Log-Structured Non-Volatile Main Memory


Qingda Hu, Tsinghua University; Jinglei Ren and Anirudh Badam, Microsoft Research; Jiwu Shu, Tsinghua University; Thomas Moscibroda, Microsoft Research


Emerging non-volatile main memory (NVMM) unlocks the performance potential of applications by storing persistent data in the main memory. Such applications require a lightweight persistent transactional memory (PTM) system, instead of a heavyweight filesystem or database, to have fast access to data. In a PTM system, the memory usage, both capacity and bandwidth, plays a key role in dictating performance and efficiency. Existing memory management mechanisms for PTMs generate high memory fragmentation, high write traffic and a large number of persist barriers, since data is first written to a log and then to the main data store.

In this paper, we present a log-structured NVMM system that not only maintains NVMM in a compact manner but also reduces the write traffic and the number of persist barriers needed for executing transactions. All data allocations and modifications are appended to the log which becomes the location of the data. Further, we address a unique challenge of log-structured memory management by designing a tree-based address translation mechanism where access granularities are flexible and different from allocation granularities. Our results show that the new system enjoys up to 89.9% higher transaction throughput and up to 82.8% lower write traffic than a traditional PTM system.

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@inproceedings {203189,
author = {Qingda Hu and Jinglei Ren and Anirudh Badam and Jiwu Shu and Thomas Moscibroda},
title = {{Log-Structured} {Non-Volatile} Main Memory},
booktitle = {2017 USENIX Annual Technical Conference (USENIX ATC 17)},
year = {2017},
isbn = {978-1-931971-38-6},
address = {Santa Clara, CA},
pages = {703--717},
url = {https://www.usenix.org/conference/atc17/technical-sessions/presentation/hu},
publisher = {USENIX Association},
month = jul

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