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Liyin Liu

dblp:290/8424 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2021
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Hardware reliability and fault tolerance · 44% Memory systems · 44% Cloud and datacenter computing · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems
DRAM
0.512021
CARE: Coordinated Augmentation for Elastic Resilience on DRAM Errors in Data Centers · HPCA 2021
Hardware reliability and fault tolerance › memory fault tolerance
DRAM fault tolerance
0.512021
CARE: Coordinated Augmentation for Elastic Resilience on DRAM Errors in Data Centers · HPCA 2021
Cloud and datacenter computing › datacenter operations
datacenter reliability
0.112021
CARE: Coordinated Augmentation for Elastic Resilience on DRAM Errors in Data Centers · HPCA 2021

Methods — techniques the papers use, named apart from their topics

proactive resilience · 0.5cache-like error tracking · 0.5
YearPublicationVenuePosition
2021 CARE: Coordinated Augmentation for Elastic Resilience on DRAM Errors in Data Centers
abstract
As the computation density and memory capacity continues to grow, DRAM errors have become the leading cause of server crashes and/or system failures in modern data centers. While myriads of techniques have been proposed to mitigate their impact on system reliability, these solutions either incur significant overhead on performance, power and memory capacity or require modifying multiple system components; hence, they are impractical to implement or deploy. This paper proposes CARE, a novel error tolerance framework for efficient and elastic resilience on DRAM errors. It introduces a cache-like structure in the memory controller for dynamic error tracking and proactive resilience enhancement to achieve high error tolerance economically and practically. Experiment results show that with around 58KB area overhead in the memory controller, CARE achieves near Chipkill reliability without any memory capacity penalty and incurs negligible performance overhead compared with the baseline SEC-DED systems. CARE provides an attractive alternative to enhance the reliability in data centers.
Xiaowei Jiang, Liyin Liu, Huifeng Xu
HPCA4