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.

抽象的 UltraScan 是一款成熟的开源多平台分析软件，用于分析超速离心 (AUC) 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。 丰富的桌面环境，包括GMP数据采集模块，以及现场独有的 高性能并行计算 (HPC) 组件 此 HPC 组件是本补充的重点。 UltraScan 已在高性能计算 (HPC) 集群上实施，以利用这一优势。 UltraScan 已完全集成到开源 Apache Airavata 中。 框架，管理九种不同 HPC 上 UltraScan 的 HPC 执行和数据传输 它由印第安纳州网络基础设施集成研究中心运营。 大学为 UltraScan 和 Apache Airavata 的其他合作者提供网格中间件层。 访问由 NSF/XSEDE 分配的超级计算机上的 HPC 分析软件的共享安装，以及 几所大学的机构集群不断整合。 已集成到系统中的可用超级计算平台的数量和多样性不断增加 通过 Apache Airavata 的 UltraScan 系统在可用性和可扩展性方面提出了一些新的未解决的挑战 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 架构通过开发新的元调度程序将作业提交抽象到任何可用的 HPC 或云资源，自动确定最佳可用集群和队列，因此用户不再拥有 了解如何自行确定这一点 调度程序将确定作业的最佳可用性。 由UltraScan社区账户提交，并会根据提交情况自动优化提交 作业对内存、计算时间和计算核心的要求元调度器将支持动态。 如果集群进入维护状态，或者排队时间变得令人望而却步，则重新提交作业。 也可以配置，简化新资源的添加。在目标 2 中，我们将制定资源需求。 预测微服务最初将根据所有存储在之前作业中的元数据进行训练。 UltraScan LIMS 数据库将在作业完成时动态更新。 由元调度程序公开并利用它来接收特定于作业和资源的最大挂起时间。 减少排队时间，提高超额资源的利用率。