TritonBench provides the first comprehensive benchmark for evaluating LLMs' capabilities in generating performance-optimized Triton code for GPU acceleration.

• Creates a specialized dataset of 74 diverse Triton operators
• Evaluates both correctness and performance of LLM-generated GPU code
• Reveals current LLMs struggle with producing optimized Triton operators
• Identifies key areas where LLMs need improvement for GPU programming

This research is crucial for engineering teams building AI acceleration infrastructure, as it highlights both the potential and current limitations of using LLMs to automate GPU kernel optimization, a traditionally manual and expertise-intensive task.

TritonBench: Benchmarking Large Language Model Capabilities for Generating Triton Operators