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

这个项目的重点是使性能不对称多核处理器（AMP）的编程变得更容易。多核处理器被称为性能不对称，当它的组成核心可能有不同的特性，如频率，功能单位等。高性能计算（HPC）社区对amp表现出了极大的兴趣，因为它们在性能和功耗之间提供了很好的平衡。另一方面，不对称使这些平台的编程变得困难。以这些硬件平台为目标的程序员必须确保软件系统的任务与运行它的处理器的特性很好地匹配。使事情变得更加复杂的是，在实践中存在着各种各样的amp，其配置也各不相同。为了有效地利用这些平台，程序员必须考虑到它们的不对称性，并针对每种配置优化软件。这种手工的、昂贵的、乏味的、容易出错的过程极大地复杂化了AMP平台的软件工程，并导致了版本维护的噩梦。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，基于相位的调优。基于相位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无需在编译器或操作系统中进行更改即可部署的技术，不需要程序员的大量输入，并且在很大程度上与目标处理器的性能不对称无关。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。

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.