---
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](https://huggingface.co/datasets/MBZUAI/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

```python
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

* 🤗 Hugging Face Collection: [CASS on Hugging Face](https://huggingface.co/collections/MBZUAI/cass-6825b5bf7414503cf16f87b2)
* 📂 Code & Tools: [GitHub Repository](https://github.com/GustavoStahl/CASS)
* Paper: [Arxiv CASS](https://arxiv.org/abs/2505.16968)