LLMPerf: GPU Performance Modeling meets Large Language Models

This repository contains the source code for creating large-scale performance datasets, training and evaluating the LLMPerf model. The model is used to predict the performance of OpenCL kernels on a specific device.

The folder structure is as follows:

• performance-model: Source code for training and evaluating the LLMPerf model.
• cldrive: Source code for automatically running OpenCL kernels on a specific device and collecting performance data.
• mem-access-analysis: Source code for inserting memory access hook into OpenCL kernels, used to collect memory access traces and generate bound-aware datasets.

Citation

If you use this codebase, or otherwise find our work valuable, please cite our paper:

@inproceedings{nguyen2024llmperf,
  title={LLMPerf: GPU Performance Modeling meets Large Language Models},
  author={Nguyen-Nhat, Minh-Khoi and Do, Hoang Duy Nguyen and Le, Huyen Thao and Dao, Thanh Tuan},
  booktitle={Proceedings of the International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems},
  year={2024},
  organization={IEEE}
}