# Development of an UltraScan Meta-Scheduler for HPC Job Submission

> **NIH NIH R01** · UNIVERSITY OF MONTANA · 2022 · $241,872

## Abstract

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.

## Key facts

- **NIH application ID:** 10609259
- **Project number:** 3R01GM120600-07S1
- **Recipient organization:** UNIVERSITY OF MONTANA
- **Principal Investigator:** Emre H. Brookes
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $241,872
- **Award type:** 3
- **Project period:** 2016-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10609259

## Citation

> US National Institutes of Health, RePORTER application 10609259, Development of an UltraScan Meta-Scheduler for HPC Job Submission (3R01GM120600-07S1). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10609259. Licensed CC0.

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