Experimental Evaluation of Software Coherence for Next-Generation Networks

在商用硬件上高效地支持共享内存是广泛接受并行计算的最大挑战之一。一致性是核心问题：大规模系统中的数据必须被复制，并保存在使用它们的处理器附近，但更改必须传达给所有副本。这项研究通过软件和低成本硬件的独特组合，并通过积极整合“行为驱动”一致性协议和基于编译器的程序分析来解决这一问题。将研究的应用包括与经济学、生物学、放射学、遗传学、数据挖掘、计算机视觉和虚拟现实领域的研究人员合作。研究的硬件基础是根据与Digital Equipment Corporation的合作协议购买的8个多处理器(总共32个处理器)的集群，并通过DEC新的内存通道网络连接。通过直接访问远程内存，该网络允许处理器交互的速度比同类系统快两个数量级。软件一致性协议可以利用这种快速通信来维护目录、实现同步并避免中断远程处理器的工作。这项研究的最终目标是在商用机器网络上获得传统应用和新兴应用的超级计算机性能。

Efficiently supporting shared memory on top of commodity hardware is one of the greatest challenges to widespread acceptance of parallel computing. Coherence is the central problem: data in a large-scale system must be replicated and kept near to the processors that use them, but changes must be communicated to all of the copies. This research addresses this problem through a distinctive combination of software and low-cost hardware, and through an active integration of "behavior-driven" coherence protocols with compiler-based program analysis. Applications to be studied include collaborative work with researchers in Economics, Biology, Radiology, Genetics, data mining, computer vision, and virtual reality. The hardware base for the research is a cluster of eight multiprocessors (32 processors total) acquired under a collaborative agreement with Digital Equipment Corporation, and connected by DEC's new Memory Channel network. By directly accessing remote memory, the network allows processors to interact two orders of magnitude faster than they can in comparable systems. Software coherence protocols can exploit this fast communication to maintain directories, implement synchronization, and avoid interrupting the work of remote processors. The ultimate goal of the research is to obtain supercomputer performance for both traditional and emerging applications on networks of commodity machines.