REDUCING THE BULK IN THE BULK SYNCHRONOUS PARALLEL MODEL

Author:

BARRETT R. F.1,VAUGHAN C. T.1,HAMMOND S. D.1,ROWETH D.2

Affiliation:

1. Sandia National Laboratories, Albuquerque, New Mexico, USA

2. Cray, Inc., Reading, UK

Abstract

For over two decades the dominant means for enabling portable performance of computational science and engineering applications on parallel processing architectures has been the bulk-synchronous parallel programming (BSP) model. Code developers, motivated by performance considerations to minimize the number of messages transmitted, have typically pursued a strategy of aggregating message data into fewer, larger messages. Emerging and future high-performance architectures, especially those seen as targeting Exascale capabilities, provide motivation and capabilities for revisiting this approach. In this paper we explore alternative configurations within the context of a large-scale complex multi-physics application and a proxy that represents its behavior, presenting results that demonstrate some important advantages as the number of processors increases in scale.

Publisher

World Scientific Pub Co Pte Lt

Subject

Hardware and Architecture,Theoretical Computer Science,Software

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Geometric Mapping of Tasks to Processors on Parallel Computers with Mesh or Torus Networks;IEEE Transactions on Parallel and Distributed Systems;2019-09-01

2. Local search to improve coordinate-based task mapping;Parallel Computing;2016-01

3. Infrastructure for In Situ System Monitoring and Application Data Analysis;Proceedings of the First Workshop on In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization;2015-11-15

4. Toward an evolutionary task parallel integrated MPI + X programming model;Proceedings of the Sixth International Workshop on Programming Models and Applications for Multicores and Manycores;2015-02-07

5. Assessing the role of mini-applications in predicting key performance characteristics of scientific and engineering applications;Journal of Parallel and Distributed Computing;2015-01

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3