EAGER: Tensor500: A Streaming Analytics High Performance Computing Benchmark

EAGER：Tensor500：流分析高性能计算基准

• 批准号: 1849463
• 负责人: Tim Andersen
• 金额: $ 9.99万
• 依托单位: Boise State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1849463&HistoricalAwards=false
• 关键词: EAGER; Tensor500; Streaming; Analytics; Performance

Large-scale data analytics is essential to the National Strategic Computing Initiative (NSCI) and NSF's scientific discovery mission. Data-intensive supercomputing applications are increasingly important for handling high-performance computing workloads. However, current benchmarks and performance metrics do not provide useful information on suitability of computing systems for data-intensive applications. This project will develop a new benchmark that can be used to assess the performance of new system implementations aimed at this important class of applications. The research effort will produce a new Stream500 benchmark, complementary to Graph500, that replicates streaming analytics workload. The current benchmark capabilities will be expanded by enabling the exiting web-based infrastructure to accept submissions for multiple benchmarks.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

大规模数据分析对国家战略计算倡议(NSCI)和NSF的科学发现任务至关重要。数据密集型超级计算应用程序对于处理高性能计算工作负载越来越重要。然而，目前的基准和性能指标没有提供有关计算系统是否适合数据密集型应用的有用信息。该项目将制定一个新的基准，可用来评估针对这类重要应用程序的新系统实施的性能。这项研究工作将产生一个新的Stream500基准，作为Graph500的补充，复制流分析工作负载。目前的基准能力将通过使现有的基于网络的基础设施能够接受多个基准的提交来扩展。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A parallel sparse tensor benchmark suite on CPUs and GPUs

• DOI: 10.1145/3332466.3374513
• 发表时间: 2020-01
• 期刊: Proceedings of the 25th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming
• 作者: Jiajia Li;M. Lakshminarasimhan;Xiaolong Wu;Ang Li;C. Olschanowsky;K. Barker