributed and multicore computers to achieve high performance, the non-expert may struggle to achieve a program that even functions correctly.
We argue that high level abstractions are an effective way of making parallel computing accessible to the non-expert. An abstraction is a regularly structured framework into which a user may plug in simple sequential programs to create very large parallel programs. By virtue of a regular structure and declarative specification, abstractions may be materialized on distributed, multicore, and distributed multicore systems with robust performance across a wide range of problem sizes. In previous work, we presented the All-Pairs abstraction for computing on distributed systems of single CPUs. In this paper, we extend All-Pairs to multicore systems, and introduce Wavefront, which represents a number of problems in economics and bioinformatics. We demonstrate good scaling of both abstractions up to 32-cores on one machine and hundreds of cores in a distributed system.