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Collaborative: CSR-AES: System Support for Auto-tuning MPI Applications

Collaborative: CSR-AES: System Support for Auto-tuning MPI Applications
协作:CSR-AES:自动调整 MPI 应用程序的系统支持
批准号:
0720678
负责人:
George Bosilca
金额:
$17.5万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2007
资助国家:
美国
项目状态:
已结题
起止时间:
2007-09-01 至 2010-08-31

项目摘要

项目成果

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中文摘要
翻译
ATOM(自动调优MPI软件)项目正在研究一种能够自动提高大规模科学应用程序性能的软件系统。需要越来越多计算资源的科学代码对现代科学至关重要,但科学家往往必须花费时间来构建运行速度较快的程序,而不是以进行初步研究为代价。随着计算机包含越来越多的计算元素,问题变得更加严重。ATOM项目的目标是通过应用自动应用程序调优来开始解决这个问题。优化编译器将程序转换为语义等价的程序,从而提高程序的性能。在处理任何复杂的体系结构时,很难知道哪些转换将提高性能。自动调优采用的方法是尝试许多转换并对生成的版本进行经验评估。Atom通过结合基于静态分析的代码转换引擎(称为沥青)和OpenMPI库中的运行时支持来执行这种自动调优。编译时和运行时支持的组合允许代码重组以重叠计算和通信,并创建优化的数据打包例程。此外,还可以生成代码以利用多核处理器体系结构。智能优点:建议项目的优点是了解支持可自动调整的MPI程序所需的条件。更广泛的影响:这个项目将通过使其更容易实现良好的性能来影响高性能和科学计算社区以及并行计算机的用户。
英文摘要
The AToMS (Automatic Tuning of MPI Software) project is investigating a software system that can automatically improve the performance of large-scale scientific applications. Scientific codes that demand more and more computing resources are critical to modern science, but too often scientists must spend time constructing programs that run fast at the expense of doing their primary research. As computers contain an increasing number of computing elements, the problem worsens. The goal of the AToMS project is to begin to address this issue by applying automatic application tuning.An optimizing compiler transforms programs into sematically equivalent ones that perform better. When dealing with any complicated architecture it is difficult to know which transformations will improve performance. Auto-tuning takes the approach of trying many transformations and empirically evaluating the resulting versions. AToMS performs this auto-tuning with a combination of a static analysis based code transformation engine (called ASPhALT) and runtime support in the OpenMPI library. The combination of compile-time and run-time support allows for code restructuring to overlap computation and communication and the creation of optimized data-packing routines. In addition, code can be generated to take advantage of multicore processorarchitectures.Intellectual Merit: The merit of the proposed pro ject is in gaining understanding about what is required to support automatically tunable MPI programs. Broader Impacts: This project will impact the high-performance and scientific computing community and users of parallel computers by making it easier to achieve good performance.
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Collaborative Research: Frameworks: Production quality Ecosystem for Programming and Executing eXtreme-scale Applications (EPEXA)
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  • 资助金额:
    $156.62万
  • 财政年份:
    2017
  • 负责人:
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