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A Perspective on the Future of Massively Parallel Computing:Fine-Grain vs.Coarse-Grain Parallel Models ComparisonContrast

机译:大规模并行计算的未来展望:细粒度与粗粒度并行模型比较与对比

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Models,architectures and languages for parallel computation have been of utmost research interest in computer science and engineering for several decades.A great variety of parallel computation models has been proposed and studied,and different parallel and distributed architectures designed as some possible ways of harnessing parallelism and improving performance of the general purpose computers. rnMassively parallel connectionist models such as artificial neural networks(ANNs)and cellular automata(CA)have been primarily studied in domain-specific contexts,namely, learning and complex dynamics,respectively.However,they can also be viewed as generic abstract models of massively parallel computers that are in many respects fundamentally different from the“main stream”parallel and distributed computation models. rnWe compare and contrast herewith the parallel computers as they have been built by the engineers with those built by Nature.We subsequently venture onto a high-level discussion of the properties and potential advantages of the pro- posed massively parallel computers of the future that would be based on the fine-grained connectionist parallel models, rather than on either various multiprocessor architectures, or networked distributed systems,which are the two main architecture paradigms in building parallel computers of the late 20th and early 21st centuries.The comparisons and contrasts herein are focusing on the fundamental conceptual characteristics of various models rather than any particular engineering idiosyncrasies,and are carried out at both structural and functional levels.The fundamental distinctions between the fine-grain connectionist parallel models and their“classical”coarsegrain counterparts are discussed, and some important expected advantages of the hypothetical massively parallel computers based on the connectionist paradigms conjectured. rnWe conclude with some brief remarks on the role that the paradigms,concepts,and design ideas originating from the connectionist models have already had in the existing parallel design,and what further role the connectionist models may have in the foreseeable future of parallel and distributed computing.
机译:数十年来,并行计算的模型,体系结构和语言一直是计算机科学和工程领域的最大研究兴趣。已经提出并研究了各种各样的并行计算模型,并设计了不同的并行和分布式体系结构作为利用并行性的一些可能方式。并提高通用计算机的性能。 rn分别在特定领域的上下文中(即学习和复杂动力学)研究了诸如神经网络(ANN)和细胞自动机(CA)之类的大规模并行连接模型。但是,它们也可以看作是大规模的通用抽象模型。在许多方面与“主流”并行和分布式计算模型根本不同的并行计算机。 rn我们将由工程师与自然界制造的并行计算机进行比较和对比。随后,我们进行了高级别的讨论,讨论了未来提出的大型并行计算机的特性和潜在优势。基于细粒度的连接主义并行模型,而不是基于各种多处理器体系结构或网络分布式系统,它们是构建20世纪末和21世纪初的并行计算机的两个主要体系结构范例。着眼于各种模型的基本概念特征,而不是任何特定的工程特性,并且在结构和功能两个层面上进行。讨论了细粒度的连接主义并行模型与其“经典”粗麻布对应模型之间的基本区别,并讨论了一些假设质量的重要预期优势基于连接主义范式的虚拟并行计算机。 rn我们在结束语中简要介绍了源于连接主义模型的范式,概念和设计思想在现有并行设计中已经扮演的角色,以及在可预见的并行和分布式计算的未来中,连接主义模型还可以发挥什么作用。

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