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

TL;DR

This paper advances bulk-bitwise processing-in-memory (PIM) to support complex relational database operations like GROUP-BY and JOIN, significantly improving performance and energy efficiency for analytical processing.

Contribution

It introduces hardware modifications enabling efficient GROUP-BY and JOIN in bulk-bitwise PIM, extending previous capabilities for relational database analytics.

Findings

Average execution time improved by 1.83X

Energy consumption reduced by 4.31X

Achieved 4.65X speedup over MonetDB

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 1.83X, energy by 4.31X, and the system's lifetime by 3.21X. We also achieved a speedup of 4.65X over MonetDB, a modern state-of-the-art in-memory database.