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.

该项目重点关注如何更轻松地对性能不对称多核处理器 (AMP) 进行编程的问题。当多核处理器的组成核心可能具有不同的特性（例如频率、功能单元等）时，多核处理器被称为性能不对称。高性能计算 (HPC) 社区对 AMP 表现出了极大的兴趣，因为它们被证明可以提供良好的权衡性能和功率之间。另一方面，不对称性使得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运行该系统的处理器的特性良好匹配。更复杂的是，实践中存在多种具有不同配置的 AMP。为了有效地利用此类平台，程序员必须考虑其不对称性并针对每种配置优化其软件。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，并导致版本维护的噩梦。为了解决这个问题，该项目正在开发一种新颖的程序分析技术，即基于阶段的调整。基于相位的调整使应用程序能够有效地利用性能不对称多核。主要目标是创建一种无需更改编译器或操作系统即可部署的技术，不需要程序员的大量输入，并且在很大程度上独立于目标处理器的性能不对称性。更广泛的影响是帮助实现新兴 AMP 和其他新颖的超大规模计算架构的潜力，这反过来又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。