Invention Application
- Patent Title: DEEP LEARNING AUTOTUNING TASK OPTIMIZATION
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Application No.: US17077962Application Date: 2020-10-22
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Publication No.: US20220129315A1Publication Date: 2022-04-28
- Inventor: JUNGUK CHO , PUNEET SHARMA , DOMINIK STILLER
- Applicant: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
- Applicant Address: US TX Houston
- Assignee: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
- Current Assignee: HEWLETT PACKARD ENTERPRISE DEVELOPMENT LP
- Current Assignee Address: US TX Houston
- Main IPC: G06F9/50
- IPC: G06F9/50 ; G06N20/00

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.
Public/Granted literature
- US12067420B2 Deep learning autotuning task optimization Public/Granted day:2024-08-20
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