Enabling Relational Database Analytical Processing in Bulk-Bitwise Processing-In-Memory

Ben Perach, Ronny Ronen, Shahar Kvatinsky

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

Abstract

Bulk-bitwise processing-in-memory (PIM), an emerging computational paradigm utilizing memory arrays as computational units, has been shown to benefit database applications. This paper demonstrates how GROUP-BY and JOIN, database operations not supported by previous works, can be performed efficiently in bulk-bitwise PIM for relational database analytical processing. We extend the gem5 simulator and evaluated our hardware modifications on the Star Schema Benchmark. We show that compared to previous works, our modifications improve (on average) execution time by 1.83×, energy by 4.31×, and the system's lifetime by 3.21×. We also achieved a speedup of 4.65× over MonetDB, a modern state-of-the-art in-memory database.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE 36th International System-on-Chip Conference, SOCC 2023
EditorsJurgen Becker, Andrew Marshall, Tanja Harbaum, Amlan Ganguly, Fahad Siddiqui, Kieran McLaughlin
ISBN (Electronic)9798350300116
DOIs
StatePublished - 2023
Event36th IEEE International System-on-Chip Conference, SOCC 2023 - Santa Clara, United States
Duration: 5 Sep 20238 Sep 2023

Publication series

NameInternational System on Chip Conference
Volume2023-September

Conference

Conference36th IEEE International System-on-Chip Conference, SOCC 2023
Country/TerritoryUnited States
CitySanta Clara
Period5/09/238/09/23

Keywords

  • Database
  • Memristors
  • OLAP
  • Processing-in-memory

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Hardware and Architecture

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