Collaborative Research: Automatic Extraction of Parallel I/O Benchmarks from HEC Applications

协作研究：从 HEC 应用程序中自动提取并行 I/O 基准

• 批准号: 0937908
• 负责人: Xiaosong Ma
• 金额: $ 50万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0937908&HistoricalAwards=false
• 关键词: Collaborative; Research; Automatic; Extraction; Parallel

I/O performance is often an issue for high-end computing (HEC) codes, due to their increasingly data-intensive nature and the ever-growing CPU-I/O performance gap. Portable parallel I/O benchmarks can help(1) application developers to improve their codes' performance,(2) HEC storage systems architects to improve their designs, and(3) future and current owners of HEC platforms to reduce hardware cost and improve application performance through better system provisioning and configuration.To keep up with the growing scale and complexity of HEC applications, this project develops automated generation of parallel I/O benchmarks, analogous to the SPEC and NAS benchmarks for computation. Our approach will be embedded in BenchMaker, a prototype tool that takes a real-world, large-scale parallel application and automatically distills it into a compact, human-intelligible, I/O-intensive, and parameterized benchmark. Such a benchmark accurately reflects the original application's I/O characteristics and I/O performance, yet with shorter execution time, reduced need for libraries, better portability, and easy scalability.Benchmarks and tools that benefit the computational science community at large will be produced by this research. These benchmark prototypes will be used for parallel computing course projects and student research contests.

I/O性能通常是高端计算（HEC）代码的问题，因为它们的数据密集型性质越来越大，并且不断增长的CPU-I/O性能差距。 便携式平行I/O基准可以帮助（1）应用程序开发人员改善其代码的性能，（2）HEC存储系统建筑师以改善其设计，以及（3）HEC平台的未来和现任所有者，以降低硬件成本并通过更好的系统提供和配置来提高应用程序性能，以保持越来越多。和NAS基准用于计算。我们的方法将嵌入Benchmaker，这是一种原型工具，该工具采用了真实的，大规模的并行应用，并自动将其提炼成紧凑的，人类无能的，I/O的I/O密集型和参数化的基准。这样的基准准确地反映了原始应用的I/O特性和I/O性能，但是随着执行时间的较短，对库的需求减少，更好的可移植性和易于可伸缩性。这项研究将产生大量计算科学社区的基准和工具。 这些基准原型将用于并行计算课程项目和学生研究竞赛。