- By:
- Jin, Zheming ; Pophale, Swaroop S; Teranishi, Keita
- Page Number:
- 524-533
- Book Title:
- Proceedings of the SC '25 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis
- Publication Date:
- March 12, 2026
- Conference Name:
- SC Workshops '25: Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis
- Conference Location:
- St Louis, Missouri, United States of America
- Conference Sponsor:
- ACM and IEEE
- View DOI Listing:
- https://doi.org/10.1145/3731599.3767398
Abstract
Large Language Models (LLMs) have shown strong capabilities in general code translation. However, code translation involving parallel programming models remains largely unexplored. This work enhances the capabilities of code LLMs in CUDA-to-SYCL kernel translation with parameter-efficient fine-tuning. The resultant fine-tuned LLM, called ChatPORT, is an effort to provide high-fidelity translations from one programming model to another. We describe the preparation of datasets from heterogeneous computing benchmarks for model fine-tuning and testing, the parameter-efficient fine-tuning of 19 open-source code models ranging in size from 0.5 to 34 billion parameters and evaluate the correctness rates of the SYCL kernels by the fine-tuned models. The experimental results show that most code models fail to translate CUDA codes to SYCL correctly. However, fine-tuning these models using a small set of CUDA and SYCL kernels can enhance the capabilities of these models in kernel translation. Depending on the sizes of the models, the correctness rate ranges from 19.9% to 81.7% for a test dataset of 62 CUDA kernels.
