ITR: Optimizing Execution of Parallel Programs on a Cluster of Shared Memory Machines

ITR：优化共享内存机器集群上并行程序的执行

• 批准号: 0082666
• 负责人: Tao Yang
• 金额: $ 21.11万
• 依托单位: University of California-Santa Barbara
• 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: 2000-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0082666&HistoricalAwards=false
• 关键词: ITR; Optimizing; Execution; Parallel; Programs

Parallel programs using MPI are widely used in compute-intensive scientific and engineering applications. They perform well on dedicated distributed memory machines and workstation clusters. However, their performance can deteriorate on multiprogrammed shared memory machines (SMMs) or clusters of those machines. This project will optimize execution of parallel programs through both program transformation and efficient run-time support. The resulting programs will deliver robust performance in both dedicated and multiprogrammed SMM clusters.Technically, the work has three aspects:It will study compile-time code transformations to achieve threaded execution of parallel code on a cluster of SMMs, allowing each MPI node to be executed safely as a thread.It will study thread-safe run-time execution and fast lock-free communication that takes advantage of address space sharing among threads within an SMM.It will evaluate and model a variety of scientific applications (including sparse-matrix algorithms with irregular computation, PDE computations with coarse-grain computation, and data-intensive applications) to verify the proposed techniques.

使用MPI的并行程序广泛用于计算密集型科学和工程应用。它们在专用的分布式内存机器和工作站集群上表现良好。然而，它们的性能可能会在多程序共享内存机器（SMM）或这些机器的集群上恶化。该项目将通过程序转换和有效的运行时支持来优化并行程序的执行。由此产生的程序将在专用和多编程SMM集群中提供强大的性能。从技术上讲，这项工作有三个方面：它将研究编译时代码转换，以实现SMM集群上并行代码的线程执行，允许每个MPI节点作为线程安全地执行。它将研究线程安全运行时执行和快速无锁通信，利用SMM中线程之间的地址空间共享。它将评估和模拟各种科学应用程序（包括稀疏矩阵算法与不规则计算，偏微分方程计算与粗粒度计算，和数据密集型应用），以验证所提出的技术。