Programmable Code Optimization and Empirical Tuning For High-end Computing

高端计算的可编程代码优化和经验调整

• 批准号: 1261778
• 负责人: Qing Yi
• 金额: $ 11万
• 依托单位: University of Colorado at Colorado Springs
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1261778&HistoricalAwards=false
• 关键词: Programmable; Code; Optimization; Empirical; Tuning

The complexity of modern high-end computers has made it exceedingly difficult for scientific applications to effectively manage resources such as extreme-scale parallelism, single-chip multi-processors, and deep hierarchy of shared/distributed caches and memories. In particular, as machines and applications have both evolved to become complex and massively parallel, compilers have failed to automatically bridge the gap between complex software and diverse hardware platforms.Optimization models for parallel computing have lagged far behind those for serial applications, and conventional compilers are increasingly unable to accommodate emerging high-end architectures.This research develops a new optimization model that allows1) developers to effectively interact with advanced optimizing compilers to provide both domain-specific knowledge and high-level optimization strategies (e.g., directions to enable new or choose amongst differing parallelization strategies); 2) computational specialists to easily define arbitrary domain-specific transformations to directly control performance optimizations to their code; 3) architecture-sensitive optimizations to be easily parameterized and empirically tuned to achieve portable high performance.The optimization model is supported with an integrated environment that contains two main components: ROSE, a C/C++/Fortran2003 source-to-source optimizing compiler developed at DOE/LLNL; and POET, a transformation language together with an empirical optimization engine developed at UTSA. This framework permits different levels of automation and programmer intervention, from fully-automated tuning to semi-automated development to fully programmable control. The research targets both the optimization needs of computational kernels and the more general requirements of whole program optimizations. The framework is integrated as an external development mechanism for the widely-adopted ATLAS library and is connected with empirical tuning research under DOE SciDAC program to improve the efficiency of large-scale scientific applications.

现代高端计算机的复杂性使得科学应用程序很难有效地管理资源，例如超大规模并行性、单芯片多处理器以及共享/分布式缓存和存储器的深层层次结构。特别是，随着机器和应用程序都变得复杂和大规模并行，编译器无法自动弥合复杂软件和多样化硬件平台之间的差距。并行计算的优化模型远远落后于串行应用程序和传统编译器越来越无法适应新兴的高端架构。这项研究开发了一种新的优化模型，允许1)开发人员与高级优化编译器有效交互，以提供特定领域的知识和高级优化策略（例如，启用新的或选择不同的并行化策略）； 2) 计算专家可以轻松定义任意特定领域的转换，以直接控制其代码的性能优化； 3) 体系结构敏感的优化可以轻松参数化和凭经验调整，以实现可移植的高性能。优化模型由包含两个主要组件的集成环境支持：ROSE，开发的 C/C++/Fortran2003 源到源优化编译器美国能源部/LLNL； POET，一种转换语言以及 UTSA 开发的经验优化引擎。该框架允许不同级别的自动化和程序员干预，从全自动调整到半自动开发再到完全可编程控制。该研究既针对计算内核的优化需求，也针对整个程序优化的更一般要求。 该框架作为广泛采用的ATLAS库的外部开发机制进行集成，并与DOE SciDAC计划下的实证调优研究相连接，以提高大规模科学应用的效率。