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metadata
license: mit
dataset_info:
  features:
    - name: filename
      dtype: string
    - name: cuda_source
      dtype: string
    - name: cuda_host
      dtype: string
    - name: cuda_device
      dtype: string
    - name: hip_source
      dtype: string
    - name: hip_host
      dtype: string
    - name: hip_device
      dtype: string
  splits:
    - name: train
      num_bytes: 18979794237
      num_examples: 70694
    - name: stack
      num_bytes: 6087813411
      num_examples: 24170
    - name: synth
      num_bytes: 11766271412
      num_examples: 40591
    - name: bench
      num_bytes: 3676152
      num_examples: 40
  download_size: 10789629544
  dataset_size: 36837555212
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: stack
        path: data/stack-*
      - split: synth
        path: data/synth-*
      - split: bench
        path: data/bench-*

💻 CASS: CUDA–AMD Assembly and Source Mapping

CASS is the first large-scale dataset for cross-architecture GPU transpilation, providing semantically aligned CUDA–HIP source pairs and their corresponding host/device assemblies for NVIDIA (SASS) and AMD (RDNA3) platforms. It enables research in:

  • 🔁 Source-to-source translation (CUDA ↔ HIP)
  • ⚙️ Assembly-level translation (SASS ↔ RDNA3)
  • 🧠 LLM-guided GPU code transpilation

📚 Dataset Structure

Each sample contains the following fields:

Field Description
filename Sample ID or file name
cuda_source Original CUDA source code
cuda_host Compiled x86 host-side assembly from CUDA
cuda_device Compiled SASS (Nvidia GPU) device assembly
hip_source Transpiled HIP source code (via HIPIFY)
hip_host Compiled x86 host-side assembly from HIP
hip_device Compiled RDNA3 (AMD GPU) device assembly

🔀 Dataset Splits

Split Description # Examples
train Union of synth, stack, and opencl 70,694
synth LLM-synthesized CUDA programs 40,591
stack Scraped and filtered CUDA from StackV2 24,170
bench 40 curated eval tasks from 16 GPU domains 40

📦 How to Load 

from datasets import load_dataset

# 🧠 Load the full dataset (default config with all splits)
cass = load_dataset("MBZUAI/cass", name="default")

# Access a specific split
train_data = cass["train"]     # train = stack + synth + opencl
stack_data = cass["stack"]
synth_data = cass["synth"]
bench_data = cass["bench"]

📈 Benchmark and Evaluation

The bench split includes 40 samples across 16 domains like:

  • 🧪 Physics Simulation
  • 📊 Data Structures
  • 📸 Image Processing
  • 🧮 Linear Algebra

All samples have been manually verified for semantic equivalence across CUDA and HIP and come with executable device/host binaries.

📄 License

Released under the MIT license.

🔗 Useful Links