The quality of data layout greatly affects the performance of applications on distributed memory parallel computers.
在分布式并行机上，数据布局的质量极大的影响着应用程序的执行性能。
The method of message passing is widely used in some parallel computers, especially in distributed memory parallel computers.
消息传递方式是广泛应用于一些并行机，特别是分布式存储并行机的一种模式。
But in a parallel supercomputer with a sparse, distributed memory, the distinction between memory and processing fades.
在采用稀疏分布式内存的超级计算机里，记忆与数据处理之间的差异消失了。
This article shows you how to set up an in-memory grid of data, and then perform computation and data updates in a distributed and parallel manner across the grid.
本文介绍如何设置内存中数据网格，然后以分布式和并行方式跨网格执行计算和数据更新。
This paper starts with parallel computer of distributed memory, educe two kinds of parallel programming models-message transferring model and data paralleling model.
本文从分布式存储的并行计算机入手，引出了它的两种并行编程模型消息传递模型和数据并行模型。
Message passing programming is main paradigm in distributed memory systems with which high efficient and scalable parallel programs can be designed.
基于消息传递的程序设计模式是分布存储并行计算系统上设计高效，可扩展并行程序的主要模式。
Massively parallel Processing system (MPP) and PC cluster provide distributed-memory environments for parallel solving the generalized eigenvalue problem.
大规模并行处理系统(MPP)和PC机群为并行求解矩阵广义特征值问题提供了分布式存储环境。
Combined input and cross point queue have advantage due to relative independency. it can be more parallel due to its independent and distributed memory.
组合输入交叉缓存交换机以结构上存储的相对独立性而占有优势，能获得更高的并行性。
Combined input and cross point queue have advantage due to relative independency. it can be more parallel due to its independent and distributed memory.
组合输入交叉缓存交换机以结构上存储的相对独立性而占有优势，能获得更高的并行性。
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