Dynamic scheduling strategies for shared-memory multiprocessors

Babak Hamidzadeh, David J. Lilja

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations


Efficiently scheduling parallel tasks on to the processors of a shared-memory multiprocessor is critical to achieving high performance. Given perfect information at compile-time, a static scheduling strategy can produce an assignment of tasks to processors that ideally balances the load among the processors while minimizing the run-time scheduling overhead and the average memory referencing delay. Since perfect information is seldom available, however, dynamic scheduling strategies distribute the task assignment function to the processors by having idle processors allocate work to themselves from a shared queue. While this approach can improve the load balancing compared to static scheduling, the time required to access the shared work queue adds directly to the overall execution time. To overlap the time required to dynamically schedule tasks with the execution of the tasks, we examine a class of Self-Adjusting Dynamic Scheduling (SADS) algorithms that centralizes the assignment of tasks to processors. These algorithms dedicate a single processor of the multiprocessor to perform a novel on-line branch-and-bound technique that dynamically computes partial schedules based on the loads of the other processors and the memory locality (affinity) of the tasks and the processors. Our simulation results show that this centralized scheduling outperforms self-scheduling algorithms even when using only a small number of processors.

Original languageEnglish (US)
Title of host publicationProceedings - International Conference on Distributed Computing Systems
Number of pages8
StatePublished - Jan 1 1996
EventProceedings of the 1996 16th International Conference on Distributed Computing Systems - Hong Kong, Hong Kong
Duration: May 27 1996May 30 1996


OtherProceedings of the 1996 16th International Conference on Distributed Computing Systems
CityHong Kong, Hong Kong


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