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Adaptive runtime management of spatial and temporal heterogeneity of dynamic SAMR applications.

机译:动态SAMR应用程序的空间和时间异构性的自适应运行时管理。

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摘要

Structured adaptive mesh refinement (SAMR) techniques provide an effective means for dynamically concentrating computational effort and resources to appropriate regions in the application domain and have the potential for enabling highly accurate solutions to simulations of complex systems. However, due to the dynamism and space-time heterogeneity exhibited by these techniques, their scalable parallel implementation continues to present significant challenges.; This thesis aims at designing and evaluating an adaptive runtime management system for parallel SAMR applications by explicitly considering their spatial and temporal heterogeneity on large systems. The key idea is to identify relatively homogenous regions in the computational domain at runtime and apply the most appropriate algorithms that address local requirements of these regions. A hybrid space-time runtime management strategy based on this idea has been developed, which consists of three components. First, adaptive hierarchical strategies dynamically apply multiple partitioners to different regions of the application domain, in a hierarchical manner, to match the local requirements. Novel clustering and partitioning algorithms are developed. The strategy allows incremental repartitioning and rescheduling and concurrent operations. The second component is an application-level pipelining strategy, which trades space for time when resources are sufficiently large and under-utilized. The third component is an application-level out-of-core strategy, which trades time for space when resources are scarce in order to improve the performance and enhance the survivability of applications.; The proposed solutions have been implemented and experimentally evaluated on large-scale systems including the IBM SP4 cluster at San Diego Supercomputer Center with up to 1280 processors. These experiments demonstrate the performance benefits of the developed strategies. Finally, the GridMate simulator is developed to investigate applicability of theses strategies in Grid environments.
机译:结构化自适应网格细化(SAMR)技术提供了一种有效的手段,可以将计算工作量和资源动态地集中到应用程序域中的适当区域,并且具有为复杂系统的仿真提供高度精确的解决方案的潜力。然而,由于这些技术表现出的动态性和时空异质性,它们的可扩展并行实现继续带来重大挑战。本文的目的是通过明确考虑大型系统上的时空异构性,为并行SAMR应用程序设计和评估自适应运行时管理系统。关键思想是在运行时识别计算域中相对同质的区域,并应用最合适的算法来满足这些区域的本地需求。已经开发了基于此思想的混合时空运行时管理策略,该策略由三个组件组成。首先,自适应分层策略以分层方式动态地将多个分区应用于应用程序域的不同区域,以匹配本地需求。开发了新颖的聚类和分区算法。该策略允许增量重新分区和重新安排以及并发操作。第二部分是应用程序级流水线策略,当资源足够大且未充分利用时,它会为时间交换空间。第三部分是应用程序级别的核心策略,该策略在资源不足时用时间换空间,以提高性能并提高应用程序的生存能力。所提出的解决方案已在包括1280个处理器的圣地亚哥超级计算机中心的IBM SP4集群等大规模系统上实施和实验评估。这些实验证明了所开发策略的性能优势。最后,开发了GridMate模拟器来研究这些策略在Grid环境中的适用性。

著录项

  • 作者

    Li, Xiaolin.;

  • 作者单位

    Rutgers The State University of New Jersey - New Brunswick.;

  • 授予单位 Rutgers The State University of New Jersey - New Brunswick.;
  • 学科 Engineering Electronics and Electrical.; Computer Science.
  • 学位 Ph.D.
  • 年度 2005
  • 页码 114 p.
  • 总页数 114
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 无线电电子学、电信技术;自动化技术、计算机技术;
  • 关键词

  • 入库时间 2022-08-17 11:41:22

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