Parallel Loop Scheduling for High Performance Computers

Kelvin K. Yue, David J. Lilja

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Executing loop iterations in parallel on a multiprocessor system is one of the many ways to improve the execution of a program. However, due to the scheduling overhead and the potential for load imbalance among processors, maximum performance might not be attained. This article reviews current loop scheduling algorithms and studies their scheduling overhead versus load balancing tradeoffs. Using analytical models, simulations, and experimental measurements, the performance and the scalability of chunk scheduling, self-scheduling, guided self-scheduling, factoring, and trapezoid self-scheduling are compared.

Original languageEnglish (US)
Pages (from-to)243-264
Number of pages22
JournalAdvances in Parallel Computing
Issue numberC
StatePublished - Jan 1 1995


  • Analytical modeling
  • Parallel loop scheduling
  • Performance analysis
  • Scalability
  • Shared memory multiprocessor


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