CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark
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}}.
683656aefd55e753bf26ed3e
Ahmed Heakl
claimed_verified
2025-05-28T08:58:30.760Z
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Ahmed Heakl
user
ahmedheakl
683656aefd55e753bf26ed3f
Sarim Hashmi
claimed_verified
2025-06-02T07:49:01.879Z
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SARIM HASHMI
user
Sarim-Hash
683656aefd55e753bf26ed40
Gustavo Bertolo Stahl
claimed_verified
2025-06-05T08:31:48.782Z
62eaadf4086bd1debb30a122
https://cdn-avatars.huggingface.co/v1/production/uploads/62eaadf4086bd1debb30a122/wgxsPVnkOuEfq1oqlUhiB.jpeg
Gustavo Stahl
user
GustavoStahl
683656aefd55e753bf26ed41
Seung Hun Eddie Han
683656aefd55e753bf26ed42
Salman Khan
683656aefd55e753bf26ed43
Abdulrahman Mahmoud
CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark
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}}.
CASS is a dataset and model suite for GPU code transpilation at both source and assembly levels, achieving high accuracy and performance matching with native code.
cross-architecture GPU code transpilation CASS CUDA HIP Nvidia SASS AMD RDNA3 domain-specific language models source translation accuracy assembly translation accuracy native performance CASS-Bench GPU compiler tooling binary compatibility LLM-guided hardware translation