Development of an UltraScan Meta-Scheduler for HPC Job Submission

用于 HPC 作业提交的 UltraScan 元调度程序的开发

• 批准号: 10609259
• 负责人: Emre H. Brookes
• 金额: $ 24.19万
• 依托单位: UNIVERSITY OF MONTANA
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10609259
• 关键词: Address; Aging; Algorithms; Apache; Architecture; Australia; Biophysics; Canada; Charge; Collaborations; Communities; Computer software; Data; Data Analyses; Databases; Development; Disease; Drug Targeting; Environment; Finland; Germany; HIV; High Performance Computing; India; Indiana; Infrastructure; Institution; Letters; Libraries; Licensing; Linux; Maintenance; Malignant Neoplasms; Memory; Metadata; Molecular; Neurodegenerative Disorders; Occupations; Online Systems; Performance; Private Sector; Research; Research Personnel; Resolution; Resources; Running; Schedule; Scheme; Science; Source Code; Supercomputing; System; Time; Training; United States National Institutes of Health; Universities; Update; Wait Time; Work; analytical ultracentrifugation; base; biophysical analysis; cluster computing; code development; computing resources; cyber infrastructure; data acquisition; data exchange; data framework; drug testing; experimental study; improved; instrument; large datasets; middleware; novel; open data; open source; parallel computer; relational database; supercomputer; therapeutic development; tool; usability

Abstract UltraScan is a mature, open-source multi-platform analysis software for analytical ultracentrifugation (AUC) experiments. It includes a desktop GUI system, a relational database and a web-based LIMS server, a feature rich desktop environment, including a GMP data acquisition module, and a unique-to-the field high-performance parallel computing (HPC) component. This HPC component is the focus of this supplemental request. UltraScan has been implemented on high-performance computing (HPC) clusters to take advantage of parallel computing infrastructure. UltraScan has been fully integrated into the open source Apache Airavata framework, which manages the HPC executions and data transfers for UltraScan on nine different HPC resources across the world. It is operated by the Cyberinfrastructure Integration Research Center at Indiana University to provide a grid middleware layer for UltraScan and other collaborators. Apache Airavata enables access to shared installations of the HPC analysis software on supercomputers allocated by NSF/XSEDE and institutional clusters at several universities. New systems are continually integrated as they become available. The increasing number and diversity of available supercomputing platforms that have been integrated into the UltraScan system through Apache Airavata raise several new unmet challenges in usability and scalability which we propose to address with this supplement request. In Aim 1 we propose to extend the Apache Airavata architecture by developing a new meta-scheduler to abstract the job submission to any available HPC or cloud resource by automatically determining the best available cluster and queue, so users no longer have to know how to determine this by themselves. The scheduler will determine optimal availability for jobs submitted by the UltraScan community account and will automatically optimize the submission based on the job’s requirement for memory, compute time, and compute cores. The meta-scheduler will support dynamic resubmission of jobs in case a cluster goes into maintenance, or if queuing times become prohibitive. It will also be configurable, simplifying addition of new resources. In Aim 2 we will develop a resource requirement prediction microservice. The microservice will be initially trained on metadata from previous jobs stored in all UltraScan LIMS databases. The microservice will be dynamically updated as jobs complete. An API will be exposed and utilized by the meta-scheduler to receive job- and resource-specific maximum wall times. This will reduce queuing times and improve the utilization of oversubscribed resources.

摘要 UltraScan是一款成熟的开源多平台分析软件，用于分析超浓缩（AUC） 实验它包括一个桌面GUI系统、一个关系数据库和一个基于Web的LIMS服务器（一个功能 丰富的桌面环境，包括GMP数据采集模块和现场独有的 高性能并行计算（HPC）组件。此HPC组件是本补充说明的重点 请求过程中UltraScan已在高性能计算（HPC）群集上实施， 并行计算基础设施。UltraScan已完全集成到开源Apache Airavata中 框架，该框架在九种不同的HPC上管理UltraScan的HPC执行和数据传输 世界各地的资源。它由位于印第安纳州的网络基础设施集成研究中心运营 University为UltraScan和其他协作者提供网格中间件层。Apache Airavata使 访问NSF/XSEDE分配的超级计算机上HPC分析软件的共享安装， 几所大学的机构集群。新的系统不断地集成，因为它们变得可用。 越来越多的可用超级计算平台已经集成到 通过Apache Airavata的UltraScan系统在可用性和可扩展性方面提出了几个新的未满足的挑战 我们建议通过此补充请求来解决这个问题。在目标1中，我们建议扩展Apache Airavata架构，通过开发一个新的元调度器来将作业提交抽象到任何可用的HPC 通过自动确定最佳可用集群和队列， 知道如何自己判断。调度程序将确定作业的最佳可用性 提交的UltraScan社区帐户，并会自动优化提交的基础上， 作业对内存、计算时间和计算核心要求。元调度器将支持动态 在群集进入维护阶段或排队时间过长时重新提交作业。它将 还可以配置，简化了新资源的添加。在目标2中，我们将制定一项资源要求 预测微服务微服务最初将使用存储在所有 UltraScan LIMS数据库。微服务将在作业完成时动态更新。一个API将 由元调度器公开和使用，以接收作业和资源特定的最大壁时间。这将 减少排队时间并提高超额预订资源的利用率。