Experimental Software Systems: Integrated Applications and Systems Research in Software Shared Memory

[806751] singh, Jaswinder PalLi， KaiPrinceton大学实验软件系统：软件共享存储器中的集成应用和系统研究相干共享地址空间（SAS）是并行计算的一种有吸引力的编程模型。当在紧耦合多处理器的硬件上实现时，它正在取得商业上的成功。随着不太紧密耦合的工作站集群或对称多处理器成为重要的平台，在集群上的软件中支持该模型也很重要。否则，在两种类型的平台上运行应用程序的需求可能会导致更困难的显式消息传递模型占据主导地位。本研究在不同规模和组织的集群上开发和评估软件SAS系统。该研究的特点是高度应用驱动，包括与应用科学家的合作。它同时检查协议/系统和应用程序，而不将其中任何一个视为固定的。根据实际应用中遇到的瓶颈对协议和系统进行了改进，研究了硬件支持的作用，并比较了不同的软件方法。重点是编程的简易性和性能，包括在紧密耦合的系统和集群上比较SAS和消息传递模型。这项研究将产生新的、可扩展的软件SAS系统，将应用程序和系统研究更好地集成在一起，这是目前取得重大进展所必需的，并且可以更好地理解跨平台编程模型的权衡。

9806751Singh, Jaswinder PalLi, KaiPrinceton UniversityExperimental Software Systems: Integrated Applications and Systems Research in Software Shared MemoryA coherent shared address space (SAS) is an attractive programming model for parallel computing. It is emerging as commercially successful when implemented in hardware on tightly coupled multiprocessors. With less tightly coupled clusters of workstations or symmetric multiprocessors becoming important platforms, it is important to support this model in software on clusters as well. Otherwise, the need to run applications on both types of platforms may drive the more difficult explicit message passing model to dominate. This research develops and evaluates software SAS systems on clusters of various scales and organizations. The research is distinguished by being highly application-driven, including collaboration with application scientists. It examines protocols/systems and applications simultaneously without treating either as fixed. Protocols and systems are enhanced based on bottlenecks encountered in real applications, the role of hardware support is examined, and different software approaches are compared. The focus is on both programming ease and performance, and includes comparing the SAS and message passing models on tightly-coupled systems and clusters. The research will result in new, scalable software SAS systems, a greater integration of applications and systems research that is now necessary for major advances, and a better understanding of the tradeoffs in programming models across platforms.