High Performance Biomedical Computing And Informatics
高性能生物医学计算和信息学
基本信息
- 批准号:6675521
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:
- 资助国家:美国
- 起止时间:至
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
The goals of the High Performance Biomedical Computing and Informatics Program are to identify and solve those complex biomedical problems that can benefit from high performance computing and communications hardware, scientific database and Web technologies, data mining and visualization techniques, and modern software engineering principles and efficient algorithms. This effort includes the followings: (1) developing high performance computational methods and algorithms to analyze biomedical data and to simulate complex biological systems, (2) developing knowledge-based data management systems for the discovery of biomedical knowledge, including medical image repository and clinical information management systems, (3) providing high performance computing resources and software tools to NIH researchers, including special-purpose parallel computing machines, and (4) collaborating with NIH researchers and colleagues at other research centers in applying information technology to biomedical research problems. Using high performance parallel computing and knowledge-based data management systems, biomedical scientists can greatly reduce the time it takes to complete computationally intensive tasks, adopt new approaches for processing experimental data, and mine large complex datasets to find important data patterns. These may allow for the inclusion of more data in a calculation, the determination of a more accurate result, a reduction in the time needed to complete a long calculation, and the implementation of a new algorithm or more realistic model. High performance parallel computing allows biomedical scientists to analyze and study large datasets that cannot be processed within a practical amount of computer analysis time on conventional sequential or vector processing computing machines. With high bandwidth network connections and interactive user interfaces, parallel computing is readily accessible to a biomedical researcher in the laboratory or clinic at the investigator's computer workstation. Knowledge-based databases and data mining tools are powerful resources in modern biomedical research. They provide researchers ways to extract and use information from vast amounts of knowledge and data collected from wide-ranging sources such as public and commercial databases (e.g., genome sequences, chromosome, SNP, genetic diseases, EST cluster, gene mapping, gene expression, molecular biology, and literature databases). In addressing these complex data analysis and management challenges, the High Performance Computing and Informatics Office (HPCIO) of the Division of Computational Bioscience (DCB) in the Center for Information Technology (CIT) is developing high performance computing hardware and software infrastructure for a wide range of biomedical applications where computational speed, advanced data analysis, smart data mining and large-scale data management are important. These include gene expression data analysis, image processing of live-cell arrays, medical image and clinical information management, biostatistics, population genetics, and human genetic linkage analysis. The ultimate goal is to provide high performance parallel computing, scientific databases, and data analysis and visualization tools to facilitate the science that is done at the NIH. While developing the computationally demanding applications and scientific database systems, HPCIO is investigating the following challenging issues: (1) partitioning a problem into many parts that can be independently executed on different processors; (2) designing the parts so that the computing load can be distributed evenly over the available processors or dynamically balanced; (3) designing algorithms so that the number of processors is a parameter and the algorithms can be configured dynamically for the available machine; (4) developing tools and environments for producing portable parallel programs; (5) incorporating interactive data analysis and visualization tools into the user environment; (6) monitoring system performance; (7) proving that a parallel algorithm on a given machine meets its specifications; (8) evaluating modern parallel computer architectures for their performance characteristics on biomedical applications; and (9) developing and providing high performance database systems and data mining tools for archive and analysis of scientific data and medical images via Web-interfaces.
高性能生物医学计算和信息学计划的目标是识别和解决那些复杂的生物医学问题,这些问题可以受益于高性能计算和通信硬件、科学数据库和Web技术、数据挖掘和可视化技术以及现代软件工程原则和高效算法。这方面的努力包括:(1)开发高性能计算方法和算法来分析生物医学数据并模拟复杂的生物系统,(2)开发基于知识的数据管理系统来发现生物医学知识,包括医学图像存储库和临床信息管理系统,(3)向NIH研究人员提供高性能计算资源和软件工具,包括特殊用途的并行计算机器,以及(4)与NIH研究人员和其他研究中心的同事合作,将信息技术应用于生物医学研究问题。使用高性能并行计算和基于知识的数据管理系统,生物医学科学家可以大大减少完成计算密集型任务所需的时间,采用新的方法处理实验数据,挖掘大型复杂数据集以找到重要的数据模式。这些可以允许在计算中包括更多的数据,确定更准确的结果,减少完成长计算所需的时间,以及实现新算法或更现实的模型。高性能并行计算允许生物医学科学家分析和研究大型数据集,这些数据集无法在常规顺序或向量处理计算机器上的实际计算机分析时间内处理。随着高带宽网络连接和交互式用户界面,并行计算是很容易访问的生物医学研究人员在实验室或诊所在研究人员的计算机工作站。基于知识的数据库和数据挖掘工具是现代生物医学研究中的强大资源。它们为研究人员提供了从广泛的来源(如公共和商业数据库)收集的大量知识和数据中提取和使用信息的方法(例如,基因组序列、染色体、SNP、遗传疾病、EST簇、基因作图、基因表达、分子生物学和文献数据库)。为了应对这些复杂的数据分析和管理挑战,信息技术中心(CIT)计算生物科学部(DCB)的高性能计算和信息学办公室(HPCIO)正在为各种生物医学应用开发高性能计算硬件和软件基础设施,其中计算速度,高级数据分析,智能数据挖掘和大规模数据管理非常重要。这些包括基因表达数据分析,活细胞阵列的图像处理,医学图像和临床信息管理,生物统计学,群体遗传学和人类遗传连锁分析。最终目标是提供高性能并行计算,科学数据库,数据分析和可视化工具,以促进NIH的科学研究。HPCIO在开发高计算要求的应用程序和科学数据库系统时,正在研究以下具有挑战性的问题:(1)将问题划分为多个部分,这些部分可以在不同的处理器上独立执行;(2)设计这些部分,以便计算负载可以均匀地分布在可用的处理器上或动态地平衡;(3)设计算法,使处理器的数量成为一个参数,并且算法可以为可用的机器动态配置;(4)开发用于产生可移植并行程序的工具和环境;(5)将交互式数据分析和可视化工具纳入用户环境;(6)监控系统性能;(7)证明给定机器上的并行算法符合其规格;(8)评估现代并行计算机体系结构在生物医学应用中的性能特征;以及(9)开发和提供高性能数据库系统和数据挖掘工具,用于通过Web界面存档和分析科学数据和医学图像。
项目成果
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