Efficient execution of parallel applications in multiprogrammed multiprocessor systems

Kelvin K. Yue, David J Lilja

Research output: Contribution to journalConference articlepeer-review

8 Scopus citations


Existing techniques for sharing the processing resources in multiprogrammed shared-memory multiprocessors, such as time-sharing, space-sharing, and gang-scheduling, typically sacrifice the performance of individual parallel applications to improve overall system utilization. We present a new processor allocation technique that dynamically adjusts the number of processors an application is allowed to use for the execution of each parallel section of code based on the current system load. This approach exploits the maximum parallelism possible for each application without overloading the system. We implement our scheme on a Silicon Graphics Challenge multiprocessor system and evaluate its performance using applications from the Perfect Club benchmark suite and synthetic benchmarks. Our approach shows significant improvements over traditional time-sharing and gang scheduling. It has performance comparable to, or slightly better than, static space-sharing, but our strategy is more robust since, unlike static space-sharing, it does not require a priori knowledge of the applications' parallelism characteristics.

Original languageEnglish (US)
Pages (from-to)448-456
Number of pages9
JournalIEEE Symposium on Parallel and Distributed Processing - Proceedings
StatePublished - 1996
EventProceedings of the 1996 10th International Parallel Processing Symposium - Honolulu, HI, USA
Duration: Apr 15 1996Apr 19 1996


Dive into the research topics of 'Efficient execution of parallel applications in multiprogrammed multiprocessor systems'. Together they form a unique fingerprint.

Cite this