3 papers
cs.LG2026
How to Train Your Little Dragon: Teaching a Small Language Model to Construct and Refine GPU Optimization Scripts
Annabelle Sujun Tang, Wei Tang, Songyao Jiang +7
Compiler transformations interact, making GPU kernel optimization a search for combinations that preserve the computation while improving performance. We introduce HOW TO TRAIN YOU…
cs.LG2026
MOSAIC: More LLMs, Less Cost through Collaborative Reasoning for Compiler Optimizations
Annabelle Sujun Tang, Christopher Priebe, Lianhui Qin +1
LLM-guided compiler optimization has recently shown promise. However, current LLM-guided optimizers query a single LLM at every search step, so their cost grows with both that mode…
cs.LG2025
REASONING COMPILER: LLM-Guided Optimizations for Efficient Model Serving
Annabelle Sujun Tang, Christopher Priebe, Rohan Mahapatra +2
While model serving has unlocked unprecedented capabilities, the high cost of serving large-scale models continues to be a significant barrier to widespread accessibility and rapid…