13 citations · 13 across the 7 of their papers we have counts for
9 papers
Accurate Simulation of Distributed Training Jobs with Network Contention Modeling
Yeonho Yoo, Hyunho Lee, Hyunmok Choi +2
Trace-driven simulation is widely used to evaluate distributed training (DT) jobs in GPU clusters, but existing simulators either ignore network contention or approximate it with a…
Not All AI Agents Are Equal: Characterizing Resource and Performance Dynamics
Wonmi Choi, Minuk Park, Zhixiong Niu +3
LLM-based AI agents process user requests through iterative reasoning and tool execution, often involving the invocation of remote LLM APIs with local tool containers. This executi…
Xronos: Heterogeneity-Aware Tensor Parallelism for Collaborative LLM Fine-Tuning on Edge CPUs
Wonmi Choi, Sunjae Park, Dohyeok Kwon +4
Collaborative fine-tuning on edge devices adapts large language models to domain-specific data while keeping each device's data local. State-of-the-art (SOTA) collaborative fine-tu…
Parallelism Strategy Chaining for Fast Training Convergence
Minchul Kang, Changyong Shin, Younghun Go +4
Selecting a parallelism strategy - the configuration of data, tensor, and pipeline parallelism degrees together with micro- and global-batch sizes - largely determines the training…
Residual Sparsification via Output Importance for Compressing Mixture-of-Experts LLMs
Seungwoo Jung, Dohyeok Kwon, Seungmin Cha +4
Mixture-of-experts (MoE) architectures scale large language models efficiently, but they demand massive GPU memory. To cope with such demand, models are commonly compressed to redu…
Unified KV Pooling to Accelerate Long-Context LLM Serving
Minchul Kang, Changyong Shin, Jinwoo Jeong +6
Long-context LLM serving requires offloading KV caches to host-memory and SSDs, but existing mechanisms are not designed for such long contexts. We observe significant inefficienci…
Training Time Prediction for Mixed Precision-based Distributed Training
Minchul Kang, Changyong Shin, Jinwoo Jeong +5
Accurate prediction of training time in distributed deep learning is crucial for resource allocation, cost estimation, and job scheduling. We observe that the floating-point precis…
GPU Memory Prediction for Multimodal Model Training
Jinwoo Jeong, Minchul Kang, Younghun Go +5
As deep learning models in agentic AI systems grow in scale and complexity, GPU memory requirements increase and often exceed the available GPU memory capacity, so that out-of-memo…
Prediction of Permissioned Blockchain Performance for Resource Scaling Configurations
Seungwoo Jung, Yeonho Yoo, Gyeongsik Yang +1
Blockchain is increasingly offered as blockchain-as-a-service (BaaS) by cloud service providers. However, configuring BaaS appropriately for optimal performance and reliability res…