2 citations · 2 across the 11 of their papers we have counts for
7 papers · 1 filter
KVServe: Service-Aware KV Cache Compression for Communication-Efficient Disaggregated LLM Serving
Zedong Liu, Xinyang Ma, Dejun Luo +9
LLMs are widely adopted in production, pushing inference systems to their limits. Disaggregated LLM serving (e.g., PD separation and KV state disaggregation) improves scalability a…
TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training
Man Liu, Xingchen Liu, Xingjian Tian +8
Handling communication overhead in large-scale tensor-parallel training remains a critical challenge due to the dense, near-zero distributions of intermediate tensors, which exacer…
A Fully GPU-Accelerated Framework for High-Performance Configuration Interaction Selection with Neural Network Quantum States
Daran Sun, Bowen Kan, Haoquan Long +13
AI-driven methods have demonstrated considerable success in tackling the central challenge of accurately solving the Schrödinger equation for complex many-body systems. Among neura…
ElasticMM: Efficient Multimodal LLMs Serving with Elastic Multimodal Parallelism
Zedong Liu, Shenggan Cheng, Guangming Tan +2
Multimodal large language models (MLLMs) extend LLMs to handle images, videos, and audio by incorporating feature extractors and projection modules. However, these additional compo…
TSUE: A Two-Stage Data Update Method for an Erasure Coded Cluster File System
Zheng Wei, Jing Xing, Yida Gu +4
Compared to replication-based storage systems, erasure-coded storage incurs significantly higher overhead during data updates. To address this issue, various parity logging methods…
Overcoming Memory Constraints in Quantum Circuit Simulation with a High-Fidelity Compression Framework
Boyuan Zhang, Bo Fang, Fanjiang Ye +4
Full-state quantum circuit simulation requires exponentially increased memory size to store the state vector as the number of qubits scales, presenting significant limitations in c…
MPGemmFI: A Fault Injection Technique for Mixed Precision GEMM in ML Applications
Bo Fang, Xinyi Li, Harvey Dam +9
Emerging deep learning workloads urgently need fast general matrix multiplication (GEMM). To meet such demand, one of the critical features of machine-learning-specific accelerator…