Career: Practical Performance Prediction for Parallel Programmers

该职业奖支持并行程序性能预测方法和工具的研究。我们的目标是一个工具集，它包含了在并行应用程序执行之前预测其性能的通用和准确的方法。所采取的方法是基于先前的工作，已经建立了工具来测量并行程序的性能（谓词分析）和自动构建现有应用程序的性能模型（丢失周期分析）。虽然之前的工作主要依赖于动态整体程序分析，但本研究将增加静态分析技术并将动态工具应用于程序片段。通过将这些工具应用于程序片段，本研究寻求一种灵活的方法来预测备选程序结构的性能。由此产生的工具有望帮助计算科学家调整运行在高性能、大规模并行架构上的程序的性能。该职业奖的教育部分针对本科生的并行处理课程开发和教学，针对研究生的性能测量和预测，以及针对大一新生的计算机科学入门的新方法。

This CAREER award supports research in methods and tools for performance prediction of parallel programs. The goal is a tool set that incorporates general and accurate methods to predict the performance of a parallel application in advance of its execution. The approach taken is based on prior work that has built tools to measure the performance of parallel programs (Predicate Profiling) and automatically construct performance models of existing applications (Lost Cycles Analysis). While the prior work has relied principally on dynamic whole-program analysis, this research will add static analysis techniques and apply dynamic tools to program fragments. By applying those tools to program fragments, the research seeks a flexible method for the performance prediction of alternative program structures. The resulting tool is expected to aid computational scientists attempting to tune the performance of programs running on high performance, massively parallel architectures. The educational component of this CAREER award addresses course development and teaching of parallel processing for undergraduates, performance measurement and prediction for graduate students, and a new approach to introductory computer science for freshmen.