CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark
Authors: name: Ahmed Heakl, name: Sarim Hashmi, name: Gustavo Bertolo Stahl, name: Seung Hun Eddie Han, name: Salman Khan, name: Abdulrahman Mahmoud
Organization: Mohamed Bin Zayed University of Artificial Intelligence

We introduce CASS, the first large-scale dataset and model suite for
cross-architecture GPU code transpilation, targeting both source-level (CUDA
leftrightarrow HIP) and assembly-level (Nvidia SASS leftrightarrow AMD
RDNA3) translation. The dataset comprises 70k verified code pairs across host
and device, addressing a critical gap in low-level GPU code portability.
Leveraging this resource, we train the CASS family of domain-specific language
models, achieving 95% source translation accuracy and 37.5% assembly
translation accuracy, substantially outperforming commercial baselines such as
GPT-4o, Claude, and Hipify. Our generated code matches native performance in
over 85% of test cases, preserving runtime and memory behavior. To support
rigorous evaluation, we introduce CASS-Bench, a curated benchmark spanning 16
GPU domains with ground-truth execution. All data, models, and evaluation tools
are released as open source to foster progress in GPU compiler tooling, binary
compatibility, and LLM-guided hardware translation. Dataset and benchmark are
on
https://huggingface.co/datasets/MBZUAI/cass{blue{HuggingFace}},
with code at
https://github.com/GustavoStahl/CASS{blue{GitHub}}.