Data Distribution and Routing in Shared and Distributed Memory Parallel Computers

分布并行计算的数据，从而在没有内存库和互连网络冲突的情况下访问数据，是高效使用共享和分布式存储计算机的关键问题。存储体和互连网络中的冲突增加了处理器之间的通信延迟，这反过来又对并行计算的执行时间和并行计算机的性价比产生不利影响。这项研究探讨了硬件和软件解决方案，它们可以应用于并行体系结构的设计，以实现各种并行计算的一致高性能，并以透明的方式高效执行任务，即使在存在故障的情况下也是如此。在研究的第一部分，将扩展最近在硬件技术方面的工作，称为在任务执行时适用的倾斜存储方案。这些新技术将有助于高效地设计和使用容错和优雅降级的存储器、多维存储器和用于并行计算中的大型数据访问的存储器层次结构。这些技术的适用性将通过理论分析、在现有并行计算机上的实验和模拟来研究。在研究的第二部分，将研究在编译时适用的软件技术，以利用和辅助用于数据分区和分配的硬件技术，包括频繁访问和热点类型的数据。这些结果的有效性将通过实验进行评估。

Distribution of data of a parallel computation such that data are accessed free of memory bank and interconnection network conflicts, is a critical issue for efficient use of shared and distributed memory computers. Conflicts in memory banks and interconnection networks increase inter-processor communication delays, which in turn adversely affect the execution times of parallel computations and cost-performance ratio of parallel computers. This research investigates hardware and software solutions, which can be applied in the design of parallel architectures for consistent high performance for a variety of parallel computations and efficient execution of tasks in a transparent manner, even in the presence of faults. In the first part of the research, recent work on hardware techniques, called skewed storage schemes applicable at the time of task execution, will be expanded upon. The new techniques will be useful to design and use efficiently fault-tolerant and gracefully degrading memories, multidimensional memories, and memory hierarchies for large data accesses in parallel computations. Suitability of the techniques will be investigated with theoretical analysis, experiments on current parallel computers, and simulations. In the second part of the research, software techniques, applicable at compile-time, will be investigated to utilize and assist hardware techniques for partitioning and allocation of data, including heavily accessed and hotspot type data. Effectiveness of these results will be evaluated experimentally.