SHF: Small: Phase-Based Tuning for Better Utilization of Performance-Asymmetric Multicores

SHF：小型：基于相位的调整，以更好地利用性能不对称的多核

• 批准号: 1117937
• 负责人: Hridesh Rajan
• 金额: $ 40万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117937&HistoricalAwards=false
• 关键词: SHF; Small; Phase; Based; Tuning

This project focuses on the problem of making it easier to program performance-asymmetric multicore processors (AMP). A multicore processor is called performance-asymmetric when its constituent cores may have different characteristics such as frequency, functional units, etc. High-performance Computing (HPC) community has demonstrated significant interest in the AMPs as they are shown to provide nice trade-off between the performance and power. On the other hand, asymmetry makes programming these platforms hard. The programmers targeting these hardware platforms must ensure that tasks of a software system are well matched with the characteristics of the processor intended to run it. To make matters even more complicated, a wide range of AMPs exist in practice with varying configurations. To efficiently utilize such platforms, the programmer must account for their asymmetry and optimize their software for each configuration. This manual, costly, tedious, and error prone process significantly complicates software engineering for AMP platforms and leads to version maintenance nightmare. To approach this problem, this project is developing a novel program analysis technique, phase-based tuning. Phase-based tuning adapts an application to effectively utilize performance asymmetric multicores. Main goals are to create a technique that can be deployed without changes in the compiler or operating system, does not require significant inputs from programmer, and is largely independent of the performance-asymmetry of the target processor. The broader impacts are to help realize the potential of emerging AMPs and other novel extreme-scale computing architectures, which in turn will enable researchers in the scientific disciplines to analyze, model, simulate, and predict complex phenomena important to society.

这个项目的重点是如何更容易地对性能不对称的多核处理器（AMP）进行编程。多核处理器被称为性能不对称时，其组成核心可能有不同的特性，如频率，功能单元等，高性能计算（HPC）社区已经表现出显着的兴趣在AMP，因为他们被证明提供良好的性能和功耗之间的权衡。另一方面，不对称性使这些平台的编程变得困难。针对这些硬件平台的程序员必须确保软件系统的任务与旨在运行它的处理器的特性很好地匹配。为了使事情更加复杂，在实践中存在具有不同配置的广泛的AMP。为了有效地利用这些平台，程序员必须考虑它们的不对称性，并针对每个配置优化软件。这种手动、昂贵、繁琐且容易出错的过程使AMP平台的软件工程变得非常复杂，并导致版本维护噩梦。为了解决这个问题，本项目正在开发一种新的程序分析技术，基于阶段的调整。基于阶段的调优使应用程序能够有效地利用性能不对称的多核。主要目标是创建一种技术，可以部署在编译器或操作系统的变化，不需要从程序员的显着输入，并在很大程度上是独立的目标处理器的性能不对称。更广泛的影响是帮助实现新兴AMP和其他新型极端规模计算架构的潜力，这反过来将使科学学科的研究人员能够分析，建模，模拟和预测对社会重要的复杂现象。