Lune

DAC2023Top-tier venue

DRPTM: A Decoupled Read-efficient High-scalable Persistent Transactional Memory

Wenkai Liang, Hao Hu, Xiangyu Zou, Wen Xia, Yanqi Pan

2023Year
3Citations

Abstract

Persistent transactional memory (PTM) exploits transactions to provide an easy crash-consistent interface for persistent memory (PM). However, because of the substantial reader-side overhead brought on by the low bandwidth and long persistence latency of PM, present PTM research cannot scale effectively. This paper proposes a highly scalable PTM system, DRPTM, which allows nearly non-overhead reads without lowering the isolation level. DRPTM decouples persistence latency from concurrency control and traces the read-only copy maintained in logs as a lightweight read set. The evaluation shows that DRPTM significantly outperforms the state-of-the-art PTM systems for various workloads and achieves near-linear scalability.

Ask about this paper

Ask your agent about it.

Lune has read the top-tier papers around this one, so every answer names the papers it rests on.

Questions to start from

Your agent calls

Lunesearch_papers

Ask in Lune

Free to start. No credit card required.

lune papers get 2bd01ffa-b5a8-4889-a161-32a88c931781

Related papers

Dusk over the sea between two cliffs drawn in fine vertical lines