Automated performance optimization based on post-mortem parallel performance analysis

基于事后并行性能分析的自动性能优化

• 批准号: 2297349
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2297349
• 关键词: Automated; performance; optimization; based; post

Post-mortem parallel performance analysis allows to identify performance bottlenecks in the parallel program execution. Despite recent advances in performance analysis automation, a significant human effort is still required to take advantage of the analysis results and optimize programs. The thesis will investigate new automation techniques for static and dynamic performance optimization, leveraging the program execution analysis tools and libraries developed in the APT group. These tools employ artificial neural networks to automatically identify performance anomalies and their likely causes, but still require an expert programmer to intervene in order to improve performance. This is a time-consuming, error-prone task which this project aims to automate.The aim of the project is to investigate the possibility of replacing heuristic approaches for dynamic program optimization, in particular targeting the problems of work scheduling and data placement in non-uniform memory access architecture systems as well as in large scale distributed memory systems, with more robust machine learning techniques that can automatically adapt to either new execution environments or to dynamically evolving execution conditions (e.g. node failure requiring to re-balance the load, dynamic application behaviour such as in mesh refinement creating run-time imbalance, etc.). Previous work at The University of Manchester has shown that it is possible to automatically detect the occurrence of such events and, to some extent, to identify the causes of performance degradation. This presents an exciting opportunity to build on top of this research and to automate the last missing piece.The main source of the input data to the optimizer will be ComPerf and this thesis will investigate how identified bottlenecks can be mitigated. It can either involve recompiling the application code, modification of the program binary or changing the schedule and data placement in the parallel execution. In a first stage, a set of benchmarks will be selected to establish the execution baseline. It will include publicly available applications, but also can be extended with applications that better shows effectiveness of the applied optimizations. Large-scale applications from the EU project EuroEXA (754337), such as weather and climate modeling, physics and life science simulation. Three leading metrics will be used to asses the optimizer: throughput, latency and power efficiency.The use of Artificial Neural Networks has a potential to be a suitable tool to automate the performance optimization process as they require minimum assumptions and are able to effectively generalize based on incomplete data. One of the possible training settings is to use execution traces and initial application information as an input and a reference optimization as the target. Finally, this thesis aims to show that such approaches can be used to generalize over different architectures, from off-the-shelf Intel platforms to the EuroEXA supercomputer prototype.

事后并行性能分析允许识别并行程序执行中的性能瓶颈。尽管最近在性能分析自动化方面取得了进展，但仍然需要大量的人力来利用分析结果并优化程序。本论文将探讨新的自动化技术，静态和动态性能优化，利用程序执行分析工具和库开发的APT组。这些工具采用人工神经网络来自动识别性能异常及其可能的原因，但仍然需要专家程序员进行干预以提高性能。这是一个耗时，容易出错的任务，本项目旨在实现自动化。本项目的目的是研究替代启发式方法进行动态程序优化的可能性，特别是针对非均匀存储器访问体系结构系统以及大规模分布式存储器系统中的工作调度和数据放置问题，具有更健壮的机器学习技术，可以自动适应新的执行环境或动态演变的执行条件（例如，需要重新平衡负载的节点故障、动态应用行为（诸如在网格细化中创建运行时不平衡等））。曼彻斯特大学以前的工作表明，可以自动检测此类事件的发生，并在一定程度上识别性能下降的原因。这提出了一个令人兴奋的机会，建立在这项研究的顶部，并自动化的最后一个缺失pieces.The输入数据的优化器的主要来源将是ComPerf和本论文将探讨如何识别的瓶颈可以减轻。它可能涉及重新编译应用程序代码、修改程序二进制文件或更改并行执行中的调度和数据放置。在第一阶段，将选择一套基准来确定执行基线。它将包括公开可用的应用程序，但也可以扩展应用程序，以更好地显示应用优化的有效性。欧盟项目EuroEXA（754337）的大规模应用，如天气和气候建模，物理和生命科学模拟。三个主要指标将用于评估优化器：吞吐量，延迟和电源效率。人工神经网络的使用有可能成为自动化性能优化过程的合适工具，因为它们需要最少的假设，并且能够有效地基于不完整的数据进行概括。可能的训练设置之一是使用执行跟踪和初始应用信息作为输入，并将参考优化作为目标。最后，本论文的目的是表明，这种方法可以用来概括不同的体系结构，从现成的英特尔平台的EuroEXA超级计算机原型。