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CAREER: Application-centric, Reliable and Efficient High Performance Computing

职业：以应用为中心、可靠且高效的高性能计算

基本信息

批准号：
1553645
负责人：
Dong Li
金额：
$ 50万
依托单位：
University of California - Merced
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-02-01 至 2023-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1553645&HistoricalAwards=false
关键词：
CAREER Application centric Reliable Efficient

项目摘要

Mission-critical scientific simulations (e.g., climate simulation and fluid dynamics simulation) and enterprise workloads (e.g., search and encryption) running on large-scale computing systems are jeopardized by the increase of faults and errors in hardware and software. Understanding the vulnerability of these large-scale applications is important to minimize performance and power. Lack of the knowledge of application vulnerability forms a major bottleneck of execution efficiency, and jeopardizes HPC simulation capabilities. Previous works rely on random fault injection or detailed architecture analysis to evaluate application vulnerability. They can be slow and inaccurate. There is a big gap between the needs of reliable and efficient HPC and what the current methodologies can provide. This research explores a new methodology to understand application vulnerability. It investigates new analytical and statistical models to quantify and characterize application vulnerability based on a novel metric and application semantics (including algorithm semantics and data semantics). The PI integrates modeling techniques into a broader context for vulnerability analysis to improve the modeling accuracy and explore reliable and efficient protection for applications while examine the interplay between reliability, power, and performance.The outcome from this research will provide support for execution correctness and efficiency of large-scale applications running on future computing systems that demand high data integrity. The proposed research will affect design of reliable applications and algorithms. Built upon the collaboration with industry, the research outcome is expected to be tangible and have direct impact on realistic scientific problems. Furthermore, the tight coupling between research components and education components creates a HPC learning culture to engage students in HPC, addressing HPC workforce shortage in the nation.

在大规模计算系统上运行的关键任务科学模拟(例如，气候模拟和流体动力学模拟)和企业工作负荷(例如，搜索和加密)由于硬件和软件中的故障和错误的增加而受到危害。了解这些大规模应用程序的漏洞对于最大限度地降低性能和功耗非常重要。缺乏对应用程序漏洞的了解是影响执行效率的主要瓶颈，并危及HPC模拟能力。以前的工作依赖于随机错误注入或详细的体系结构分析来评估应用程序漏洞。它们可能很慢，也不准确。在可靠和高效的高性能计算的需求与当前方法所能提供的之间存在很大差距。这项研究探索了一种了解应用程序漏洞的新方法。它基于一种新的度量和应用语义(包括算法语义和数据语义)，研究新的分析和统计模型来量化和表征应用漏洞。PI将建模技术融入到更广泛的脆弱性分析环境中，以提高建模的准确性，探索对应用程序可靠而高效的保护，同时考察可靠性、功耗和性能之间的相互影响，为运行在未来对数据完整性要求较高的计算系统上的大规模应用程序的执行正确性和效率提供支持。拟议的研究将影响可靠的应用程序和算法的设计。在与工业界合作的基础上，研究成果预计将是有形的，并对现实的科学问题产生直接影响。此外，研究部分和教育部分之间的紧密结合创造了一种HPC学习文化，以吸引学生参与HPC，解决全国HPC劳动力短缺的问题。