II-NEW: Enhanced Parallelization for High Performance Computing

II-新：高性能计算的增强并行化

• 批准号: 0855247
• 负责人: Dakai Zhu
• 金额: $ 22.72万
• 依托单位: University of Texas at San Antonio
• 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: 2009-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0855247&HistoricalAwards=false
• 关键词: II; NEW; Enhanced; Parallelization; Performance

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).With the growth in the size of scientific applications the level of parallelism provided by existing techniques leads to suboptimal performance and, therefore, an integrated approach to parallelism is necessary. This research involves the acquisition of a cluster for high performance computing. Using this infrastructure the investigators will improve parallelism at many levels ranging from compiler to I/O, in order to increase the execution performance of scientific applications. The infrastructure offers a valuable tool to validate the results on a state-of-the-art system using large scale scientific applications.This research aims to elevate the productivity and efficiency of high-performance scientific computing through innovative language, compiler, empirical tuning, parallel I/O, and power management technologies. The investigators develop new methods for flow-sensitive static and dynamic program analysis to enhance loop parallelization. The investigators implement new speculative parallelization techniques to expose higher levels of thread parallelism for chip-multiprocessors (CMPs). Furthermore, the investigators plan to build an integrated framework for parallel I/O by studing various aspects of declustering, including novel declustering schemes, replicated declustering, heterogeneous declustering, adaptive declustering and declustering using multiple databases. Finally, the investigators plan to develop efficient energy management schemes for parallel high-performance clusters, study various fault tolerance approaches by exploring the inherent space redundancy in CMPs, and address the potential negative effects of energy management on system reliability. The computing platform enables the investigators to validate the impact of their research on application performance and scalability on a large scale parallel system.

该奖项是根据2009年《美国回收与再投资法》（公法111-5）资助的。随着科学应用的规模的增长，现有技术提供的并行性水平导致了次优绩效，因此，必须采用平行性的综合方法。这项研究涉及获取用于高性能计算的集群。使用此基础架构，研究人员将改善从编译器到I/O的许多级别的并行性，以提高科学应用的执行性能。该基础设施提供了一种有价值的工具，可以使用大规模的科学应用来验证最先进的系统的结果。本研究旨在通过创新语言，编译器，经验调整，平行I/O和电力管理技术来提高高性能科学计算的生产率和效率。研究人员开发了用于流动敏感的静态和动态程序分析的新方法，以增强循环并行化。研究人员实施了新的投机并行化技术，以揭示Chip-Multiprocessors（CMP）的较高水平的线程并行性。此外，调查人员计划通过培养清理的各个方面，包括新型的拆毁方案，重复的拆毁，异质拆毁，自适应侦探和使用多个数据库的拆除，来构建并行I/O的集成框架。最后，研究人员计划为平行高性能群集开发有效的能源管理方案，通过探索CMP中固有的空间冗余，研究各种容错方法，并解决能量管理对系统可靠性的潜在负面影响。计算平台使研究人员能够验证其研究对应用程序性能和可伸缩性对大规模平行系统的影响。