A Parallel Workload Model and its Implications for Processor Allocation

Abstract

We develop a workload model based on the observed behavior of parallel computers at the San Diego Supercomputer Center and the Cornell Theory Center. This model gives us insight into the performance of strategies for scheduling malleable jobs on space-sharing parallel computers. We find that Adaptive Static Partitioning (ASP), which has been reported to work well for other workloads, is inferior to some FIFO strategies that adapt better to system load. The best of the strategies we consider is one that explicitly restricts cluster sizes when load is high (a variation of Sevcik's A+ strategy [13]).

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Document Details

Document Type
Technical Report
Publication Date
Nov 01, 1996
Accession Number
ADA637066

Entities

People

  • Allen B. Downey

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Batch Processing
  • California
  • Computer Programming
  • Computer Science
  • Computers
  • Data Science
  • Engineering
  • Observation
  • Order Statistics
  • Production Engineering
  • Scheduling (Production)
  • Sensitivity
  • Simulations
  • Simulators
  • Statistics
  • Supercomputers
  • Workload

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Parallel and Distributed Computing.

Technology Areas

  • Space