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Summary: Adaptive Scheduling with Parallelism Feedback
Kunal Agrawal Yuxiong He Wen Jing Hsu Charles E. Leiserson
Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology
Cambridge, MA 02139
Abstract
Multiprocessor scheduling in a shared multiprogramming environ-
ment is often structured as two-level scheduling, where a kernel-
level job scheduler allots processors to jobs and a user-level task
scheduler schedules the work of a job on the allotted processors. In
this context, the number of processors allotted to a particular job
may vary during the job's execution, and the task scheduler must
adapt to these changes in processor resources. For overall system
efficiency, the task scheduler should also provide parallelism feed-
back to the job scheduler to avoid the situation where a job is allot-
ted processors that it cannot use productively.
We present an adaptive task scheduler for multitasked jobs with
dependencies that provides continual parallelism feedback to the
job scheduler in the form of requests for processors. Our sched-
uler guarantees that a job completes near optimally while utiliz-
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