QEI: Query Acceleration Can be Generic and Efficient in the Cloud

Yifan Yuan, Yipeng Wang, Ren Wang, Rangeen Basu Roy Chowhury, Charlie Tai, Nam Sung Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Data query operations of different data structures are ubiquitous and critical in today's data center infrastructures and applications. However, query operations are not always performance-optimal to be executed on general-purpose CPU cores. These operations exhibit insufficient memory-level parallelism and frontend bottlenecks due to unstructured control flow. Furthermore, the data access patterns are not cache-or prefetch-friendly. Based on our performance analysis on a commodity server, query operations can consume a large percentage of the CPU cycles in various modern cloud workloads. Existing accelerator solutions for query operations do not strike a balance between their generality, scalability, latency, and hardware complexity. In this paper, we propose QEI, a generic, integrated, and efficient acceleration solution for various data structure queries. We first abstract the query operations to a few regular steps and map them to a simple and hardware-friendly configurable finite automaton model. Based on this model, we develop the QEI architecture that allows multiple query operations to execute in parallel to maximize throughput. We also propose a novel way to integrate the accelerator into the CPU that balances performance, latency, and hardware cost. QEI keeps the main control logic near the L2 cache to leverage existing hardware resources in the core while distributing the data-intensive comparison logic to each last-level cache slice for higher parallelism. Our results with five representative data center workloads show that QEI can achieve 6. 5 \times \sim 11. 2 \times performance improvement in various scenarios with low overhead.

Original languageEnglish (US)
Title of host publicationProceeding - 27th IEEE International Symposium on High Performance Computer Architecture, HPCA 2021
PublisherIEEE Computer Society
Pages385-398
Number of pages14
ISBN (Electronic)9780738123370
DOIs
StatePublished - Feb 2021
Event27th Annual IEEE International Symposium on High Performance Computer Architecture, HPCA 2021 - Virtual, Seoul, Korea, Republic of
Duration: Feb 27 2021Mar 1 2021

Publication series

NameProceedings - International Symposium on High-Performance Computer Architecture
Volume2021-February
ISSN (Print)1530-0897

Conference

Conference27th Annual IEEE International Symposium on High Performance Computer Architecture, HPCA 2021
Country/TerritoryKorea, Republic of
CityVirtual, Seoul
Period2/27/213/1/21

Keywords

  • data query
  • near-cache processing
  • on-chip accelerator

ASJC Scopus subject areas

  • Hardware and Architecture

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