Synchronised execution on shared memory multiprocessors

IF 2 4区计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS Parallel Computing Pub Date : 1988-10-01 DOI:10.1016/0167-8191(88)90121-4

Rhys Francis, Ian Mathieson

引用次数: 3

Abstract

Threads provides a mechanism for simulating the execution of parallel algorithms on a simplified model of a shared-memory multiprocessor. The algorithms can be expressed in a high-level block-structured language, which supports multiple threads of execution within a common body of program code. Results show an ability to achieve good speedup for small problems using algorithms derived by simple modifications of sequential algorithms. As well, a sibling thread synchronisation feature provides the basis for the synchronous execution of threads. k-parallel algorithms tailored to the machine size and implemented as synchronously executing iterations, can provide near linear speedup as the problem size is increased. The techniques described in this paper seem to promise an effective synchronous execution mode for shared-memory MIMD architectures.

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在共享内存多处理器上同步执行

线程提供了一种机制，用于在共享内存多处理器的简化模型上模拟并行算法的执行。算法可以用一种高级的块结构语言表示，这种语言支持在一个通用的程序代码体中执行多个线程。结果表明，通过对顺序算法进行简单修改而得到的算法能够在小问题上实现良好的加速。同样，兄弟线程同步特性为线程的同步执行提供了基础。根据机器大小定制的k-并行算法并作为同步执行迭代实现，可以随着问题规模的增加提供接近线性的加速。本文中描述的技术似乎为共享内存MIMD体系结构提供了一种有效的同步执行模式。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Parallel Computing 工程技术-计算机：理论方法

CiteScore

3.50

自引率

7.10%

发文量

审稿时长

4.5 months

期刊介绍： Parallel Computing is an international journal presenting the practical use of parallel computer systems, including high performance architecture, system software, programming systems and tools, and applications. Within this context the journal covers all aspects of high-end parallel computing from single homogeneous or heterogenous computing nodes to large-scale multi-node systems. Parallel Computing features original research work and review articles as well as novel or illustrative accounts of application experience with (and techniques for) the use of parallel computers. We also welcome studies reproducing prior publications that either confirm or disprove prior published results. Particular technical areas of interest include, but are not limited to: -System software for parallel computer systems including programming languages (new languages as well as compilation techniques), operating systems (including middleware), and resource management (scheduling and load-balancing). -Enabling software including debuggers, performance tools, and system and numeric libraries. -General hardware (architecture) concepts, new technologies enabling the realization of such new concepts, and details of commercially available systems -Software engineering and productivity as it relates to parallel computing -Applications (including scientific computing, deep learning, machine learning) or tool case studies demonstrating novel ways to achieve parallelism -Performance measurement results on state-of-the-art systems -Approaches to effectively utilize large-scale parallel computing including new algorithms or algorithm analysis with demonstrated relevance to real applications using existing or next generation parallel computer architectures. -Parallel I/O systems both hardware and software -Networking technology for support of high-speed computing demonstrating the impact of high-speed computation on parallel applications

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