SGER:Performance Analysis of the BlueGene Class of Machines via the ASTRO-FOLD Protein Structure Prediction Framework

SGER：通过 ASTRO-FOLD 蛋白质结构预测框架对 BlueGene 类机器进行性能分析

• 批准号: 0401635
• 负责人: Christodoulos Floudas
• 金额: --
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-15 至 2006-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0401635&HistoricalAwards=false
• 关键词: SGER; Performance; Analysis; BlueGene; Class

Abstract, CNS-0401635The project will port the Astro-Fold first principles methodology for protein structure prediction to the BlueGene class of machines with primary objectives: (i) to conduct a performance analysis of the BlueGene architecture, and (ii) to validate the architecture's unique features of concurrency between computation and communication.The project will develop parameter representation of the distributed system and will perform distributed computing studies with different number of nodes so as to assess and understand the performance of the network delays on the computations, the message passing and the communications overhead. The project will also investigate and assess the performance of the BlueGene class of machines not only on the individual levels of distributed computations (i.e, the helical prediction, the loop prediction, and the three-dimensional structure prediction), but also on the combined distributed computing performance of all levels and hierarchy in the distributed computations. This will be of major significance for the BlueGene class of machines and for the Astro-Fold methodology.

摘要，CNS-0401635该项目将把用于蛋白质结构预测的 Astro-Fold 第一原理方法移植到 BlueGene 类机器上，其主要目标是：(i) 对 BlueGene 架构进行性能分析，以及 (ii) 验证该架构的性能计算和通信之间的并发性的独特特征。该项目将开发分布式系统的参数表示，并对不同数量的节点进行分布式计算研究，以评估和了解网络延迟对计算、消息传递的性能以及通信开销。 该项目还将研究和评估 BlueGene 类机器的性能，不仅在分布式计算的各个级别（即螺旋预测、循环预测和三维结构预测）上，而且还在组合分布式计算上分布式计算中所有级别和层次的计算性能。 这对于 BlueGene 类机器和 Astro-Fold 方法具有重大意义。