DRIM-ANN: An Approximate Nearest Neighbor Search Engine based on Commercial DRAM-PIMs

TL;DR

DRIM-ANN is a novel approximate nearest neighbor search engine that leverages DRAM-PIM technology to significantly improve speed and efficiency over traditional CPU and GPU implementations.

Contribution

This paper introduces the first optimized ANNS engine using UPMEM's DRAM-PIM, addressing low computing power with configuration optimizations and load-balancing strategies.

Findings

DRIM-ANN achieves 2.46x speedup over 32-thread CPU

Up to 2.67x speedup over GPU on PIM platforms

Effective load-balancing and I/O optimization strategies implemented

Abstract

Approximate nearest neighbor search (ANNS) is essential for applications like recommendation systems and retrieval-augmented generation (RAG) but is highly I/O-intensive and memory-demanding. CPUs face I/O bottlenecks, while GPUs are constrained by limited memory. DRAM-based Processing-in-Memory (DRAM-PIM) offers a promising alternative by providing high bandwidth, large memory capacity, and near-data computation. This work introduces DRIM-ANN, the first optimized ANNS engine leveraging UPMEM's DRAM-PIM. While UPMEM scales memory bandwidth and capacity, it suffers from low computing power because of the limited processor embedded in each DRAM bank. To address this, we systematically optimize ANNS approximation configurations and replace expensive squaring operations with lookup tables to align the computing requirements with UPMEM's architecture. Additionally, we propose load-balancing and I/O optimization strategies to maximize parallel processing efficiency. Experimental results show that DRIM-ANN achieves a 2.46x speedup over a 32-thread CPU and up to 2.67x over a GPU when deployed on computationally enhanced PIM platforms.