JSPIM: A Skew-Aware PIM Accelerator for High-Performance Databases Join and Select Operations

TL;DR

JSPIM is a novel skew-aware PIM accelerator that significantly speeds up database join and select operations by optimizing hash table lookups and parallelism, overcoming memory bottlenecks and data skew issues.

Contribution

The paper introduces JSPIM, a PIM architecture with algorithm-hardware co-design that achieves O(1) lookups and handles data skew, enabling high-performance database joins.

Findings

400x to 1000x speedup on join queries

2.5x overall throughput improvement on SSB benchmark

7% data overhead with minimal area increase

Abstract

Database applications are increasingly bottlenecked by memory bandwidth and latency due to the memory wall and the limited scalability of DRAM. Join queries, central to analytical workloads, require intensive memory access and are particularly vulnerable to inefficiencies in data movement. While Processing-in-Memory (PIM) offers a promising solution, existing designs typically reuse CPU-oriented join algorithms, limiting parallelism and incurring costly inter-chip communication. Additionally, data skew, a main challenge in CPU-based joins, remains unresolved in current PIM architectures. We introduce JSPIM, a PIM module that accelerates hash join and, by extension, corresponding select queries through algorithm-hardware co-design. JSPIM deploys parallel search engines within each subarray and redesigns hash tables to achieve O(1) lookups, fully exploiting PIM's fine-grained parallelism. To mitigate skew, our design integrates subarray-level parallelism with rank-level processing, eliminating redundant off-chip transfers. Evaluations show JSPIM delivers 400x to 1000x speedup on join queries versus DuckDB. When paired with DuckDB for the full SSB benchmark, JSPIM achieves an overall 2.5x throughput improvement (individual query gains of 1.1x to 28x), at just a 7% data overhead and 2.1% per-rank PIM-enabled chip area increase.