1 citations · 1 across the 5 of their papers we have counts for
10 papers
Moebius: Serving Mixture-of-Expert Models with Seamless Runtime Parallelism Switch
Shaoyu Wang, Yizhuo Liang, Jaeyong Song +2
Mixture-of-Experts (MoE) architectures scale large language models (LLMs) to hundreds of billions of parameters. Serving a single MoE model requires multiple GPUs operating in para…
Vector-Quantized Discrete Latent Factors Meet Financial Priors: Dynamic Cross-Sectional Stock Ranking Prediction for Portfolio Construction
Namhyoung Kim, Jae Wook Song
Predicting cross-sectional stock returns is challenging due to low signal-to-noise ratios and evolving market regimes. Classical factor models offer interpretability but limited fl…
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…
GriNNder: Breaking the Memory Capacity Wall in Full-Graph GNN Training with Storage Offloading
Jaeyong Song, Seongyeon Park, Hongsun Jang +4
Full-graph training of graph neural networks (GNNs) is widely used as it enables direct validation of algorithmic improvements by preserving complete neighborhood information. Howe…
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…