A scalable scheduling scheme for functional parallelism on distributed memory multiprocessor systems
We attempt a new variant of the scheduling problem by investigating the scalability of the schedule length with the required number of processors, by performing scheduling partially at compile time and partially at run time. Assuming infinite number of processors, the compile time schedule is found...
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| Veröffentlicht in: | IEEE transactions on parallel and distributed systems Jg. 6; H. 4; S. 388 - 399 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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Los Alamitos, CA
IEEE
01.04.1995
IEEE Computer Society |
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| ISSN: | 1045-9219 |
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| Abstract | We attempt a new variant of the scheduling problem by investigating the scalability of the schedule length with the required number of processors, by performing scheduling partially at compile time and partially at run time. Assuming infinite number of processors, the compile time schedule is found using a new concept of the threshold of a task that quantifies a trade-off between the schedule-length and the degree of parallelism. The schedule is found to minimize either the schedule length or the number of required processors and it satisfies: A feasibility condition which guarantees that the schedule delay of a task from its earliest start time is below the threshold, and an optimality condition which uses a merit function to decide the best task-processor match for a set of tasks competing for a given processor. At run time, the tasks are merged producing a schedule for a smaller number of available processors. This allows the program to be scaled down to the processors actually available at run time. Usefulness of this scheduling heuristic has been demonstrated by incorporating the scheduler in the compiler backend for targeting Sisal (Streams and Iterations in a Single Assignment Language) on iPSC/860.< > |
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| AbstractList | We attempt a new variant of the scheduling problem by investigating the scalability of the schedule length with the required number of processors, by performing scheduling partially at compile time and partially at run time. Assuming infinite number of processors, the compile time schedule is found using a new concept of the threshold of a task that quantifies a trade-off between the schedule-length and the degree of parallelism. The schedule is found to minimize either the schedule length or the number of required processors and it satisfies: A feasibility condition which guarantees that the schedule delay of a task from its earliest start time is below the threshold, and an optimality condition which uses a merit function to decide the best task-processor match for a set of tasks competing for a given processor. At run time, the tasks are merged producing a schedule for a smaller number of available processors. This allows the program to be scaled down to the processors actually available at run time. Usefulness of this scheduling heuristic has been demonstrated by incorporating the scheduler in the compiler backend for targeting Sisal (Streams and Iterations in a Single Assignment Language) on iPSC/860 We attempt a new variant of the scheduling problem by investigating the scalability of the schedule length with the required number of processors, by performing scheduling partially at compile time and partially at run time. Assuming infinite number of processors, the compile time schedule is found using a new concept of the threshold of a task that quantifies a trade-off between the schedule-length and the degree of parallelism. The schedule is found to minimize either the schedule length or the number of required processors and it satisfies: A feasibility condition which guarantees that the schedule delay of a task from its earliest start time is below the threshold, and an optimality condition which uses a merit function to decide the best task-processor match for a set of tasks competing for a given processor. At run time, the tasks are merged producing a schedule for a smaller number of available processors. This allows the program to be scaled down to the processors actually available at run time. Usefulness of this scheduling heuristic has been demonstrated by incorporating the scheduler in the compiler backend for targeting Sisal (Streams and Iterations in a Single Assignment Language) on iPSC/860.< > |
| Author | Agrawal, D.P. Mauney, J. Pande, S. |
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| Keywords | Multiprocessor Distributed memory multiprocessor system Graph Parallelism Scheduling Distributed system Data flow |
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| SubjectTerms | Applied sciences Computer science; control theory; systems Computer systems and distributed systems. User interface Costs Delay effects Dynamic scheduling Exact sciences and technology Multiprocessing systems Optimal scheduling Parallel processing Processor scheduling Runtime Scalability Scheduling algorithm Software Software engineering |
| Title | A scalable scheduling scheme for functional parallelism on distributed memory multiprocessor systems |
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