Ayudante: A Deep Reinforcement Learning Approach to Assist Persistent Memory Programming


Hanxian Huang, Zixuan Wang, Juno Kim, Steven Swanson, and Jishen Zhao, University of California, San Diego


Nonvolatile random-access memories (NVRAMs) are envisioned as a new tier of memory in future server systems. They enable a promising persistent memory (PM) technique, with comparable performance of DRAM and the persistence property of storage. However, programming PM imposes non-trivial labor effort on writing code to adopt new PM-aware libraries and APIs. In addition, non-expert PM code can be error-prone. In order to ease the burden of PM programmers, we propose Ayudante, a deep reinforcement learning (RL)-based PM programming assistant framework consisting of two key components: a deep RL-based PM code generator and a code refining pipeline. Given a piece of C, C++, or Java source code developed for conventional volatile memory systems, our code generator automatically generates the corresponding PM code and checks its data persistence. The code refining pipeline parses the generated code to provide a report for further program testing and performance optimization. Our evaluation on an Intel server equipped with Optane DC PM demonstrates that both microbenchmark programs and a key-value store application generated by Ayudante pass PMDK checkers. Performance evaluation on the microbenchmarks shows that the generated code achieves comparable speedup and memory access performance as PMDK code examples.

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@inproceedings {273824,
author = {Hanxian Huang and Zixuan Wang and Juno Kim and Steven Swanson and Jishen Zhao},
title = {Ayudante: A Deep Reinforcement Learning Approach to Assist Persistent Memory Programming},
booktitle = {2021 USENIX Annual Technical Conference (USENIX ATC 21)},
year = {2021},
isbn = {978-1-939133-23-6},
pages = {789--804},
url = {https://www.usenix.org/conference/atc21/presentation/huang-hanxian},
publisher = {USENIX Association},
month = jul,

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