Collaborative Research: Personalized Benchmarks for High Performance Computing Applications

协作研究：高性能计算应用程序的个性化基准

• 批准号: 1535177
• 负责人: Marianne Winslett
• 金额: $ 30.9万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1535177&HistoricalAwards=false
• 关键词: Collaborative; Research; Personalized; Benchmarks; Performance

As high-performance computing applications target ever-larger problems, data input and output (I/O) takes up more and more run time. Users, software developers, and platform administrators often find it difficult to understand what an application's I/O code is doing, why it is slow, how it might be improved, or how well it would perform on a different platform. I/O benchmarks help address this problem, but they are expensive to produce and thus are not available for most applications. This project is providing user-friendly personalized I/O benchmarks for all applications, by leveraging existing lightweight I/O profilers that already monitor the behavior of applications on high-performance computing platforms. The resulting personalized benchmarks will help researchers, developers, and purchasers in evaluating potential new storage system architectures, evaluating existing or new versions of storage systems and I/O libraries, planning for purchases, comparing performance of application clusters or workloads across platforms, and improving the performance of parallel I/O libraries and applications. The analytics and benchmark generation software, and example benchmarks, will be publicly released. This project uses two methods to construct personalized I/O benchmarks. First, the project is making existing applications self-benchmarking across all of their runs, by providing analytics and visualization facilities to convey to stakeholders the information already automatically captured by lightweight I/O profilers such as Darshan during each run. Second, the project is creating platform-customized benchmark suites that represent the mix of application-level workloads observed on a given platform. To accomplish this, the project is clustering observed production jobs based on their I/O behavior and using both new and existing I/O kernel generation techniques to generate a compact benchmark for each cluster. The resulting benchmark suite will advance the state of the art by serving as a proxy for real-world, platform-specific production I/O workloads, and by providing previously unavailable insight into how prevalent those workloads are at a given facility.

由于高性能计算应用程序针对越来越多的问题，数据输入和输出（I/O）占用越来越多的运行时间。用户，软件开发人员和平台管理员通常会发现很难理解应用程序的I/O代码在做什么，为什么慢，如何改进它或在其他平台上的性能。 I/O基准测试有助于解决此问题，但生产成本昂贵，因此对于大多数应用程序都不可用。该项目通过利用现有的轻量级I/O剖面仪为所有应用程序提供了用户友好的个性化I/O基准，这些I/O参皮师已经监视了高性能计算平台上应用程序的行为。由此产生的个性化基准将帮助研究人员，开发人员和购买者评估潜在的新存储系统架构，评估现有或新版本的存储系统和I/O库，计划购买计划，比较跨平台的应用程序群集或工作量的性能，并提高I/O库和应用程序的性能。分析和基准生成软件以及示例基准将公开发布。该项目使用两种构建个性化I/O基准测试的方法。首先，该项目是通过提供分析和可视化设施来向利益相关者传达已经由轻量级I/O毕业生（例如Darshan）在每次运行中自动捕获的信息，从而使现有的应用程序在其所有运行中进行自基础测量。其次，该项目正在创建平台定制的基准套件，代表在给定平台上观察到的应用程序级工作负载的组合。为此，该项目是根据其I/O行为聚类观察到的生产作业，并使用新的和现有的I/O内核生成技术为每个群集生成紧凑的基准。由此产生的基准套件将通过担任现实世界，特定于平台的I/O工作负载的代理，并通过提供以前不可用的洞察力，以了解这些工作负载如何在给定的设施中。