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

基本信息

  • 批准号:
    0401635
  • 负责人:
  • 金额:
    --
  • 依托单位:
  • 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

项目摘要

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该项目将蛋白质结构预测的 Astro-Fold 第一原理方法移植到 BlueGene 类机器上,主要目标是:(i)对 BlueGene 架构进行性能分析,以及(ii)验证该架构计算和通信之间并发性的独特功能。该项目将开发分布式系统的参数表示,并将使用不同数量的节点进行分布式计算研究,以评估 并了解网络延迟对计算、消息传递和通信开销的性能。 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,而且还将在分布式计算中所有级别和层次结构的综合分布式计算性能上进行研究和评估。 这对于 BlueGene 类机器和 Astro-Fold 方法具有重大意义。

项目成果

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Christodoulos Floudas其他文献

Christodoulos Floudas的其他文献

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{{ truncateString('Christodoulos Floudas', 18)}}的其他基金

EAGER: Towards Multiscale Modeling, Optimization, and Uncertainty in Materials Design for CO2 Capture
EAGER:二氧化碳捕获材料设计中的多尺度建模、优化和不确定性
  • 批准号:
    1263165
  • 财政年份:
    2013
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
Novel Optimization Methods for Design, Synthesis, Supply Chain, and Uncertainty of Hybrid Biomass, Coal, and Natural Gas to Liquids, CBGTL, Processes
用于混合生物质、煤炭和天然气液化、CBGTL、工艺的设计、合成、供应链和不确定性的新颖优化方法
  • 批准号:
    1158849
  • 财政年份:
    2012
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
Integrated Framework for Operational Planning and Scheduling Under Uncertainty
不确定性下的运营规划和调度综合框架
  • 批准号:
    0856021
  • 财政年份:
    2009
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
CDI-Type II: MS-Omics Hub for Cyber-enabled Acceleration of Mass Spectrometry-based Metabolomics and Proteomics
CDI-Type II:MS-Omics 中心,用于网络加速基于质谱的代谢组学和蛋白质组学
  • 批准号:
    0941143
  • 财政年份:
    2009
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
Novel Methods and Computational Studies for Global Optimization
全局优化的新方法和计算研究
  • 批准号:
    0827907
  • 财政年份:
    2008
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
GOALI: Short-term Scheduling Under Uncertainty: A Robust Optimization Framework
GOALI:不确定性下的短期调度:鲁棒优化框架
  • 批准号:
    0355336
  • 财政年份:
    2004
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
FOCAPD 2004 Conference: Discovery through Product and Process Design
FOCAPD 2004 会议:通过产品和工艺设计进行发现
  • 批准号:
    0355399
  • 财政年份:
    2004
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
ITR: Collaborative Research: (ASE+NHS+EVS)-(sim+dmc+int): In Silico De Novo Protein Design: A Dynamically Data Driven, (DDDAS), Computational and Experimental Framework
ITR:协作研究:(ASE NHS EVS)-(sim dmc int):计算机从头蛋白质设计:动态数据驱动、(DDDAS)、计算和实验框架
  • 批准号:
    0426691
  • 财政年份:
    2004
  • 资助金额:
    --
  • 项目类别:
    Continuing Grant
Improved Convex Underestimators and Hybrid Methods for Deterministic Global Optimization
用于确定性全局优化的改进凸低估器和混合方法
  • 批准号:
    0330541
  • 财政年份:
    2003
  • 资助金额:
    --
  • 项目类别:
    Standard Grant
QSB: Computational and Experimental Studies of Pathways in Yeast
QSB:酵母途径的计算和实验研究
  • 批准号:
    0222471
  • 财政年份:
    2002
  • 资助金额:
    --
  • 项目类别:
    Continuing Grant

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