  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.
