Computer cluster for computational biology

描述（由申请人提供）： 本申请旨在建立用于计算生物学和生物信息学的计算机集群（CCBB）。该集群将由256个与Giganet交换机连接的双节点组成，以实现处理器之间的快速通信。该集群将使这两种方法的整合，并有可能有效地解决该领域的高要求的计算任务。它将为一小群研究人员提供服务，由NIH及其密切合作者提供支持。计算生物学和生物信息学应用以及本申请中列出的研究者团队的硬件需求可以总结如下： 1.强大的计算机能力，可进行复杂而昂贵的模拟。 2.为整个群集（共享）和（单独）单个节点提供大存储容量。 3.大型且可快速访问的内存，用于有效的统计分析，机器学习技术的应用和生物发现。 4.快速的网络信息更新在整个网络。 此外，CCBB将拥有高水平的数据库和软件集成，包括 1.更新共享数据库的重要“镜像”（如NR、swissprot、人类EST、人类基因组、蛋白质数据库等） 2.本地安装和频繁升级广泛使用的软件包（例如BLAST、Pfam、CHARMm等） 3.帮助移植新软件，以便在CCBB硬件平台上实现最佳使用。 计算生物学和生物信息学的最佳硬件和软件的联合统一将使新的集群成为NIH相关研究的杰出资源

DESCRIPTION (provided by applicant): The present application aims to establish a computer Cluster for Computational Biology and Bioinformatic (CCBB). The cluster will consists of 256 dual nodes connected with Giganet switches to enable rapid communication between the processors. The cluster will enable the integration of the two approaches and make it possible to effectively address the highly demanding computational tasks of the field. It will serve a small group of investigators, supported by the NIH, and their close collaborators. The hardware needs of computational biology and bioinformatic applications, and of the team of investigators listed in this application can be summarized as follows: 1. Significant computer power for complex and expensive simulations. 2. Large storage capacity for the whole cluster (shared) and (separately) for the individual nodes. 3. Large and rapidly accessible memory for effective statistical analysis, application of machine learning techniques, and biological discovery. 4. Fast network for information updates across the network. In addition CCBB will have high level of databases and software integration including 1. Updates of important "mirrors" of shared databases (such as NR, swissprot, human EST, human genome, protein databank, etc.) 2. Local installation and frequent upgrade of widely used software packages (e.g. BLAST, Pfam, CHARMm etc.) 3. Help in porting novel software for optimal use on the CCBB hardware platform. The combined unification of optimal hardware and software for computational biology and bioinformatic will make the new cluster; an outstanding resource for NIH related research