DEEP LEARNING AUTOTUNING TASK OPTIMIZATION

    公开(公告)号:US20220129315A1

    公开(公告)日:2022-04-28

    申请号:US17077962

    申请日:2020-10-22

    Abstract: Systems and methods are provided for improving autotuning procedures. For example, the system can implement a task launcher, a scheduler, and an agent to launch, schedule, and execute decomposed autotuning stages, respectively. The scheduling policy implemented by the scheduler may perform operations beyond a simple scheduling policy (e.g., a FIFO-based scheduling policy), which produces a high queuing delay. By leveraging autotuning specific domain knowledge, this may help reduce queuing delay and improve resource utilization that is otherwise found in traditional systems.

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