Enabling Relational Database Analytical Processing in Bulk-Bitwise Processing-In-Memory
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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 used for relational database analytical processing. We develop a gem5 simulator and show that our hardware modifications, on the Star Schema Benchmark and compared to previous works, 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.
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