Transputer及多机并行处理系统结构分析

分析了Ｔｒａｎｓｐｕｔｅｒ和基于Ｔｒａｎｓｐｕｔｅｒ网络加速板的多机并行处理系统的结构，着重说明了Ｔｒａｎｓｐｕｔｅｒ如何支持并发进程的调度和通信。最后指出为获得Ｔｒａｎｓｐｕｔｅｒ多机系统并行处理的高性能，并行程序设计应避免并发过程死锁和选取最佳任务粒度。     

基于Transputer的分布并行处理机的通信与任务分解

本文基于Ｍｕｌｔｉ－Ｔｒａｎｓｐｕｔｅｒ并行处理系统的结构特点，分析了Ｔｒａｎｓｐｕｔｅｒ网的通信通信及任务通信开销对作业运行效率的制约关系。结果表明：并行程序设计的作业分解方式、选取合理的任务粒度和Ｔｒａｎｓｐｕｔｅｒ网络拓扑是其影响通信开销和作业运行效率的主要原因。     

Transnuter并行处理系统的任务粒度临界值

本文对基于Ｔｒａｎｓｐｕｔｅｒ的多机并行处理系统，提出在两种情况下任务粒度临界值的计算表达式，并给出一个在Ｔｒａｎｓｐｕｔｅｒ多机系统上的并行Ｃ语言编程示例，说明如何平衡任务并行性和通信开销以减少作业运行时间。     

Transputer并行处理系统的任务粒度临界值

本文对基于Ｔｒａｎｓｐｕｔｅｒ的多机并行处理系统，提出在两种情况下任务粒度临界值的计算表达式，并给出一个在Ｔｒａｎｓｐｕｔｅｒ多机系统上的并行Ｃ语言编程示例，说明了如何平衡任务并行性和通信开销以减少作业运行时间。     

多Transputer模拟研究

ＴＤＳ是借助于微机环境提供给用户的一个用于开发Ｔｒａｎｓｐｕｔｅｒ并发程序的程序开发集成环境，本文介绍了一个笔者在该环境下利用二个Ｔｒａｎｓｐｕｔｅｒ和Ｏｃｃａｍ语言实现的多Ｔｒａｎｓｐｕｔｅｒ的模拟环境。     

容错多Transputer体系结构

本文提出了一种容错的多Ｔｒａｎｓｐｕｔｅｒ的体系结构。该体系结构采用并行处理芯片Ｔｒａｎｓｐｕｔｅｒ作为基本处理单元，利用多Ｔｒａｎｓｐｕｔｅｒ并行处理系统的并行性、可拓扑性，用软件实现了带一个后援备份的三倍任务仿作的动态混合冗余，达到了高可靠性的目标。在保证高可靠性的前提下，该体系结构还使资源的消耗最少。该体系结构能连续容忍系统处理单元的单故障，并自动地启动后援备份进行重构，具有很高的可靠性、实     

分布式并行计算机系统在污水处理厂中的应用

本文首先描述了该分布式并行计算机系统的结构,基于Ｔｒａｎｓｐｕｔｅｒ的ＰＬＣ是其中的核心单元。由于Ｔｒａｎｓｐｕｔｅｒ处理器的强大功能,该系统具有较强的并行处理能力和通信网络能力,以及较高的稳定性。     

一种包含Transputer的异构型分布式并行计算系统的架构

本文针对ＰＶＭ不支持Ｔｒａｎｓｐｕｔｅｒ的不足，介绍了基于ＴＣＰ／ＩＰ的Ｔｒａｎｓｐｕｔｅｒ异构型分布式并行计算系统（Ｔ－ＤＰＣＳ）的软硬件架构。从通信协议的选择、Ｔｒａｎｓｐｕｔｅｒ共享支持软件、分布式协同软件架构和通信函数库的实现等４个方面详细地阐述了实现ＴＤＰＣＳ的方案，并进行了原型实现。     

适合于多种并行计算结构的TRANSPUTER互连结构：HCCTT

本文将提出一种用于Ｔｒａｎｓｐｕｔｅｒ多机系统的Ｈａｍｉｌｔｏｎ回路连接三元树互连结构，并给出其构造算法。这种结构的Ｔｒａｎｓｐｕｔｅｒ多机系统在其通信服务程序的支持下，可以很好地适应扇型，星型，环和超环等多种并行结构的计算，并且系统的并行处理规模可扩充性强。     

多Transputer系统上的并行消隐算法及其图形显示

本文讨论了三维物体隐面消除的并行处理问题。给出了一类ＭＩＭＤ并行深度缓冲器算法，并在多Ｔｒａｎｓｐｕｔｅｒ系统上实现。文中还对这些算法的效率进行了比较。     

多源定量遥感产品并行处理系统设计及实现*

针对遥感数据非结构化、有格式、单景数据量大、记录总量小、大部分处理过程后数据量不显著减少的特点和大规模遥感数据处理的迫切需求,基于已有硬件资源,设计和实现了集中式集群计算的多源定量遥感产品生产系统。系统为解决遥感科学工作者设计开发的算法进行并行计算的问题以及大规模数据管理的需求,设计了基于任务并行的处理系统和面向应用的并行文件系统。系统集成了遥感科学工作者开发的30多种原始数据的预处理算法和50多种多源定量遥感产品生产算法,解决了多源定量遥感产品规模化流程化按需生产的难题,并进行了产品生产,证明了系统在管理和处理大规模遥感数据时的可靠性和有效性。     

Analysis of a model for parallel image processing

An increasing awareness of the need for high speed parallel processing systems for image analysis has stimulated a great deal of interest in the design and development of such systems. Efficient processing schemes for several specific problems have been developed providing some insight into the general problems encountered in designing efficient image processing algorithms for parallel architectures. However it is still not clear what architecture or architectures are best suited for image processing in general, or how one may go about determining those which are. An approach that would allow application requirements to specify architectural features would be useful in this context. Working towards this goal, general principles are outlined for formulating parallel image processing tasks by exploiting parallelism in the algorithms and data structures employed. A synchronous parallel processing model is proposed which governs the communication and interaction between these tasks. This model presents a uniform framework for comparing and contrasting different formulation strategies. In addition, techniques are developed for analyzing instances of this model to determine a high level specification of a parallel architecture that best ‘matches’ the requirements of the corresponding application. It is also possible to derive initial estimates of the component capabilities that are required to achieve predefined performance levels. Such analysis tools are useful both in the design stage, in the selection of a specific parallel architecture, or in efficiently utilizing an existing one. In addition, the architecture independent specification of application requirements makes it a useful tool for benchmarking applications.     

An Analytical Method for Predicting the Performance of Parallel Image Processing Operations

This paper presents an analytical performance prediction model and methodology that can be used to predict the execution time, speedup, scalability and similar performance metrics of a large set of image processing operations running on a p-processor parallel system. The model which requires only a few parameters obtainable on a minimal system can help in the systematic design, evaluation and performance tuning of parallel image processing systems. Using the model one can reason about the performance of a parallel image processing system prior to implementation. The method can also support programmers in detecting critical parts of an implementation and system designers in predicting hardware performance and the effect of hardware parameter changes on performance. The execution of parallel image processing operations was studied and operations were arranged in three main problem classes based on data locality and the communication patterns of the algorithms. The core of the method is the derivation of the overhead function, as it is the overhead that determines the achievable speedup. The overheads were examined and modelled for each class. The use of the method is illustrated by four class-representative image processing algorithms: image-scalar addition, convolution, histogram calculation and the Fast Fourier Transform. The developed performance model has been validated on a 16-node parallel machine and it has been shown that the model is able to predict the parallel run-time and other performance metrics of parallel image processing operations accurately.     

A template polynomial approach for image processing and visual recognition

A polynomial approach to the representation of gray images for machine vision is described. An algebraic system is developed where a polynomial in two variables with real coefficients represents a gray image and it is shown that most of the standard image processing tasks like smoothing, edge detection, rotation and magnification can be done by operating certain polynomials called template polynomials. This method is also applied to connected component labelling, shape decomposition, template matching, and the skeletonization of a gray image without a priori thresholding. A technique is developed to decompose a template and do parallel processing.     

Visualization of large data sets with the Active Data Repository

We implement ray-casting-based volume rendering and isosurface rendering methods using the Active Data Repository (ADR) for visualizing out-of-core data sets. We have developed the ADR object-oriented framework to provide support for applications that employ range queries with user-defined mapping and aggregation operations on large-scale multidimensional data. ADR targets distributed-memory parallel machines with one or more disks attached to each node. It is designed as a set of modular services implemented in C++, which can be customized for application-specific processing. The ADR runtime system supports common operations such as memory management, data retrieval, and scheduling of processing across a parallel machine     

Identification- and Elimination-Based Parallel Query Processing Techniques for Object-Oriented Databases

The newly developed object-oriented database management systems provide rich facilities for the modeling and processing of structural as well as behavioral properties of complex application objects. However, due to their inherent generality, new functionalities to be added to these systems as they continue to evolve, and high performance demand in many application domains, efficient parallel algorithms and architectures would be needed to meet the performance requirement for processing large OODBs. In our previous work, we have shown that processing OODBs can be viewed as the manipulation of patterns of object associations. In this paper, we present several parallel, multiwavefront algorithms based on two approaches, i.e., identification and elimination approaches, to verify association patterns specified in queries. Both approaches allow more processors to operate concurrently on a query than the traditional tree-structured query processing approach, thus introducing a higher degree of parallelism in query processing. We present a graph model to transform the query processing problem into a graph problem. Based on the graph model, proofs of correctness of both approaches for tree-structured queries are given, and a combined approach for solving cyclic queries is also provided. We present a new data structure to represent associations between objects, parallel algorithms based on these approaches, and some evaluation results obtained from an actual implementation of these algorithms on an nCUBE 2 parallel computer.     

面向并行图像处理的实时分布式操作系统的设计与实现

针对多片TMS320C64 DSP构成的多计算机体系结构的实时图像识别系统，设计并实现了面向并行图像处理的实时分布式操作系统PIPORTDOS（Parallel image processing-oriented real-time distributed operating system）．PIPORTDOS基于微内核体系结构，包括硬件抽象层、系统核心层、分布式消息通信机制和系统服务层四个层次．其多任务内核实现了基于优先级的抢先式调度、任务闻的同步和通信原语、实时的中断处理以及面向应用的缓存管理机制．为了实现对分布式并行图像处理的支持，PIPORTDOS采用了基于消息传递（Message Passing）的方式，并在实现中充分考虑了上层图像处理算法的应用需求以及DSP的硬件功能．相关性能指标表明，本文设计实现的PIPORTDOS完全可以满足系统的强实时性要求．在功能上也能适应算法对不同并行结构的需求．     

A parallel solution for high resolution histological image analysis

This paper describes a general methodology for developing parallel image processing algorithms based on message passing for high resolution images (on the order of several Gigabytes). These algorithms have been applied to histological images and must be executed on massively parallel processing architectures. Advances in new technologies for complete slide digitalization in pathology have been combined with developments in biomedical informatics. However, the efficient use of these digital slide systems is still a challenge. The image processing that these slides are subject to is still limited both in terms of data processed and processing methods. The work presented here focuses on the need to design and develop parallel image processing tools capable of obtaining and analyzing the entire gamut of information included in digital slides. Tools have been developed to assist pathologists in image analysis and diagnosis, and they cover low and high-level image processing methods applied to histological images. Code portability, reusability and scalability have been tested by using the following parallel computing architectures: distributed memory with massive parallel processors and two networks, INFINIBAND and Myrinet, composed of 17 and 1024 nodes respectively. The parallel framework proposed is flexible, high performance solution and it shows that the efficient processing of digital microscopic images is possible and may offer important benefits to pathology laboratories.     

Algorithms for asynchronous parallel processing of object-orienteddatabases

Management of large quantities of complex data is essential in many advanced application areas. Object-oriented (OO) database management system have been developed to effectively model and process the complex domain knowledge. They have been shown to outperform some existing relational systems. The existing implementations of OO database management systems attempt to improve the efficiency of OO queries by explicitly capturing the relationships among objects. However, the execution of complex queries involving the retrieval of objects from many classes and relationships among them causes the existing system to operate inefficiently. In this paper, we present parallel algorithms for the processing of queries against a large OO database. The algorithms are based on a closed model of query processing pattern-based access instead of the conventional value-based access. During processing, the algorithms avoid the execution of time-consuming join operations by making use of the explicitly stored object associations. Generation of large quantities of temporary data is avoided by marking objects using their identifiers and by employing a two-phase query processing strategy. A query is processed by concurrent multiple waves, thereby improving parallelism avoiding the complexities introduced in their sequential implementation. The correctness and the performance of the parallel algorithms have been tested and analyzed by running parallel programs on a 32-node transputer based parallel machine designed and developed at the IBM Research Center at Yorktown Heights, New York. Benchmark queries of different semantic complexities are generated, and their performance is analyzed for various data and query parameters     

基于JPPF的分布式并行检索系统研究

为解决传统串行检索方式在面对海量数据进行处理和查询时效率低下的问题,提出基于JPPF的分布式并行检索策略。JPPF是一种基于Java的功能强大的并行处理框架,其并行环境易于搭建、简单实用。通过分析JPPF的框架结构和分布式工作流程,利用其在执行队列管理及负载均衡方面的优势,设计和实现了一个基于JPPF的检索系统。采用对比实验的方法,以数据库查询为例,比较了串行检索和JPPF并行检索的效率。实验结果表明在数据规模较大的情况下,JPPF并行方式较之串行方式可以显著提高检索效率。