Scheduling for Locality in Shared-Memory Multiprocessors

Abstract

The last decade has produced enormous improvements in processor speed without a corresponding improvement in bus or interconnection network speeds. As a result, the relative costs of communication and computation in shared-memory multiprocessors have changed dramatically, and many parallel applications do not execute efficiently on today's multiprocessors. In this dissertation we quantify the effect of this trend-in architecture on parallel program performance, explain the implications of this trend on popular parallel programming models, and propose system software to efficiently map parallel programs and programming models to modern shared-memory multiprocessors. We propose new decomposition and scheduling algorithms that significantly reduce communication overhead. Our experiments over a wide variety of shared-memory multiprocessors demonstrate that the performance benefits of our scheduling-for-locality algorithms are significant, improving performance by up to 60% for some applications. We conclude that communication overhead need not dominate performance, given an appropriate programming model, multiprogramming scheduling policy, and user- level decomposition and scheduling algorithms. Shared-memory multiprocessors, Architecture trends, Loop scheduling, Lightweight thread scheduling, Multiprogramming

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Document Details

Document Type
Technical Report
Publication Date
May 01, 1993
Accession Number
ADA272948

Entities

People

  • Evangelos Markatos

Organizations

  • University of Rochester

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Application Software
  • Computer Programming
  • Computer Science
  • Computers
  • Convolution
  • Hierarchies
  • Load Monitoring
  • Numerical Analysis
  • Operating Systems
  • Parallel Computing
  • Parallel Processing
  • Parallel Processors
  • Scheduling (Production)
  • Simulators
  • Synchros
  • Theses
  • Workload

Fields of Study

  • Computer science
  • Engineering

Readers

  • Parallel and Distributed Computing.