CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark
This addresses a critical gap in low-level GPU code portability for developers and researchers working with heterogeneous hardware.
The paper tackles the problem of cross-architecture GPU code transpilation between Nvidia and AMD platforms by introducing CASS, a dataset and model suite that achieves 95% source translation accuracy and 37.5% assembly translation accuracy, with generated code matching native performance in over 85% of test cases.
We introduce CASS, the first large-scale dataset and model suite for cross-architecture GPU code transpilation, targeting both source-level (CUDA <--> HIP) and assembly-level (Nvidia SASS <--> 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.