← 研踪 PaperTrailarXiv:2605.25522 · v2
PIMCQG: Co-Designing Billion-Scale Graph-Based Approximate Nearest Neighbor Search for Processing-in-Memory
首次提交 2026-05-25 15:22
版本更新 2026-11-03 17:13
雷达观测 2026-11-04 23:39
摘要
Graph-based Approximate Nearest Neighbor Search (ANNS) has emerged as the dominant approach for large-scale AI applications, offering superior accuracy-efficiency trade-offs over alternative methods. However, graph-based ANNS is inherently memory-bound due to frequent and irregular graph traversal. This constrains CPU throughput due to limited memory bandwidth, while GPUs lack sufficient HBM capacity for billion-scale indexes. Processing-in-Memory (PIM) offers massive internal bandwidth by co-locating computation with data, making it a natural fit for irregular, memory-bound workloads. However, directly mapping graph-based ANNS onto PIM exposes severe algorithm-architecture mismatches: restricted per-PU local memory, costly inter-PU communication, heavy coordination overhead, and weak compute capability, forcing existing PIM-based ANNS to rely on cluster-based algorithms that plateau at low recall. We propose PIMCQG, an algorithm-hardware co-design framework through three optimizations: (1) a compact index reducing PIM-resident footprint by 14.5x; (2) an asynchronous pipelined scheduler fully utilizing host-PIM bandwidth; and (3) a multiplication-free distance kernel with less than 0.08% recall loss. Evaluations on three billion-scale datasets show up to 20x/17.1x throughput over CPU/GPU baselines, 129x over prior PIM accelerators at high recall, and scale well across multi-node and emerging PIM architectures.
作者备注：Accepted by SC26
观测到的版本
相邻记录可能跳过中间版本；首次发现 v2 及以上也会标为版本更新。
v2 · 2026-11-03 17:13 · 最新观测版本
PIMCQG: Co-Designing Billion-Scale Graph-Based Approximate Nearest Neighbor Search for Processing-in-Memory
Graph-based Approximate Nearest Neighbor Search (ANNS) has emerged as the dominant approach for large-scale AI applications, offering superior accuracy-efficiency trade-offs over alternative methods. However, graph-based ANNS is inherently memory-bound due to frequent and irregular graph traversal. This constrains CPU throughput due to limited memory bandwidth, while GPUs lack sufficient HBM capacity for billion-scale indexes. Processing-in-Memory (PIM) offers massive internal bandwidth by co-locating computation with data, making it a natural fit for irregular, memory-bound workloads. However, directly mapping graph-based ANNS onto PIM exposes severe algorithm-architecture mismatches: restricted per-PU local memory, costly inter-PU communication, heavy coordination overhead, and weak compute capability, forcing existing PIM-based ANNS to rely on cluster-based algorithms that plateau at low recall. We propose PIMCQG, an algorithm-hardware co-design framework through three optimizations: (1) a compact index reducing PIM-resident footprint by 14.5x; (2) an asynchronous pipelined scheduler fully utilizing host-PIM bandwidth; and (3) a multiplication-free distance kernel with less than 0.08% recall loss. Evaluations on three billion-scale datasets show up to 20x/17.1x throughput over CPU/GPU baselines, 129x over prior PIM accelerators at high recall, and scale well across multi-node and emerging PIM architectures.
查看此版本解读 →查看此版本原文 ↗