CONTEXT-AWARE AND STATELESS DEEP LEARNING AUTOTUNING FRAMEWORK

    公开(公告)号:US20220198317A1

    公开(公告)日:2022-06-23

    申请号:US17125626

    申请日:2020-12-17

    Abstract: Systems and methods are provided for improving autotuning procedures using stateless processing with a remote key-value store. 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. Compared to the traditional systems, by leveraging autotuning specific domain knowledge, queueing delay is reduced and resource utilization is improved.

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