title: CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark
summary: 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}}.
paper_authors._id: 683656aefd55e753bf26ed3e
paper_authors.name: Ahmed Heakl
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-28T08:58:30.760Z
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paper_authors.fullname: Ahmed Heakl
paper_authors.type: user
paper_authors.user: ahmedheakl
paper_authors._id: 683656aefd55e753bf26ed3f
paper_authors.name: Sarim Hashmi
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-02T07:49:01.879Z
paper_authors._id: 62676a94dacab364889bb36c
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paper_authors.fullname: SARIM HASHMI
paper_authors.type: user
paper_authors.user: Sarim-Hash
paper_authors._id: 683656aefd55e753bf26ed40
paper_authors.name: Gustavo Bertolo Stahl
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T08:31:48.782Z
paper_authors._id: 62eaadf4086bd1debb30a122
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/62eaadf4086bd1debb30a122/wgxsPVnkOuEfq1oqlUhiB.jpeg
paper_authors.fullname: Gustavo Stahl
paper_authors.type: user
paper_authors.user: GustavoStahl
paper_authors._id: 683656aefd55e753bf26ed41
paper_authors.name: Seung Hun Eddie Han
paper_authors._id: 683656aefd55e753bf26ed42
paper_authors.name: Salman Khan
paper_authors._id: 683656aefd55e753bf26ed43
paper_authors.name: Abdulrahman Mahmoud
paper_title: CASS: Nvidia to AMD Transpilation with Data, Models, and Benchmark
paper_summary: 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}}.
paper_ai_summary: 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.
paper_ai_keywords: 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