CISE Research Instrumentation

CISE 研究仪器

• 批准号: 9985991
• 负责人: Doug Burger
• 金额: $ 13.95万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-03-15 至 2003-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9985991&HistoricalAwards=false
• 关键词: CISE; Research; Instrumentation

EIA-9985991Doug BurgerUniversity of Texas-AustinCISE Research InstrumentationThe Department of Computer Science at the University of Texas will purchase a cluster of high performance, multigranular workstations, dedicated to support research in computer and information science and engineering. The equipment will initially be used to support four research projects in the department. The first project will use the cluster for detailed evaluations of future high-performance microprocessor designs. The second project will use the cluster to improve algorithms for reconstructing large evolutionary trees, with applications in evolutionary biology, pharmaceutical design, and linguistics. The third project will use the cluster to improve I/O -intensive data mining simulations using numerical techniques. The fourth project will develop software support for scientific cluster-based computing, including support for effective heterogeneous job scheduling, low-overhead application fault-tolerance, and run-time dynamic load balancing. The cluster will consist of heterogeneous machines, with varied quantities of memory, disk, and processors per machine. We will support several types of jobs submitted through a common interface: uniprocessor jobs, shared-memory parallel jobs, PVM message-passing jobs, and distributed-shared memory jobs. Our long-term goal is to build a centralized but scalable resource that can meet the needs of numerous scientific workloads concurrently.

EIA-9985991 Doug Burger德克萨斯大学奥斯汀分校CISE研究仪器德克萨斯大学计算机科学系将购买一组高性能、多粒度工作站，致力于支持计算机和信息科学与工程的研究。 这些设备最初将用于支持该部门的四个研究项目。 第一个项目将使用集群对未来的高性能微处理器设计进行详细评估。 第二个项目将使用集群来改进重建大型进化树的算法，并将其应用于进化生物学，药物设计和语言学。 第三个项目将使用集群来改进使用数值技术的I/O密集型数据挖掘模拟。 第四个项目将开发支持科学集群计算的软件，包括支持有效的异构作业调度、低开销应用程序容错和运行时动态负载平衡。 集群将由异构机器组成，每台机器的内存、磁盘和处理器数量各不相同。 我们将支持通过公共接口提交的几种类型的作业：单处理器作业、共享内存并行作业、PVM消息传递作业和分布式共享内存作业。 我们的长期目标是建立一个集中但可扩展的资源，可以同时满足众多科学工作负载的需求。