Robust, High-Speed Network Design for Large-Scale Multiprocessing

Abstract

Large-scale multiprocessing remains an elusive, yet promising paradigm for achieving high-performance computation. As machine size scales upward, there are two important aspects of multiprocessor systems which will generally get worse rather than better: (1) interprocessor communication latency will increase and (2) the probability that some component in the system will fail with increase. Both of these problems can prevent us from realizing the potential benefits of large-scale multiprocessing. In this document we consider the problem of designing networks which simultaneously minimize communication latency while maximizing fault tolerance for large-scale multiprocessors. Using a synergy of techniques including, connection topologies, routing protocols, signalling techniques, and packaging technologies we assemble integrated, system-level solutions to this network design problem. In particular, we recommend the use of multipath, multistage networks, simple, source-responsible routing, protocols, stochastic fault-avoidance, dense three-dimensional packaging, low-voltage, series-terminated transmission line signalling and scan based diagnostic and reconfiguration. Fault tolerance, Latency, Multipath, Multiprocessing, Network, Transit.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1993
Accession Number
ADA271003

Entities

People

  • AndrĂ© DeHon

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Circuit Boards
  • Computer Networks
  • Computer Programming
  • Computer Science
  • Computers
  • Data Transmission
  • Geometry
  • Mesh Networks
  • Network Architecture
  • Network Protocols
  • Network Topology
  • Optical Interconnects
  • Parallel Computing
  • Printed Circuits
  • Routing Protocols
  • Three Dimensional
  • Transmission Lines

Fields of Study

  • Computer science
  • Engineering

Readers

  • Integrated Circuit Design and Technology.
  • Neural Network Machine Learning.
  • Parallel and Distributed Computing.