Collaborative Research: High Performance Parallel Processing: Fault Tolerant Computing on a Network of Workstations

这个项目涉及并行处理的理论和原型研究，以利用工作站集群的力量。 主要的技术是基于主动调度、回避内存和分散数据管理。 正式的工作扩展了以前的结果的PI在异步计算在几个方向： (1)将模型扩展到细粒度程序的模型。 (2)允许将体系结构的特定特征纳入理论框架。 (3)区分由超时、中断和故障（或计算的无限期推迟）引起的不同处理器所看到的内存状态不一致的技术。(4)允许非确定性执行的技术。 (5)对容错程序的输入/输出操作建模。 该项目的实验部分将建立一个缩放版本，在工作站集群上执行并行程序。 该原型将解决与线程的急切调度，回避内存和数据管理的实现有关的问题。 原型将试图从效率的角度评估环境的有效性。 该项目由亚利桑那州立大学的P. Dasgupta和Z. Kedem，来自纽约大学。

This project deals with both theoretical and prototyping research in parallel processing to harness the power of workstation clusters. The primary techniques will based on aggressive scheduling, evasive memory and dispersed data management. The formal work expands the previous results of the PIs in asynchronous computing in several directions: (1) Extend the models to models of fine-grained programs. (2) Permit the inclusion of architecture specific characteristics into theoretical frameworks. (3) Techniques to distinguish inconsistencies in memory states as viewed by different processors resulting from time-outs, interrupts and failures (or indefinite postponement of a computation). (4) Techniques to allow non-deterministic executions. (5) Model input/output operations of fault-tolerant programs. The experimental part of the project will build a scaled version to execute parallel programs on clusters of workstations. The prototype will address issues related to the eager scheduling of threads, the implementation of evasive memory and data management. The prototype will attempt to evaluate the effectiveness of the environment in terms of efficiency. This project is conducted jointly by P. Dasgupta of Arizona State University and Z. Kedem, of New York University.