4 citations · 7 across the 10 of their papers we have counts for
10 papers
A CXL Memory Rack for Multi-Turn LLM Serving
Hakbeom Jang, Inho Song, Hoshik Kim +2
Long-context, multi-turn, and agentic LLM workloads increasingly reuse previously processed context, making KV-cache reuse essential for reducing redundant computation. However, th…
ITME: Inference Tiered Memory Expansion with Disaggregated CXL-Hybrid Memories
Hakbeom Jang, Younghoon Min, Sunwoong Kim +5
The rapid shift toward agentic and long-context workloads in Large Language Models (LLMs) is pushing the industry beyond the capacity of individual servers toward disaggregated sha…
Characterizing How Complex Agentic AI Systems Handle General Tasks: A Trace-Based Simulation Study
Donghwan Kim, Prakhar Singh, Younghoon Min +3
Agentic AI completes tasks through iterative planning, tool use, and reasoning based on observed outcomes. Despite its popularity, its system-level behavior remains poorly understo…
Hybrid Adaptive Tuning for Tiered Memory Systems
Xi Wang, Jie Liu, Shuangyan Yang +3
Memory tiering provides a cost-effective solution to increase memory capacity, utilization, and even bandwidth. Memory tiering relies on system software for memory profiling, detec…
Double-P: Hierarchical Top-P Sparse Attention for Long-Context LLMs
Wentao Ni, Kangqi Zhang, Zhongming Yu +7
As long-context inference becomes central to large language models (LLMs), attention over growing key-value caches emerges as a dominant decoding bottleneck, motivating sparse atte…
TraCT: Disaggregated LLM Serving with CXL Shared Memory KV Cache at Rack-Scale
Dongha Yoon, Younghoon Min, Hoshik Kim +2
Disaggregated LLM serving improves resource efficiency by separating the compute-intensive prefill phase from the latency-critical decode phase. However, this architecture introduc…
Accelerating Sparse Matrix-Matrix Multiplication on GPUs with Processing Near HBMs
Shiju Li, Younghoon Min, Hane Yie +5
Sparse General Matrix-Matrix Multiplication (SpGEMM) is a fundamental operation in numerous scientific computing and data analytics applications, often bottlenecked by irregular me…
Cocoon: A System Architecture for Differentially Private Training with Correlated Noises
Donghwan Kim, Xin Gu, Jinho Baek +6
Machine learning (ML) models memorize and leak training data, causing serious privacy issues to data owners. Training algorithms with differential privacy (DP) have been gaining at…
cMPI: Using CXL Memory Sharing for MPI One-Sided and Two-Sided Inter-Node Communications
Xi Wang, Bin Ma, Jongryool Kim +3
Message Passing Interface (MPI) is a foundational programming model for high-performance computing. MPI libraries traditionally employ network interconnects (e.g., Ethernet and Inf…
SDT-GNN: Streaming-based Distributed Training Framework for Graph Neural Networks
Xin Huang, Weipeng Zhuo, Minh Phu Vuong +4
Recently, distributed GNN training frameworks, such as DistDGL and PyG, have been developed to enable training GNN models on large graphs by leveraging multiple GPUs in a distribut…