SIMD-MIMD cocktail in a hybrid memory glass: Shaken, not stirred

Mikhail Zarubin, Patrick Damme, Alexander Krause, Dirk Habich, Wolfgang Lehner

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Hybrid memory systems consisting of DRAM and NVRAM offer a great opportunity for column-oriented data systems to persistently store and to efficiently process columnar data completely in main memory. While vectorization (SIMD) of query operators is state-of-the-art to increase the single-thread performance, it has to be combined with thread-level parallelism (MIMD) to satisfy growing needs for higher performance and scalability. However, it is not well investigated how such a SIMD-MIMD interplay could be leveraged efficiently in hybrid memory systems. On the one hand, we deliver an extensive experimental evaluation of typical workloads on columnar data in this paper. We reveal that the choice of the most performant SIMD version differs greatly for both memory types. Moreover, we show that the throughput of concurrent queries can be boosted (up to 2x) when combining various SIMD flavors in a multi-threaded execution. On the other hand, to enable that optimization, we propose an adaptive SIMD-MIMD cocktail approach incurring only a negligible runtime overhead.

Original languageEnglish
Title of host publicationSYSTOR 2021 - Proceedings of the 14th ACM International Conference on Systems and Storage
PublisherAssociation of Computing Machinery
ISBN (Electronic)9781450383981
DOIs
Publication statusPublished - 14 Jun 2021
Event14th ACM International Conference on Systems and Storage, SYSTOR 2021 - Virtual, Online, Israel
Duration: 14 Jun 202116 Jun 2021

Publication series

NameSYSTOR 2021 - Proceedings of the 14th ACM International Conference on Systems and Storage

Conference

Conference14th ACM International Conference on Systems and Storage, SYSTOR 2021
Country/TerritoryIsrael
CityVirtual, Online
Period14/06/2116/06/21

Keywords

  • Column store
  • Hybrid memory
  • MIMD
  • Optimization
  • SIMD

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Hardware and Architecture
  • Software

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