Washington University Institute of Clinical and Translational Sciences

项目总结/摘要 临床和转化科学研究所与CTSA的目标一致，即开发新的解决方案， 提高转化科学的效率、质量和影响。 父母补助金追求五个目标： 目标1：推进跨学科项目，以发展、促进、指导和留住高素质和 不同的教师，学员和工作人员谁转化为行动的科学发现，从而成为 推动机构和社区的变革。 目标2：通过让不同社区参与，促进为执行而设计的研究， 在翻译研究过程的各个阶段的多学科合作团队的利益相关者， 从而证明了转化科学的益处和影响。 目标3：整合跨个人生命周期的研究，并在复杂和复杂的环境中应用转化科学。 通过有意义的研究改善个人和社区健康 合作和可持续的伙伴关系。 目标4：推动转化研究的创新、质量、效率和包容性， 合作和促进发现科学的实施。 目标5：应用创新的信息学和生物统计学解决方案， 转化研究阶段，并创建一个生态系统，整合不同的数据，并促进 数字资产的互操作性、使用和重用。 该补编将侧重于以下目标： 目标1：将混合云HPC集群部署到公共云，并与 本地工作节点和数据存储。 目标2：在一系列HPC工作负载上运行测试，包括数据模拟、生物信息学、人工智能、 智能/机器学习和成像（以GPU加速器为重点），以及通用交互式 工作量。我们将比较在云和本地节点上运行的工作负载的性能， 利用作业运行时间和成本作为主要度量。 目标3：评估将HPC集群和节点部署到公共云的可行性。我们将完成 通过检查目标2中描述的运行工作负载的成本，并将其与 传统内部部署方法的预计成本。 补充工作的预期成果是全面了解，以确定 在混合云HPC环境中， 传统的内部部署。

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.