Development of an UltraScan Meta-Scheduler for HPC Job Submission

摘要 超声扫描是一个成熟的、开源的、多平台的分析软件，用于分析超速离心法(AUC)。 实验。它包括一个桌面图形用户界面系统、一个关系数据库和一个基于Web的LIMS服务器，这是一个功能 丰富的桌面环境，包括GMP数据采集模块和现场独一无二的 高性能并行计算(HPC)组件。此HPC组件是本补充文章的重点 请求。已在高性能计算(HPC)群集上实施了超声扫描，以利用 并行计算基础设施。已完全集成到开源的ApacheAiravata中 框架，它管理九种不同HPC上的HPC执行和超声波扫描的数据传输 世界各地的资源。它由印第安纳州的数字基础设施集成研究中心运营 大学为超音速扫描和其他合作者提供网格中间件层。阿帕奇Airavata支持 访问NSF/XSEDE分配的超级计算机上共享安装的HPC分析软件 几所大学的机构集群。新系统在可用时会不断地进行集成。 越来越多的可用超级计算平台已经集成到 ApacheAiravata的超声波扫描系统在可用性和可伸缩性方面提出了几个新的挑战 我们建议通过这项补充请求来解决这一问题。在目标1中，我们建议扩展APACHE Airavata架构，通过开发新的元调度器将作业提交抽象到任何可用的HPC 或云资源，通过自动确定最佳可用集群和队列，因此用户不再 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 由超级扫描社区帐户提交，并将根据 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 在群集进入维护状态或排队时间过长时重新提交作业。会的 也是可配置的，简化了新资源的添加。在目标2中，我们将制定资源需求 预测微服务。微服务最初将接受关于存储在 超微扫描LIMS数据库。微服务将在作业完成时动态更新。一个API将是 由元调度器公开和使用，以接收特定于作业和资源的最长工作时间。这将是 减少排队次数，提高超额订阅资源利用率。

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.