Washington University Institute of Clinical and Translational Sciences

Washington University, Saint Louis MO

Investigators

Linked publications & trials

Abstract

PROJECT SUMMARY/ABSTRACT The Institute of Clinical and Translational Sciences aligns with the CTSA goals of developing novel solutions to improve the efficiency, quality, and impact of translational science. The parent grant pursues five aims: Aim 1: Advance interdisciplinary programs to develop, promote, mentor, and retain highly qualified and diverse faculty, trainees, and staff who translate scientific discoveries into action, thereby becoming agents for change in their institutions and communities. Aim 2: Facilitate research designed for implementation by engaging diverse communities and stakeholders in multidisciplinary collaborative teams at all stages of the translational research process, thereby demonstrating the benefit and impact of translational science. Aim 3: Integrate research across individual lifespans and apply translational science in complex and diverse populations to improve individual and community health through meaningful research collaborations and sustainable partnerships. Aim 4: Drive innovation, quality, efficiency, and inclusion in translational research to enhance collaborations and catalyze the implementation of discovery science. Aim 5: Apply innovative informatics and biostatistics solutions to improve quality and efficiency at every stage of translational research, and create an ecosystem that integrates diverse data and facilitates the interoperability, use, and reuse of digital assets. This supplement will focus on the following aims: Aim 1: Deploy a hybrid-cloud HPC cluster to the public cloud that is seamlessly integrated with on- premises worker nodes and data storage. Aim 2: Run tests on a range of HPC workloads, including Data Simulations, Bioinformatics, Artificial Intelligence/Machine Learning & Imaging (GPU accelerator-focused), and general interactive workloads. We will compare the performance of workloads running on cloud and on-premises nodes, utilizing job run time and cost as the primary metrics. Aim 3: Evaluate the viability of deploying HPC clusters and nodes to the public cloud. We will complete this by examining the cost of running the workloads described in Aim 2 and comparing them to the projected costs of traditional on-premises approaches. The expected outcome of the supplement work is a comprehensive understanding to determine either what kinds of HPC workloads are more cost effective, or more performant in a hybrid-cloud HPC environment than a traditional on-premises deployment.