From the 1 of 4 linked papers with an AI index.
4 papers
Are LLM-Generated GPU Kernels Production-Ready? A Trace-Driven Benchmark and Optimization Agent
Lingyun Yang, Yuxiao Wang, Shenghao Liang +8
The paper introduces Atrex-Bench, a trace-driven GPU kernel benchmark derived from real production inference workloads, and evaluates LLM-generated kernels, revealing a large perfo…
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…
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…
EDGC: Entropy-driven Dynamic Gradient Compression for Efficient LLM Training
Qingao Yi, Jiaang Duan, Hanwen Hu +10
Training large language models (LLMs) poses significant challenges regarding computational resources and memory capacity. Although distributed training techniques help mitigate the…