5 papers
NAVIS: Concurrent Search and Update with Low Position-Seeking Overhead in On-SSD Graph-Based Vector Search
Jaeyong Song, Hongsun Jang, Changmin Shin +4
On-disk graph-based vector search (GVS) has become the dominant approach for serving large-scale vector databases at high recall, but prior systems struggle to sustain concurrent s…
LOCALUT: Harnessing Capacity-Computation Tradeoffs for LUT-Based Inference in DRAM-PIM
Junguk Hong, Changmin Shin, Sukjin Kim +6
Lookup tables (LUTs) have recently gained attention as an alternative compute mechanism that maps input operands to precomputed results, eliminating the need for arithmetic logic.…
A Cost-Effective Near-Storage Processing Solution for Offline Inference of Long-Context LLMs
Hongsun Jang, Jaeyong Song, Changmin Shin +4
The computational and memory demands of large language models for generative inference present significant challenges for practical deployment. One promising solution targeting off…
FlexiWalker: Extensible GPU Framework for Efficient Dynamic Random Walks with Runtime Adaptation
Seongyeon Park, Jaeyong Song, Changmin Shin +3
Dynamic random walks are fundamental to various graph analysis applications, offering advantages by adapting to evolving graph properties. Their runtime-dependent transition probab…
Piccolo: Large-Scale Graph Processing with Fine-Grained In-Memory Scatter-Gather
Changmin Shin, Jaeyong Song, Hongsun Jang +7
Graph processing requires irregular, fine-grained random access patterns incompatible with contemporary off-chip memory architecture, leading to inefficient data access. This ineff…