A perspective on the future of massively parallel computing: fine-grain vs. coarse-grain parallel models comparison & contrast

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.Massively 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.We 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 proposed 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" coarse-grain counterparts are discussed, and some important expected advantages of the hypothetical massively parallel computers based on the connectionist paradigms conjectured.We 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.

[1]  R. Jackendoff Consciousness and the Computational Mind , 1987 .

[2]  Richard M. Karp,et al.  Parallel Algorithms for Shared-Memory Machines , 1991, Handbook of Theoretical Computer Science, Volume A: Algorithms and Complexity.

[3]  S. Sitharama Iyengar,et al.  Introduction to parallel algorithms , 1998, Wiley series on parallel and distributed computing.

[4]  Eitan M. Gurari,et al.  Introduction to the theory of computation , 1989 .

[5]  Timothy F. Havel,et al.  Nuclear magnetic resonance spectroscopy: an experimentally accessible paradigm for quantum computing , 1997, quant-ph/9709001.

[6]  John von Neumann,et al.  The Computer and the Brain , 1960 .

[7]  Andrew Adamatzky,et al.  Computing in nonlinear media and automata collectives , 2001 .

[8]  Idan Segev,et al.  Methods in neuronal modeling: From synapses to networks , 1989 .

[9]  George Cboom,et al.  Mind: A Quarterly Review of Psychology and Philosophy , 1876, Journal of Psychological Medicine and Mental Pathology (London, England : 1875).

[10]  A. Roli Artificial Neural Networks , 2012, Lecture Notes in Computer Science.

[11]  Graham Birtwistle,et al.  Vlsi and Parallel Computation , 1990 .

[12]  A. M. Turing,et al.  Computing Machinery and Intelligence , 1950, The Philosophy of Artificial Intelligence.

[13]  A. Fleischmann Distributed Systems , 1994, Springer Berlin Heidelberg.

[14]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[15]  Max H. Garzon,et al.  Models of massive parallelism: analysis of cellular automata and neural networks , 1995 .

[16]  Dake Liu,et al.  Power consumption estimation in CMOS VLSI chips , 1994, IEEE J. Solid State Circuits.

[17]  Terrence J. Sejnowski,et al.  The Computer and the Brain Revisited , 1989, Annals of the History of Computing.

[18]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[19]  Eric Goles,et al.  Neural and automata networks , 1990 .

[20]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[21]  John von Neumann,et al.  Theory Of Self Reproducing Automata , 1967 .

[22]  Paul G. Spirakis,et al.  Lectures on parallel computation , 1993 .

[23]  Terrence J. Sejnowski,et al.  The Computational Brain , 1996, Artif. Intell..

[24]  Wojciech Rytter,et al.  Efficient parallel algorithms , 1988 .

[25]  George Coulouris,et al.  Distributed systems - concepts and design , 1988 .

[26]  Albert Y. Zomaya,et al.  Solutions to Parallel and Distributed Computing Problems: Lessons from Biological Sciences , 2000 .

[27]  George Coulouris,et al.  Distributed systems (3rd ed.): concepts and design , 2000 .

[28]  C. Collin,et al.  An Introduction to Natural Computation , 1998, Trends in Cognitive Sciences.

[29]  Kenneth L. Artis Design for a Brain , 1961 .

[30]  J SejnowskiTerrence The Computer and the Brain Revisited , 1989 .

[31]  Allen,et al.  Optimizing Compilers for Modern Architectures , 2004 .

[32]  Jeffrey D. Ullman,et al.  Introduction to Automata Theory, Languages and Computation , 1979 .

[33]  R. Landauer,et al.  The Fundamental Physical Limits of Computation. , 1985 .

[34]  Achim Hoffman Paradigms of Artificial Intelligence: A Methodological and Computational Analysis , 1998 .

[35]  Sape J. Mullender,et al.  Distributed systems (2nd Ed.) , 1993 .

[36]  S. Lloyd Quantum-Mechanical Computers , 1995 .

[37]  Jeffrey D Ullma Computational Aspects of VLSI , 1984 .

[38]  Yale Patt,et al.  Exploiting fine-grained parallelism through a combination of hardware and software techniques , 1991, ISCA '91.

[39]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[40]  M. Delorme,et al.  Cellular automata : a parallel model , 1999 .