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README.md

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+---
+tags:
+- gpu-runtime-prediction
+- code-understanding
+- regression
+- performance-modeling
+datasets:
+- RajBhope/gpu-runtime-prediction-dataset
+language:
+- code
+library_name: scikit-learn
+pipeline_tag: tabular-regression
+---
+
+# GPU Runtime Predictor 🚀⚡
+
+Predicts GPU kernel/operation **runtime in milliseconds** given **source code** + **GPU hardware specifications**.
+
+## How It Works
+
+1. **Code Feature Extraction**: Analyzes source code to extract 48 features (tensor dimensions, operation types, complexity indicators)
+2. **GPU Feature Encoding**: Uses 12 hardware specs (CUDA cores, memory bandwidth, compute capability, etc.)
+3. **ML Prediction**: Ensemble of Gradient Boosted Trees + Random Forest + Neural Network
+
+### Model Comparison
+
+| Model | R² | RMSE | Spearman ρ | MAPE % |
+|-------|-----|------|------------|--------|
+| **GBR** | 0.9923 | 0.0728 | 0.9264 | 16.5% |
+| **RF** | 0.9924 | 0.0724 | 0.9277 | 16.3% |
+| **NN** | 0.9932 | 0.0687 | 0.9187 | 17.0% |
+| **Ensemble** | 0.9930 | 0.0693 | 0.9272 | 16.3% |
+
+### GPU Catalog (12 GPUs)
+
+| GPU | FP32 TFLOPS | Memory BW | VRAM |
+|-----|------------|-----------|------|
+| NVIDIA T4 | 8.1 | 320 GB/s | 16 GB |
+| NVIDIA V100 | 15.7 | 900 GB/s | 32 GB |
+| NVIDIA A10G | 31.2 | 600 GB/s | 24 GB |
+| NVIDIA A100 40GB | 19.5 | 1555 GB/s | 40 GB |
+| NVIDIA A100 80GB | 19.5 | 2039 GB/s | 80 GB |
+| NVIDIA L4 | 30.3 | 300 GB/s | 24 GB |
+| NVIDIA L40S | 91.6 | 864 GB/s | 48 GB |
+| NVIDIA RTX 3090 | 35.6 | 936 GB/s | 24 GB |
+| NVIDIA RTX 4090 | 82.6 | 1008 GB/s | 24 GB |
+| NVIDIA H100 SXM | 67.0 | 3350 GB/s | 80 GB |
+| NVIDIA H100 PCIe | 48.0 | 2039 GB/s | 80 GB |
+| NVIDIA RTX A6000 | 38.7 | 768 GB/s | 48 GB |
+
+### 15 Supported Workload Types
+matmul, conv2d, attention, transformer_block, linear, layernorm, batchnorm, 
+softmax, embedding, elementwise, reduction, pooling, FFT, sort, loss+backward
+
+## Usage
+
+```python
+# See the Gradio demo for interactive use
+# Or load models directly:
+import pickle
+with open('model_gbr.pkl', 'rb') as f:
+    model = pickle.load(f)
+```
+
+## Training
+
+- **Dataset**: [RajBhope/gpu-runtime-prediction-dataset](https://hf.co/datasets/RajBhope/gpu-runtime-prediction-dataset)
+- **51,900 samples** = 4,325 workloads × 12 GPUs
+- Runtime generated via physics-based roofline performance model
+- Based on research from [Regression Language Models](https://arxiv.org/abs/2509.26476) and [HELP](https://arxiv.org/abs/2106.08630)