SHF: Medium: Collaborative Research: An Inspector/Executor Compilation Framework for Irregular Applications

SHF:Medium:协作研究:针对不规则应用的检查器/执行器编译框架

基本信息

  • 批准号:
    1563818
  • 负责人:
  • 金额:
    $ 39.72万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2016
  • 资助国家:
    美国
  • 起止时间:
    2016-09-01 至 2021-10-31
  • 项目状态:
    已结题

项目摘要

Computational science and engineering provides inexpensive exploration of physical phenomena and design spaces and helps direct experimentation and advise theory. Irregular applications such as molecular dynamics simulations, n-body simulations, finite element analysis, and big graph analytics constitute a critical and significant portion of scientific computing applications. An irregular application is characterized by having indirect memory accesses such as A[B[i]] that cannot be determined when the application is being compiled, therefore severely limiting the applicability of the large body of work on parallelizing compiler technology. Consequently, irregular applications, which are so important in pushing forward the frontiers of science, place a very large burden on computational and domain scientists in developing high-performance implementations for the ever-changing landscape of parallel architectures. The intellectual merit of this project is to develop a compiler and runtime framework for irregular applications, particularly well suited for sparse matrix and graph computations that underlie critical problems in computational science and data science. The broader impact is to provide domain scientists a powerful tool for optimizing and porting performance-critical, irregular computations to current and future multi-core processors and many-core accelerators. The PIs will also continue efforts in outreach and diversity to increase the participation in STEM careers, particularly among women and underrepresented minorities.The approach in this project is to extend the well-established inspector/executor paradigm where the computational dependence structure (based on the memory access pattern) is determined at runtime, and runtime information is passed to a compile-time generated executor. Specifically, an inspector can examine the memory access patterns early in the computation at runtime, and an executor leverages this information to perform data and computation reordering and scheduling to affect memory hierarchy and parallelism optimizations. The project is developing a compiler and runtime framework with new abstractions for expressing and manipulating inspectors; these inspectors may then be integrated nearly seamlessly with each other and with existing compiler optimizations (e.g., loop tiling) to optimize executors. The project is also extending prior work that supports non-affine input code and mixes compile-time and runtime optimization. The resulting system increases the productivity of expert programmers in achieving both high performance and portability on a wide variety of irregular applications.
计算科学与工程提供了对物理现象和设计空间的廉价探索,并有助于指导实验和为理论提供建议。分子动力学模拟、n体模拟、有限元分析和大图分析等不规则应用构成了科学计算应用的关键且重要的部分。 不规则应用程序的特点是具有间接内存访问,例如在编译应用程序时无法确定的 A[B[i]],因此严重限制了并行编译器技术的大量工作的适用性。因此,对于推动科学前沿非常重要的不规则应用程序,在为不断变化的并行体系结构开发高性能实现方面给计算和领域科学家带来了非常大的负担。该项目的智力优点是为不规则应用程序开发编译器和运行时框架,特别适合计算科学和数据科学中关键问题的稀疏矩阵和图计算。更广泛的影响是为领域科学家提供一个强大的工具,用于优化性能关键的、不规则的计算并将其移植到当前和未来的多核处理器和众核加速器。 PI 还将继续努力扩大外展和多样性,以增加对 STEM 职业的参与,特别是女性和代表性不足的少数群体。该项目的方法是扩展完善的检查员/执行员范式,其中计算依赖性结构(基于内存访问模式)在运行时确定,运行时信息被传递给编译时生成的执行器。具体来说,检查器可以在运行时计算的早期检查内存访问模式,执行器利用此信息来执行数据和计算重新排序和调度,以影响内存层次结构和并行性优化。该项目正在开发一个编译器和运行时框架,其中包含用于表达和操作检查器的新抽象;然后,这些检查器可以几乎无缝地相互集成,并与现有的编译器优化(例如循环平铺)集成以优化执行器。该项目还扩展了之前的工作,支持非仿射输入代码并混合编译时和运行时优化。由此产生的系统提高了专家程序员在各种不规则应用程序上实现高性能和可移植性的生产力。

项目成果

期刊论文数量(2)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Abstractions for specifying sparse matrix data transformations
用于指定稀疏矩阵数据转换的抽象
An Object-Oriented Interface to The Sparse Polyhedral Library
稀疏多面体库的面向对象接口
  • DOI:
    10.1109/compsac51774.2021.00275
  • 发表时间:
    2021-07
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Popoola, Tobi;Shankar, Ravi;Rift, Anna;Singh, Shivani;Davis, Eddie C.;Strout, Michelle Mills;Olschanowsky, Catherine
  • 通讯作者:
    Olschanowsky, Catherine
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Catherine Olschanowsky其他文献

Accelerating the Lagrangian particle tracking of residence time distributions and source water mixing towards large scales
加速大尺度停留时间分布和源水混合的拉格朗日粒子追踪
  • DOI:
    10.1016/j.cageo.2021.104760
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    4.4
  • 作者:
    Chen Yang;You-Kuan Zhang;Xiuyu Liang;Catherine Olschanowsky;Xiaofan Yang;Reed Maxwell
  • 通讯作者:
    Reed Maxwell

Catherine Olschanowsky的其他文献

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{{ truncateString('Catherine Olschanowsky', 18)}}的其他基金

CAREER: Compilation Processes to Enhance Dataflow Optimizations
职业:增强数据流优化的编译过程
  • 批准号:
    1943319
  • 财政年份:
    2020
  • 资助金额:
    $ 39.72万
  • 项目类别:
    Continuing Grant
SHF: Small: The Loop Chain Abstraction for Balancing Locality and Parallelism
SHF:小:平衡局部性和并行性的循环链抽象
  • 批准号:
    1700723
  • 财政年份:
    2016
  • 资助金额:
    $ 39.72万
  • 项目类别:
    Standard Grant
SHF: Small: The Loop Chain Abstraction for Balancing Locality and Parallelism
SHF:小:平衡局部性和并行性的循环链抽象
  • 批准号:
    1422725
  • 财政年份:
    2014
  • 资助金额:
    $ 39.72万
  • 项目类别:
    Standard Grant

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