DEVELOPMENT OF SOFTWARE COMPONENTS TO SUPPORT PARALLEL ADAPTIVE MULTISCALE ANAL

开发支持并行自适应多尺度分析的软件组件

• 批准号: 8364223
• 负责人: FABIEN DELALONDRE
• 金额: $ 0.11万
• 依托单位: CARNEGIE-MELLON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8364223
• 关键词: Anus; Architecture; Biomedical Engineering; Biomedical Research; Charge; Complex; Computer software; Computers; Computing Methodologies; Development; Engineering; Equilibrium; Funding; Genes; Grant; High Performance Computing; International; Journals; Knowledge; Manuscripts; Mechanics; Methods; Modeling; Modification; National Center for Research Resources; Operating System; Postdoctoral Fellow; Principal Investigator; Reporting; Research; Research Infrastructure; Research Personnel; Resources; Running; Science; Scientist; Services; Slave; Source; Students; Supercomputing; Techniques; United States National Institutes of Health; Weight; Work; Writing; biological systems; cost; data management; experience; indexing; interest; models and simulation; parallel computer; programs; scale up; soft tissue; software development; supercomputer; web site

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. This work is part of a research effort of the Scientific Computation Research Center (SCOREC) which focuses on developing software components to support the parallel adaptive analysis of complex physical and biological systems. The two aims of this project are described as follows: Creation of a group of research dedicated to the development of parallel application to be solved on supercomputers: The group of researchers is made of a senior research associate, a CCNI computer scientist, three graduate and two undergraduate students. This group already has some experience in developing and running applications on a supercomputer made available to RPI researchers (CCNI: Blue Gene L architecture with 32,000 processors). The first aim of this project is to support the creation of a group of supercomputing experts who will be in charge of disseminating knowledge to other students and researchers through: The development of a website providing tutorials and promoting best practices to be used by all RPI students interested in research in supercomputing. Monthly seminars opened to all RPI students and researchers. Writing yearly report summarizing research progress. Implementation of a scalable multiscale hierarchic bioengineered application: Is supported by the development of a suite of parallel software components (Fields, Model, Domain, Error estimation, Adaptation) that are brought together to support the adaptive analysis of complex physical and biological systems [1]. Each software component, which supports a specific set of functionalities, is individually developed to support its execution on massively parallel computers. Such a strategy has been successfully implemented using a master/slave approach to model the simulation of bioengineered material using a hierarchic multiscale approach [2]. As the master/slave model is not well suited to scale on massively parallel computers, part of our on going research effort is to develop a new parallel multiscale paradigm efficiently combining: Multiscale hierarchic analysis using a solver that extends PHASTA strategy [3] which demonstrated scalability up to 300,000 processors by incorporating local instances of linear algebraic solvers [4]. Error estimation using a parallel version of the Zhu-Zienkewicz SPR technique [5]. Mesh [6] and model adaptations [7] that already demonstrated scaling up to 32,000 processors. Multiscale load balancing that consists of estimating the multiscale weights to be used by the mesh partitioner [8]. [1] F. Delalondre, C. Smith, M.S. Shephard, Collaborative software infrastructure to support adaptive multiple model simulation, Computer Methods in Applied Mechanics and Engineering, accepted manuscript, 2010. [2] X.-J. Luo, T. Stylianopoulos, V.H. Barocas, M.S. Shephard, Multiscale computation for bioartificial soft tissues with complex geometries, Engineering with Computers, Volume 25, Number 1, 87-95, [3] O. Sahni, C.D. Carothers, M.S. Shephard and K.E. Jansen, Strong Scaling Analysis of a Parallel, Unstructured, Implicit Solver and the Influence of the Operating System Interference, Scientific Programming, 17 (3), 261-274. [4] Portable, Extensible Toolkit for? Scientific Computation (PETSc), http://www.mcs.anl.gov/petsc/petsc-as/index.html [5] O. C. Zienkewicz and J. G. Zhu, A simple error estimator and adaptive strategy for practical engineering analysis, International Journal for Numerical Methods in Engineering, vol. 24, 1987, pp. 337-357. [6] F. Alauzet, X. Li, E.S. Seol, M.S. Shephard, Parallel anisotropic 3D mesh adaptation by mesh modification, Eng. Comput., 21 (2006) 247258 [7] M.A. Nuggehally, C.R. Picu, M.S. Shephard, Adaptive Model Selectionprocedure for Concurrent Multiscale Problems, Journal of Multiscale Computational Enginnering. 2007, (5), 369-386. [8] K. Devine, E. Boman, R. Heaphy, B. Hendrickson, C. Vaughan, ? Zoltan: Data Management Services for Parallel Dynamic Applications, Computing in Science and Engineering, ?2002, ?(4), ?2, ?90-97.

该副本是利用资源的众多研究子项目之一 由NIH/NCRR资助的中心赠款提供。对该子弹的主要支持 而且，副投影的主要研究员可能是其他来源提供的 包括其他NIH来源。 列出的总费用可能 代表subproject使用的中心基础架构的估计量， NCRR赠款不直接向子弹或副本人员提供的直接资金。 这项工作是科学计算研究中心（SCOREC）的研究工作的一部分，该研究重点是开发软件组件，以支持对复杂物理和生物系统的平行自适应分析。该项目的两个目标描述如下：创建一组专门用于开发超级计算机上的并行应用程序的研究：研究人员组成的一组高级研究助理，CCNI计算机科学家，三名毕业生和两名本科生。该小组已经在为RPI研究人员提供的超级计算机上开发和运行应用程序（CCNI：具有32,000个处理器的蓝色基因架构）方面具有一定的经验。该项目的第一个目的是支持创建一组超级计算专家，他们将通过：通过提供教程的网站的开发并促进所有对超级计算研究的RPI学生使用的网站的开发，并促进提供教程的网站并促进最佳实践。每月研讨会向所有RPI学生和研究人员开放。撰写年度报告总结了研究进度。实施可扩展的多尺度层次生物工程应用程序：通过开发一套并行软件组件（字段，模型，域，域，误差估计，适应性）来支持，这些组件（现场，模型，域，误差估计，适应性）支持对复杂物理和生物系统的自适应分析[1]。每个支持特定功能集的软件组件都可以单独开发，以支持其在大规模并行计算机上的执行。这种策略已通过主/从方法成功实施，以使用层次多尺度方法对生物工程材料进行模拟模拟[2]。由于主/从属模型不太适合大规模平行的计算机扩展，因此我们进行的研究工作的一部分是开发一种新的并行多尺度范式有效地结合：使用求解器扩展PHASTA策略[3]的多尺度层次分析[3]，该策略通过将固定性高达300,000个实例（通过将局部实例组合到Lineareal Algebra Algebraic Algebraic Algebraic Algebraic Algebraic [4]中。使用Zhu-Zienkewicz SPR技术的并行版本[5]的误差估计。网格[6]和模型适应[7]，已经证明了比例扩展32,000个处理器。多尺度负载平衡包括估计网格分区器要使用的多尺度权重[8]。 [1] F. Delalondre，C。Smith，M.S。 Shephard，协作软件基础架构，以支持自适应多个模型仿真，应用机械和工程中的计算机方法，接受的手稿，2010年。[2] X.-J. Luo，T。Stylianopoulos，V.H。Barocas，M.S。 Shephard，具有复杂几何形状的生物人工软组织的多尺度计算，具有计算机工程，第25卷，数字1，87-95，[3] O. Sahni，C.D。 Carothers，M.S。 Shephard和K.E.詹森（Jansen），对平行，非结构化的，隐式求解器的强大缩放分析以及操作系统干扰的影响，科学编程，17（3），261-274。 [4]便携式，可扩展的工具包？科学计算（PETSC），http：//www.mcs.anl.gov/petsc/petsc/petsc-as/index.html [5] O. C. Zienkewicz和J. G. 24，1987，第337-357页。 [6] F. Alauzet，X。Li，E.S。 Seol，M.S。 Shephard，平行各向异性3D网格通过网格修饰，Eng。 Comput。，21（2006）247258 [7] M.A. Nuggehally，C.R。Picu，M.S。 Shephard，同时多尺度问题的自适应模型选择程序，多尺度计算工程杂志。 2007，（5），369-386。 [8] K. Devine，E。Boman，R。Heaphy，B。Hendrickson，C。Vaughan，？ Zoltan：用于并行动态应用程序的数据管理服务，科学与工程中的计算，？2002，？（4），？2，？90-97。