3 citations · 3 across the 4 of their papers we have counts for
5 papers
OverlapAdvisor: Modeling and Adapting Computation-Communication Overlap for Distributed MoE Serving
Chutong Ding, Kaixuan Zhang, Luping Wang +5
Mixture-of-Experts (MoE) inference with expert parallelism (EP) relies on All-to-All communication, making computation-communication overlap strategies critical for achieving high…
Are LLM-Generated GPU Kernels Production-Ready? A Trace-Driven Benchmark and Optimization Agent
Lingyun Yang, Yuxiao Wang, Shenghao Liang +8
Existing GPU kernel generation benchmarks draw problems from synthetic or curated sources that diverge from deployed workloads. We present Atrex-Bench, a benchmark whose 30 operato…
WaveTune: Wave-aware Bilinear Modeling for Efficient GPU Kernel Auto-tuning
Kaixuan Zhang, Chutong Ding, Shiyou Qian +6
The rapid adoption of Large Language Models (LLMs) has made GPU inference efficiency an increasingly critical system concern. The runtime of LLM workloads is largely dominated by t…
PipeWeave: Synergizing Analytical and Learning Models for Unified GPU Performance Prediction
Kaixuan Zhang, Yunfan Cui, Shuhao Zhang +8
The rapid expansion of Transformer-based large language models has dramatically increased the need for high-performance GPUs. As a result, there is growing demand for fast, accurat…
Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies
Luping Wang, Sheng Chen, Linnan Jiang +4
The large models, as predicted by scaling raw forecasts, have made groundbreaking progress in many fields, particularly in natural language generation tasks, where they have approa…