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科学应用程序非常困难，可以有效地管理诸如极端尺度并行性，单芯片多处理器以及共享/分布式缓存和记忆的深层层次结构等资源。特别是，随着机器和应用都已经发展为复杂且大规模平行，编译器未能自动弥合复杂软件和各种硬件平台之间的差距。平行计算的优化模型已远远落后于串行应用程序的差异，并且越来越多地与传统的编译器相互作用，无法促进效果的效果。编译器提供特定于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的方向）； 2）计算专家可以轻松定义特定于特定领域的转换，以直接控制其代码的性能优化； 3）易于参数化和经验调整以实现便携式高性能的构造敏感优化。优化模型由一个包含两个主要组件的集成环境支持：rose，c/c ++/fortran2003 source-fortran2003在doe/llnl上开发的源源源源对源优化编译器；和诗人，一种转型语言，以及在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，从完全自动化的调整到半自动化开发到完全可编程的控制。该研究既针对计算内核的优化需求，又针对整个程序优化的更一般要求。 该框架被整合为广泛采用的地图集文库的外部开发机制，并与DOE SCIDAC计划下的经验调整研究有关，以提高大型科学应用的效率。