CISE Research Instrumentation: Enabling Technology for High-Performance Heterogeneous Clusters Focusing on Grid Middleware, Fault Tolerance & Sparse Matrix Computations

9818334 Dongarra，Jack J.Plank，James S.田纳西大学启用高性能异构集群技术专注于网格中间件、容错和稀疏矩阵计算此研究工具支持以下方面的研究项目：- 利用集群资源进行分布式科学计算，-集群中的快速和便携式检查点和集群中的检查点，以及-集群上大规模稀疏矩阵应用的工具。为了支持上述项目，该奖项有助于购买32节点的高性能集群，一些交换机、工作站以及与田纳西大学现有集群和可视化实验室的接口。 由商用计算机和互连组装而成的高性能、低延迟集群显然是并行超级计算机的经济有效的替代方案。 但是，这些集群上的软件环境充其量也是原始的;显然需要用于应用程序开发和集群管理的工具。 本提案中的三个项目就是为了满足这一需要。 他们的共同目标是为大规模集群和异构集群集群上的高级科学计算应用开发使能技术。 建议的仪器是一个高性能的工作站网络集群。 该集群将连接到两个小集群（在该部门可用），以提供一个具有可视化功能的大型异构"集群集群“。 该集群并行平台将用于三个项目的算法、软件和工具开发研究。

9818334Dongarra, Jack J.Plank, James S.University of TennesseeEnabling Technology for High-Performance Heterogeneous Clusters Focusing on Grid Middleware, Fault Tolerance and Sparse Matrix ComputationsThis research instrumentation enables research projects in:- Harnessing Cluster Resources for Distributed Scientific Computing,- Fast and Portable Checkpointing in Clusters and Clusters-of-Clusters, and- Tools for Large-Scale Sparse Matrix Applications on Clusters.To support the aforementioned projects, this award contributes to the purchase of a 32-node high performance cluster, some switches, workstations, and interface to existing clusters and visualization lab atthe University of Tennessee. High-performance, low-latency clusters assembled from commodity computers and interconnects are clearly the cost-effective alternative to parallel supercomputers. But the software environment on such clusters is primitive at best; there is an obvious need of tools for application development and cluster management. The three projects in this proposal address this need. Their common goal is the development of enabling technology for advanced scientific computing applications on large-scale clusters and heterogeneous clusters-of-clusters. The proposed instrumentation is for a high-performance networked cluster of workstations. This cluster will be connected to two small clusters (available in the department) to provide a sizable, heterogeneous ``cluster-of-clusters'' with visualization capabilities. This cluster-of-clusters parallel platform will be used for algorithm, software and tool development research in the three projects.