CISE Research Instrumentation: Applications and Programming Environments for Cache-Coherent Distributed Memory Machines

9818308Singh, Jaswinder PalOstriker， JeremiahPrinceton大学缓存相干分布式内存机器的应用和编程环境该研究仪器支持以下研究项目：-缓存相干DSM架构上计算宇宙学的并行应用和编程环境，-缓存相干DSM架构上计算蛋白质结构确定的并行应用和编程环境，系统评估的并行工作负载及其对软件和硬件的影响。为了支持上述项目的综合应用驱动研究，该奖项将用于购买现有64处理器SGI Origin2000多处理器的升级版，由普林斯顿大学计算机科学和天体物理系与普林斯顿等离子体物理实验室共同完成。这些设备将用于继续和加强所列的研究项目，并支持计算机科学和等离子体物理实验室之间的新项目。第一个项目是计算机科学和天体物理学之间的合作，由于相互作用的模型和高度不规则的动态特性的结合，处理并行实现的问题尤其具有挑战性。目标是为一系列计算开发并行算法和实现，开发可推广和可移植的技术，在提供负载平衡的同时保持局部性，扩展现有的并行语言；使用这些语言的运行时系统，构建分区技术和算法库。第二个项目是CS和斯坦福大学医学院的合作项目，目标相似，模拟了结构生物学固有的缓慢过程。最后一个项目将扩大可用的工作负载集。在关键的新兴应用领域中，并行应用程序的SPLASH套件可以作为架构研究的基准，以及编程语言和编译器的测试平台。

9818308Singh, Jaswinder PalOstriker, JeremiahPrinceton UniversityApplications and Programming Environments for Cache-Coherent Distributed Memory MachinesThis research instrumentation enables research projects in:- Parallel Applications and Programming Environments for Computational Cosmology on Cache-Coherent DSM Architectures,- Parallel Applications and Programming Environments for Computational Protein Structure Determination on Cache-Coherent DSM Architectures, and- Parallel Workload for System Evaluation and their Implications for Software and Hardware.To support integrated application-driven research in the aforementioned projects, this award contributes to the purchase of an upgrade to an existing 64-processor SGI Origin2000 multiprocessor by the Departments of Computer Science and Astrophysics together with the Princeton Plasma Physics Laboratory at Princeton University. The equipment will be used to continue and enhance the research projects listed and to support new projects between computer science and the Plasma Physics Laboratory. The first project, a collaboration between Computer Science and Astrophysics, deals with problems particularly challenging for parallel implementation due to the combination of interacting models and a highly irregular dynamic nature. The goals are to develop parallel algorithms and implementations for a range of computations, to develop generalizable and portable techniques preserving locality while providing load balance, to extend existing parallel languages; use runtime systems of these languages, to build libraries of partitioning techniques andalgorithms. The second project, a collaboration between CS and the School of Medicine at Stanford University, with similar goals, simulates inherently slow processes in structural biology. The last project will broaden the set of workloads available. The SPLASH suites of parallel applications in critical emerging application areas serve as benchmarks for architectural studies, as well as testbeds for programming languages and compilers.