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是用于分析超速离心（AUC）的成熟的开源多平台分析软件（AUC） 实验。它包括一个桌面GUI系统，关系数据库和基于Web的LIMS服务器，一个功能 丰富的桌面环境，包括GMP数据采集模块和独特的字段 高性能并行计算（HPC）组件。此HPC组件是此补充的重点 要求。 Ultrascan已在高性能计算（HPC）群集上实施以利用优势 并行计算基础架构。 Ultrascan已完全集成到开源Apache Airavata 框架，管理九个不同HPC上Ultrascan的HPC执行和数据传输 全世界的资源。它由印第安纳州的网络基础设施整合研究中心运营 大学为Ultrascan和其他合作者提供网格中间件层。 Apache Airavata启用 访问由NSF/XSEDE和 几所大学的机构集群。新系统随着它们的可用而不断集成。 已集成到该平台的可用超级计算平台的数量和多样性的增加 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满足挑战 我们建议通过此补充请求解决。在AIM 1中，我们建议扩展Apache Airavata架构通过开发一个新的元安装程序来将提交工作提交给任何可用的HPC 或通过自动确定最佳可用群集和队列来进行云资源，因此用户不再具有 知道如何自己确定这一点。调度程序将确定作业的最佳可用性 由Ultrascan Community帐户提交，并将根据 作业对内存，计算时间和计算核心的要求。元安排将支持动态 重新提交工作，以防群集进行维护，或者禁止排队时间。会 也可以配置，简化新资源的添加。在AIM 2中，我们将制定资源要求 预测微服务。微服务最初将对所有存储在所有人的工作中的元数据进行培训 Ultrascan Lims数据库。在作业完成后，微服务将动态更新。 API将是 由元安排者暴露和使用，以接收特定于工作和资源的最大墙壁时间。这会 减少排队时间并改善超认购资源的利用。