一句话：提出 TimeStone，一种高可扩展、低写放大、低内存占用的持久事务内存系统。
非易失主存（NVMM）支持字节寻址与近 DRAM 访问，但构建持久应用时需兼顾崩溃一致性、性能与可扩展性。现有持久事务内存（DTM）系统难以扩展到 16 核以上，原因包括底层 STM 层瓶颈、写入并行度受限（单写者或双版本），以及高写放大和内存占用。TimeStone 提出多层混合日志 TOC logging 保证崩溃一致性，并基于多版本并发控制（MVCC）提升可扩展性、在同一数据集上支持不同隔离级别。在最多 112 个硬件线程、不同数据集大小与竞争程度的工作负载上，TimeStone 显著优于先进 DTM 系统，写放大低于 1，而现有系统为 2×–6×。
Non-volatile main memory (NVMM) technologies promise byte addressability and near-DRAM access that allows developers to build persistent applications with common load and store instructions. However, it is difficult to realize these promises because NVMM software should also provide crash consistency while providing high performance, and scalability. Durable transactional memory (DTM) systems address these challenges. However, none of them scale beyond 16 cores. The poor scalability either stems from the underlying STM layer or from employing limited write parallelism (single writer or dual version). In addition, other fundamental issues with guaranteeing crash consistency are high write amplification and memory footprint in existing approaches. To address these challenges, we propose TimeStone: a highly scalable DTM system with low write amplification and minimal memory footprint. TimeStone uses a novel multi-layered hybrid logging technique, called TOC logging, to guarantee crash consistency. Also, TimeStone further relies on Multi-Version Concurrency Control (MVCC) mechanism to achieve high scalability and to support different isolation levels on the same data set. Our evaluation of TimeStone against the state-of-the-art DTM systems shows that it significantly outperforms other systems for a wide range of workloads with varying data-set size and contention levels, up to 112 hardware threads. In addition, with our TOC logging, TimeStone achieves a write amplification of less than 1, while existing DTM systems suffer from 2×-6× overhead.
DOI 原文 ·
@inproceedings{paperbot5408,
title = {Durable Transactional Memory Can Scale with Timestone},
author = {R. Madhava Krishnan and Jaeho Kim and Ajit Mathew and Xinwei Fu and Anthony Demeri and Changwoo Min and Sudarsun Kannan},
booktitle = {ASPLOS},
year = {2020},
doi = {10.1145/3373376.3378483}
}