EAGER: Innovative Methods for Computational Workflow Optimization

EAGER：计算工作流程优化的创新方法

• 批准号: 1118260
• 负责人: Abani Patra
• 金额: $ 26.73万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1118260&HistoricalAwards=false
• 关键词: EAGER; Innovative; Methods; Computational; Workflow

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.

我们将在这个 EAGER 提案中探索将算法和代码与架构相匹配的新方法，允许使用一系列可用的计算架构（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）高效执行大型复杂工作流程设备。在这项探索性工作中，我们将重点关注计算机架构与计算需求的匹配，以及根据现有架构调整不确定性量化方法。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南。布法罗大学计算研究中心 (CCR) 和资源提供者的 xD 框架提供给我们的设施将使我们能够进行这项调查。