Dynamic scheduling of concurrent tasks with cost performance trade-off

Yang, Pingxiong; Desmet, D.; Catthoor, Francky; Verkest, D.

doi:10.1145/354880.354895

Cited by 3 publications

(2 citation statements)

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“…Then static scheduling will be applied inside each task at design time, including processor assignment in the context of multiple processing elements (PEs). Finally, a dynamic scheduler will schedule these tasks at run time on the given platform [5].…”

Section: Framework Of the Task Concur-rency Management Methodol-ogy Amentioning

confidence: 99%

Task concurrency management methodology to schedule the MPEG4 IM1 player on a highly parallel processor platform

Wong

Marchal²,

Yang

2001

Proceedings of the Ninth International Symposium on Hardware/Software Codesign - CODES '01

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This paper addresses the concurrent task management of complex multi-media systems, like the MPEG4 IM1 player, with emphasis on how to derive energy-cost vs time-budget curves through task scheduling on a multi-processor platform. Starting from the original "standard" specification, we extract the concurrency originally hidden by implementation decisions in a "grey-box" model. Then we have applied two high-level transformations on this model to improve the task-level concurrency. Finally, by scheduling the transformed task-graph, we have derived energy-cost vs time-budget curves. These curves will be used to get globally optimized design decisions when combining subsystems into one complete system or to be used by a dynamic scheduler. The results on the MPEG4 IM1 player confirm the validity of our assumptions and the usefulness of our approach.

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Section: Framework Of the Task Concur-rency Management Methodol-ogy Amentioning

confidence: 99%

Task concurrency management methodology to schedule the MPEG4 IM1 player on a highly parallel processor platform

Wong

Marchal²,

Yang

2001

Proceedings of the Ninth International Symposium on Hardware/Software Codesign - CODES '01

Self Cite

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“…With adaptive scheduling [4] (called "dynamic" scheduling in many other papers [27,37,41,58,60]), the job scheduler can change the number of processors allotted to a job while the job executes. Thus, new jobs can enter the system, because the job scheduler can simply recruit processors from the already executing jobs and allot them to the new jobs.…”

Section: Introductionmentioning

confidence: 99%

Provably Efficient Two-Level Adaptive Scheduling

Hsu

Leiserson

Job Scheduling Strategies for Parallel Processing

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Multiprocessor scheduling in a shared multiprogramming environment can be structured in two levels, where a kernel-level job scheduler allots processors to jobs and a user-level thread scheduler maps the ready threads of a job onto the allotted processors. This paper presents two-level scheduling schemes for scheduling "adaptive" multithreaded jobs whose parallelism can change during execution. The AGDEQ algorithm uses dynamic-equipartioning (DEQ) as a job-scheduling policy and an adaptive greedy algorithm (A-Greedy) as the thread scheduler. The ASDEQ algorithm uses DEQ for job scheduling and an adaptive work-stealing algorithm (A-Steal) as the thread scheduler. AGDEQ is suitable for scheduling in centralized scheduling environments, and ASDEQ is suitable for more decentralized settings. Both two-level schedulers achieve O(1)-competitiveness with respect to makespan for any set of multithreaded jobs with arbitrary release time. They are also O(1)competitive for any batched jobs with respect to mean response time. Moreover, because the length of the scheduling quantum can be adjusted to amortize the cost of context-switching during processor reallocation, our schedulers provide control over the scheduling overhead and ensure effective utilization of processors.

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