Amber: Programming Support for Networks of Multiprocessors

本研究涉及一个程序的设计与实现 用于在网络上构建高度并行应用的系统， 多处理器 基于多处理机的共享内存多处理机 正在变得广泛可用，并承诺提供成本效益 高性能计算。 多处理器工作站肯定会成为 在明年内广泛使用：人们希望它们均匀 对于许多人来说， 应用程序，就像当前的单处理器工作站往往更多 比单处理器主机更经济实惠。 简单地说，目标 这项研究的目的是提供一种设施， 多处理器，就好像它是一个大的，集成的，共享的内存 电脑 这项工作需要对操作系统的几个方面进行研究 结构 首先，搜索编程原语， 提供正确的抽象级别来表示并行 算法 在一个系统集成中， 并行性和分布性，提供跨 在不牺牲本地性能的情况下， 第二，必须 探索操作系统组织中的替代方案， 在单个多处理器节点上的性能。 这里的目标是 为并行执行提供最低的开销机制，以便 程序员可以表达和利用中等粒度的并行性。 第三、 必须开发工具，帮助程序员评估 他或她的应用程序的性能，并在适当优化它 对于联网的多处理器的环境。 这个项目是一个研究项目的自然结果， 分布式和并行计算系统中，我们一直在 在过去的八年里。 这项研究计划已经 由NSF支持下的各种奖项;它也收到了 来自工业界的大力支持，尤其是数字 Firefly原型多处理器 工作站将是我们的实验车辆。 (The DEC系统 研究中心为华盛顿、斯坦福大学、麻省理工学院、 普林斯顿、多伦多和剑桥，使这些大学独一无二 具备进行中等规模实验研究的条件 multiprocessing.） 我们已经获得的结果， 支持并行计算的初步工作正在积极使用中， 许多大学网站和公司，如Sequent，Digital， 微软和Tera。

This research concerns the design and implementation of a programming system for building highly parallel applications on networks of multiprocessors. Multiprocessor based shared memory multiprocessors are becoming widely available and promise to provide cost effective high performance computing. Multiprocessor workstations will surely be widely available within the next year: one expects them to be even more cost effective than their mainframe counterparts for many applications, just as current uniprocessor workstations often are more cost effective than uniprocessor mainframes. Simply stated, the goal of this research is to provide a facility for programming a network of multiprocessors as if it were a large, integrated, shared memory computer. This effort requires study of several aspects of operating system structure. First, there is a search for programming primitives that provide the right level of abstraction for expressing parallel algorithms. The important tradeoff, in a system integrating parallelism and distribution, is providing uniformity of access across the network without sacrificing local performance. Second, one must explore alternatives in operating system organization for increasing performance on a single multiprocessor node. The objective here is to provide the lowest overhead mechanisms for parallel execution so that programmers can express and utilize medium grained parallelism. Third, one must develop tools that aid the programmer in assessing the performance of his or her application, and in properly optimizing it for an environment of networked multiprocessors. This project is a natural outgrowth of a program of research in distributed and parallel computing systems in which we have been involved for the past 8 years. This program of research has been supported by NSF under a variety of awards; it also has received significant support from industrial sources, particularly Digital Equipment Corporation, whose Firefly prototype multiprocessor workstations will be our experimental vehicle. (The DECSystems Research Center has supplied Fireflys to Washington, Stanford, MIT, Princeton, Toronto, and Cambridge, making these universities uniquely well equipped to conduct experimental research on medium scale multiprocessing.) The results that we have already obtained in our preliminary work on support for parallel computing are in active use by many university sites and by companies such as Sequent, Digital, Microsoft, and Tera.