SI2-SSI: FAMII: High Performance and Scalable Fabric Analysis, Monitoring and Introspection Infrastructure for HPC and Big Data

SI2-SSI：FAMII：适用于 HPC 和大数据的高性能和可扩展结构分析、监控和自省基础设施

• 批准号: 1664137
• 负责人: Dhabaleswar Panda
• 金额: $ 80万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1664137&HistoricalAwards=false
• 关键词: SI2; SSI; FAMII; Performance; Scalable

As the computing, networking, heterogeneous hardware, and storagetechnologies continue to evolve in High-End Computing (HEC) platforms,it becomes increasingly essential and challenging to understand theinteractions between time-critical High-Performance Computing (HPC)and Big Data applications, the software infrastructures upon whichthey rely for achieving high-performing portable solutions, theunderlying communication fabric these high-performance middlewaresdepend on and the schedulers that manage HPC clusters. Suchunderstanding will enable all involved parties (applicationdevelopers/users, system administrators, and middleware developers) tomaximize the efficiency and performance of the individual componentsthat comprise a modern HPC system and solve different grand challengeproblems. There is a clear need and unfortunate lack of a high-performance andscalable tool that is capable of analyzing and correlating thecommunication on the fabric with the behavior of HPC/Big Dataapplications, underlying middleware and the job scheduler on existinglarge HPC systems. The proposed synergistic and collaborative effort,undertaken by a team of computer and computational scientists from OSUand OSC, aims to create an integrated software infrastructure for high-performance and scalable Fabric Analysis, Monitoring andIntrospection for HPC and Big Data. This tool will achieve thefollowing objectives: 1) be portable, easy to use and easy tounderstand, 2) have high performance and scalable rendering andstorage techniques and, 3) be applicable to the differentcommunication fabrics and programming models that are likely to beused on existing large HPC systems and emerging exascale systems. Thetransformative impact of the proposed research and development effortis to design a comprehensive analysis and performance monitoring toolfor applications of current and next generation multipetascale/exascale systems to harness the maximum performance andscalability.The proposed research and the associated infrastructure will have asignificant impact on enabling optimizations of HPC and Big Dataapplications that have previously been difficult to provide. Thesepotential outcomes will be demonstrated by using the proposedframework to validate a variety of HPC and Big Data benchmarks andapplications under multiple scenarios. The integrated middleware andtools will be made publicly available to the community through publicrepositories and publications in the top forums, enabling other MPIand Big Data stacks to adopt the designs. Research results will alsobe disseminated to the collaborating organizations of theinvestigators to impact their HPC software products andapplications. The proposed research directions and their solutionswill be used in the curriculum of the PIs to train undergraduate andgraduate students, including under-represented minorities and femalestudents. The technical challenges addressed by the proposal include: 1)Scalable visualization of large and complex HEC networks so as toprovide a near instant rendering to end users, 2) A generalized datagathering scheme which is easily portable to multiple communicationfabrics, novel compute architectures and high-performance middleware,3) Enhanced data storage performance through optimized databaseschemas and the use of memory-backed key value stores/databases, 4)Support in MPI, PGAS, and Big Data libraries to enable the proposedmonitoring, analysis, and introspection framework, and 5) Enablingdeeper introspection of particular regions of application. Theresearch will also be driven by a set of HPC and Big Dataapplications. The transformative impact of the proposed research anddevelopment effort is to design a comprehensive analysis andperformance monitoring tool for applications of current and nextgeneration multi petascale/exascale systems to harness the maximumperformance and scalability.

随着计算、网络、异构硬件和存储技术在高端计算（HEC）平台中的不断发展，了解时间关键型高性能计算（HPC）和大数据应用程序之间的交互变得越来越重要，也越来越具有挑战性，大数据应用程序是实现高性能便携式解决方案所依赖的软件基础架构，这些高性能中间件所依赖的底层通信结构以及管理HPC群集的服务器。 这种理解将使所有相关方（应用程序开发人员/用户、系统管理员和中间件开发人员）能够最大限度地提高构成现代HPC系统的各个组件的效率和性能，并解决各种重大挑战问题。很明显，我们需要一种高性能和可扩展的工具，能够分析结构上的计算与HPC/大数据应用程序、底层中间件和现有大型HPC系统上的作业调度程序的行为，并将其关联起来。 拟议的协同和协作工作由来自OSU和OSC的计算机和计算科学家团队进行，旨在为HPC和大数据的高性能和可扩展的结构分析，监控和自省创建集成的软件基础设施。该工具将实现以下目标：1）可移植、易于使用和理解; 2）具有高性能和可扩展的渲染和存储技术; 3）适用于可能在现有大型HPC系统和新兴的exascale系统上使用的不同通信结构和编程模型。 拟议的研究和开发工作的变革性影响是为当前和下一代千万亿次/兆亿次系统的应用程序设计一个全面的分析和性能监控工具，以利用最大的性能和可扩展性。拟议的研究和相关的基础设施将对实现HPC和大数据应用程序的优化产生重大影响，这些优化以前很难提供。这些潜在的成果将通过使用建议的框架来验证多种场景下的HPC和大数据基准和应用程序来展示。 集成的中间件和工具将通过公共存储库和顶级论坛上的出版物向社区公开提供，使其他MPI和大数据堆栈能够采用这些设计。 研究结果也将传播给研究人员的合作组织，以影响他们的HPC软件产品和应用程序。拟议的研究方向及其解决方案将用于PI的课程，以培训本科生和研究生，包括代表性不足的少数民族和女学生。该提案涉及的技术挑战包括：1）大型和复杂HEC网络的可扩展可视化，以便向最终用户提供近乎即时的渲染，2）通用数据收集方案，其易于移植到多个通信结构、新颖的计算架构和高性能中间件，3）通过优化的数据库架构和使用存储器支持的键值存储/数据库来增强数据存储性能，4）支持MPI、PGAS和大数据库，以实现所提出的监控、分析和内省框架，以及5）实现对特定应用区域的更深入内省。 研究还将由一组HPC和大数据应用程序驱动。拟议的研究和开发工作的变革性影响是设计一个全面的分析和性能监控工具，用于当前和下一代多千兆/兆系统的应用程序，以利用最大的性能和可扩展性。

项目成果

A Scalable Network-Based Performance Analysis Tool for MPI on Large-Scale HPC Systems

大规模 HPC 系统上 MPI 的可扩展的基于网络的性能分析工具

• DOI: 10.1109/cluster.2017.78
• 发表时间: 2017
• 期刊: IEEE International Conference on Cluster Computing (CLUSTER
• 作者: Subramoni, Hari;Lu, Xiaoyi;Panda, Dhabaleswar K.
• 通讯作者: Panda, Dhabaleswar K.

C-GDR: High-Performance Container-Aware GPUDirect MPI Communication Schemes on RDMA Networks

• DOI: 10.1109/ipdps.2019.00034
• 发表时间: 2019-05
• 期刊: 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
• 作者: Jie Zhang;Xiaoyi Lu;Ching-Hsiang Chu;D. Panda

Designing a Profiling and Visualization Tool for Scalable and In-depth Analysis of High-Performance GPU Clusters

设计用于对高性能 GPU 集群进行可扩展和深入分析的分析和可视化工具

• DOI: 10.1109/hipc.2019.00022
• 发表时间: 2019
• 期刊: and Analytics (HiPC
• 作者: Kousha, Pouya;Ramesh, Bharath;Kandadi Suresh, Kaushik;Chu, Ching-Hsiang;Jain, Arpan;Sarkauskas, Nick;Subramoni, Hari;Panda, Dhabaleswar K.
• 通讯作者: Panda, Dhabaleswar K.

EC-Bench: Benchmarking Onload and Offload Erasure Coders on Modern Hardware Architectures

• DOI: 10.1007/978-3-030-32813-9_18
• 发表时间: 2018-12
• 作者: Haiyang Shi;Xiaoyi Lu;D. Panda

Accelerated Real-time Network Monitoring and Profiling at Scale using OSU INAM

使用 OSU INAM 加速实时网络监控和大规模分析

• DOI: 10.1145/3311790.3396672
• 发表时间: 2020
• 期刊: 2020 PEARC: Practice and Experience in Advanced Research Computing
• 作者: Kousha, P.;S. D., Kamal Raj;Subramoni, H.;Panda, D. K.;Na, H.;Dockendorf, T.;Tomko, K.
• 通讯作者: Tomko, K.