EAGER: Innovative Methods for Computational Workflow Optimization

在这份热切的提案中，我们将探索将算法和代码与体系结构匹配的新方法，允许使用从分布式内存多核超级计算机到图形处理单元和高性能数据密集型计算设备的可用计算体系结构集合来高效执行大型复杂工作流。在这项探索性工作中，我们将重点放在计算机体系结构与计算需求的匹配以及对现有体系结构的不确定性量化方法的调整上。我们将首先使用我们的工具套件来记录计算、内存、数据和能源使用情况。然后将对这些数据进行分析和建模，以创建预测有效和高效使用的指导方针。我们在布法罗大学计算研究中心(CCR)和资源提供商的xD框架上可用的设施将使我们能够进行这项调查。

We will explore in this EAGER proposal new methodologies for matching algorithms and code to architecture, allowing the efficient execution of large complex workflows using an ensemble of available computing architectures ranging from distributed memory multi-core supercomputers to graphic processing units and high performance data intensive computing devices. In this exploratory work we will focus on the matching of computer architectures to computational needs and a tuning of the methodology of Uncertainty Quantification to the architectures at hand. We will first instrument our suite of tools to record computational, memory, data and energy usage. This data will then be analyzed and modeled to create guidelines for prediction of effective and efficient usage. The facilities available to us at the University at Buffalo, Center for Computation Research (CCR) and on the xD framework of resource providers will allow us to conduct this investigation.