Scalable Parallel Computing: Technology, Architecture, Programming
Scalable Parallel Computing: Technology, Architecture, Programming
复制标题
DOI：
10.12694/scpe.v2i1.106
复制
发表时间：
1999
期刊：
影响因子：
--
通讯作者：
B. Cong;S. Morrison;Michael Yorg
中科院分区：
文献类型：
--
作者：
B. Cong;S. Morrison;Michael Yorg
黄凯和徐志伟McGraw-Hill，波士顿，1998年，802页。ISBN 0-07-031798-4, $97.30这篇文章是对并行计算概念的深入介绍。设计用于大学水平的计算机科学课程，文本涵盖了对称多处理器，工作站集群，大规模并行处理器和基于互联网的元计算平台的可扩展架构和并行编程。Hwang和Xu在保持文本易于理解的同时，对这些主题进行了出色的概述。全文共分为四个部分。第1部分介绍可伸缩性和集群。第二部分讨论用于构建并行系统的技术。第三部分涉及可扩展系统的体系结构。最后，第四部分介绍了在各种平台和语言上并行编程的方法。第一章介绍了不同的可伸缩性模型，分为资源、应用程序和技术。它定义了三个抽象模型（PRAM、BSP和相位并行模型）和五个物理模型（PVP、SMP、MPP、COW和MPP系统）。第2章介绍了并行编程背后的思想，包括进程、任务、线程和环境。第3章介绍了性能问题和度量。作为第二部分的介绍，第4章介绍了微处理器类型的历史及其在当前系统体系结构中的应用。第5章讨论分布式内存的问题。讨论了UMA、NORMA、CC-NUMA、COMA和DSM等模型。第6章介绍了千兆网络、交换互连和其他各种高速网络架构来构建集群。第7章讨论了并行计算带来的开销，比如线程、同步和节点间的高效通信。第三部分，第8,9和11章，给出了各种类型的可扩展系统（SMP, CC-NUMA，集群和MPP）之间的比较。这种比较是基于硬件架构、系统软件和使每个系统独一无二的特殊功能。第10章通过对Berkeley NOW、IBM SP2和Digital TruCluster系统的深入研究，比较了各种研究和商业集群。第12章详细介绍了第四部分的概念和并行编程范例。第13章讨论了使用消息传递编程（如MPI和PVM库）的处理器之间的通信。第14章研究了数据并行方法，重点是在Fortran 90和HPF中。通过例子，Hwang和Xu对细节的关注创造了一个很好的并行计算介绍。作者因在这一领域的贡献而闻名。这篇文章是基于前沿的研究，提供了目前在工业中使用的理论。Bin Cong， Shawn Morrison和Michael Yorg，位于圣路易斯奥比斯波的加州州立理工大学计算机科学系
Kai Hwang and Zhiwei Xu McGraw-Hill, Boston, 1998, 802 pp. ISBN 0-07-031798-4, $97.30 This text is an in depth introduction to the concepts of Parallel Computing. Designed for use in university level computer science courses, the text covers scalable architecture and parallel programming of symmetric muli-processors, clusters of workstations, massively parallel processors, and Internet-based metacomputing platforms. Hwang and Xu give an excellent overview in these topics while keeping the text easily comprehensible. The text is organized into four parts. Part I covers scalability and clustering. Part II deals with the technology used to construct a parallel system. Part III pertains to the architecture of scalable systems. Finally, Part IV presents methods of parallel programming on various platforms and languages. The first chapter presents different models on scalability as divided into resources, applications, and technology. It defines three abstract models (PRAM, BSP, and phase parallel models) and five physical models (PVP, SMP, MPP, COW, and MPP systems). Chapter 2 introduces the ideas behind parallel programming including processes, tasks, threads and environments. Chapter 3 introduces performance issues and metrics. As an introduction to Part II, Chapter 4 introduces the history of microprocessor types and their applications in the architectures of current systems. Chapter 5 deals with the issues of distributed memory. It discusses several models such as UMA, NORMA, CC-NUMA, COMA, and DSM. Chapter 6 presents gigabit networks, switched interconnects, and other various high-speed networking architectures to construct clusters. Chapter 7 discusses the overheads created by parallel computing, such as threads, synchronization, and efficient communication between nodes. Part III, Chapter 8, 9 and 11, give comparisons between various types of scalable systems (SMP, CC-NUMA, Clusters, and MPP). The comparisons are based on hardware architecture, the system software, and special features that make each system unique. Chapter 10 compares various research and commercial clusters with an in depth study of the Berkeley NOW, IBM SP2, and Digital TruCluster systems. Chapter 12 introduces the concepts of Part IV with details into parallel programming paradigms. Chapter 13 discusses communications between the processors using message passing programming (such as MPI and PVM libraries). Chapter 14 studies the data parallel approach with an emphasis in Fortran 90 and HPF. With attention to detail through examples, Hwang and Xu have created a well-written introduction to Parallel Computing. The authors are distinguished for their contributions in this field. This text is based on cutting-edge research, providing the current theories that are in use in industry today. Bin Cong, Shawn Morrison and Michael Yorg, Department of Computer Science California Polytechnic State University at San Luis Obispo