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PIRL 2020 Towards Easier Persistence Aware Programming A Deep Reinforcement Learning Approach

webinar

Author(s)/Presenter(s):

Hanzian Huang

UC San Diego

Library Content Type

Presentation

Library Release Date

Focus Areas

Persistent Memory

Abstract

This presentation from the PIRL 2020 conference is by Hanzian Huang of UC San Diego. The widely-used method to perform persistence memory-aware programming requires in-depth code changes using an NVMM-aware library, which is labor-intensive and error-prone. To address this problem, we try to introduce a deep reinforcement learning approach to help ease the persistence-aware programming. Given C, C++ or Java volatile source codes, our deep reinforcement learning model will help generate the corresponding non-volatile-aware codes based on PMDK library and the persistency will be checked by PMEMCHECK and PM-Reorder. Then the codes will be further improved by debugging tools. Experiments show that we can help generate persistence-aware programs with similar performance compared to the experts’ codes, which reduces the labor on modifying existing data structure and also reduces bugs in persistence-aware programming.