SHF: Medium: Collaborative Research: Next-Generation Message Passing for Parallel Programming: Resiliency, Time-to-Solution, Performance-Portability, Scalability, and QoS

SHF：中：协作研究：并行编程的下一代消息传递：弹性、解决时间、性能可移植性、可扩展性和 QoS

• 批准号: 1562306
• 负责人: Sidharth kumar
• 金额: $ 39.79万
• 依托单位: University of Alabama at Birmingham
• 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: 2016-06-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1562306&HistoricalAwards=false
• 关键词: SHF; Medium; Collaborative; Research; Next

Parallel programming based on MPI is being used with increased frequency in academia, government (defense and non-defense uses), as well as emerging uses in scalable machine learning and big data analytics. Emerging supercomputer systems will have more faults and MPI needs to be able to workaround such faults to be appropriate to these emerging situations, rather than causing an entire application to fail. Collaborative, transformative message passing research for High Performance Computing (HPC) critical to performance-portable parallel programming in new and forthcoming scalable systems (with a strategy of "best practice-first, standardization-later") is being reduced to practice. A substantial subset of the Message Passing Interface (MPI-3/4) application programmer interface is being made fault tolerant through extensions with weak collective transactions that synchronize between parallel tasks. This research studies the novel model that localizes faults, provides tunable fault-free overhead, allows for multiple kinds of faults, enables hierarchical recovery, and is data-parallel relevant. Fault modeling of underlying networks is being studied. Application developers control the granularity and fault-free overhead in this effort. Performance and scalability results of the middleware prototype are being demonstrated principally through compact applications that relate to real use cases of practical and academic interest. The impact of this work ranges from users of the largest supercomputers in government labs to practical clusters that have long-running, time-critical applications, and to space-based and other parallel processing in "hostile" environments where faults occur more frequently than in past years. The project is producing usable free software that will be widely shared in the community as well as guidance on how better parallel programs can be written in academia, industry, and government. The project also provides guidelines for how to update existing or legacy programs to use the new capabilities that are being reduced to practice.

基于 MPI 的并行编程在学术界、政府（国防和非国防用途）以及可扩展机器学习和大数据分析中的新兴用途越来越频繁地使用。 新兴的超级计算机系统将出现更多故障，MPI 需要能够解决此类故障，以适应这些新兴情况，而不是导致整个应用程序失败。 针对高性能计算 (HPC) 的协作性、变革性消息传递研究对于新的和即将推出的可扩展系统中的性能可移植并行编程至关重要（采用“最佳实践优先，随后标准化”的策略）正在付诸实践。消息传递接口 (MPI-3/4) 应用程序编程器接口的一个重要子集正在通过在并行任务之间同步的弱集体事务的扩展来实现容错。这项研究研究了一种新颖的模型，该模型可以定位故障，提供可调的无故障开销，允许多种故障，支持分层恢复，并且与数据并行相关。 正在研究底层网络的故障建模。应用程序开发人员控制这项工作的粒度和无故障开销。中间件原型的性能和可扩展性结果主要通过与实际和学术兴趣的实际用例相关的紧凑应用程序来展示。这项工作的影响范围从政府实验室中最大的超级计算机的用户到具有长期运行、时间关键型应用程序的实际集群，以及“敌对”环境中的基于空间和其他并行处理，其中故障发生的频率比在环境中更频繁。历年。 该项目正在生产可用的免费软件，这些软件将在社区中广泛共享，并指导学术界、工业界和政府如何编写更好的并行程序。 该项目还提供了如何更新现有或遗留程序以使用正在付诸实践的新功能的指南。