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499<p align="center">
<img src="public/logo.webp" alt="RightNow Tile" width="80" />
</p>
<h1 align="center">RightNow Tile</h1>
<p align="center">
<strong>CUDA SIMT to cuTile Python Transpiler</strong><br>
Transform your CUDA kernels for NVIDIA Blackwell GPUs
</p>
<p align="center">
<a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-green.svg" alt="License: MIT"></a>
<a href="https://nextjs.org/"><img src="https://img.shields.io/badge/Next.js-16-black" alt="Next.js 16"></a>
<a href="https://www.typescriptlang.org/"><img src="https://img.shields.io/badge/TypeScript-5.9-blue" alt="TypeScript"></a>
<a href="https://docs.nvidia.com/cuda/cutile-python/"><img src="https://img.shields.io/badge/cuTile-Blackwell-76B900" alt="cuTile"></a>
<a href="https://discord.gg/sSJqgNnq6X"><img src="https://img.shields.io/badge/Discord-Join%20Us-5865F2" alt="Discord"></a>
</p>
<p align="center">
<a href="https://tile.rightnowai.co">Live Demo</a> •
<a href="#quick-start">Quick Start</a> •
<a href="#features">Features</a> •
<a href="#supported-patterns">Patterns</a> •
<a href="https://discord.gg/sSJqgNnq6X">Discord</a>
</p>
---
## What is RightNow Tile?
**RightNow Tile** is a production-grade transpiler that converts traditional CUDA SIMT (Single Instruction, Multiple Threads) kernels into [cuTile](https://docs.nvidia.com/cuda/cutile-python/) Python code — NVIDIA's new tile-based programming model optimized for **Blackwell GPUs** (compute capability 10.x+).
Part of the [RightNow AI](https://rightnowai.co) ecosystem — a code editor built for GPU kernel development.
<br>
## Why cuTile?
NVIDIA's cuTile represents a paradigm shift in GPU programming:
| Traditional CUDA | cuTile |
|------------------|--------|
| Thread-centric programming | Tile-centric programming |
| Manual memory coalescing | Automatic tile-based loads |
| Complex index calculations | Declarative tile operations |
| Low-level synchronization | High-level tile semantics |
**RightNow Tile** bridges the gap — take your existing CUDA kernels and transform them for next-gen hardware.
<br>
## Quick Start
```bash
# Clone the repository
git clone https://github.com/RightNow-AI/RightNow-Tile.git
cd RightNow-Tile
# Install dependencies
npm install
# Start development server
npm run dev
```
Open [http://localhost:3000](http://localhost:3000) and start transpiling!
<br>
## Features
### Intelligent Pattern Detection
Automatically identifies **18 computational patterns** with **60+ variant-specific optimizations**:
```
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐
│ Your CUDA │ ──► │ Pattern Match │ ──► │ Optimized │
│ Kernel │ │ + Analysis │ │ cuTile Code │
└─────────────────┘ └──────────────────┘ └─────────────────┘
```
### 9-Stage Transpilation Pipeline
```
CUDA Source
│
▼
┌──────────────┐
│ 1. Extractor │ Parse kernel signatures, parameters, memory accesses
└──────┬───────┘
▼
┌──────────────┐
│ 2. Parser │ Recognize 150+ CUDA intrinsics & index patterns
└──────┬───────┘
▼
┌──────────────┐
│ 3. Semantic │ Detect reductions, dependencies, race conditions
└──────┬───────┘
▼
┌──────────────┐
│ 4. Memory │ Analyze coalescing, bank conflicts, access patterns
└──────┬───────┘
▼
┌──────────────┐
│ 5. Pattern │ Match against 18 patterns with confidence scoring
└──────┬───────┘
▼
┌──────────────┐
│ 6. IR Build │ Generate intermediate representation with config
└──────┬───────┘
▼
┌──────────────┐
│ 7. Optimize │ Select optimal tile sizes & configurations
└──────┬───────┘
▼
┌──────────────┐
│ 8. CodeGen │ Apply variant-specific templates
└──────┬───────┘
▼
┌──────────────┐
│ 9. Validate │ Verify correctness & generate diagnostics
└──────┴───────┘
│
▼
cuTile Python
```
### Modern Developer Experience
- **Monaco Editor** — VS Code-quality editing with syntax highlighting
- **Real-time Transpilation** — See results instantly
- **Dark/Light Themes** — Easy on the eyes
- **Expandable Output** — Full-screen code view
- **One-Click Copy** — Get your code ready to deploy
<br>
## Supported Patterns
### Core Compute Patterns
| Pattern | Variants | Use Cases | Confidence |
|---------|----------|-----------|------------|
| **GEMM** | `naive`, `tiled`, `register_blocked` | Matrix multiplication, deep learning | High |
| **Reduction** | `tree`, `warp_shuffle`, `multi_block`, `segmented` | Sum, max, min, dot product | High |
| **Scan** | `inclusive`, `exclusive`, `segmented` | Prefix sum, stream compaction | High |
| **Stencil** | `1d_3pt`, `1d_5pt`, `2d_5pt`, `2d_9pt`, `3d` | Image processing, PDE solvers | High |
| **Elementwise** | `simple`, `vectorized` | Point-wise operations | High |
### ML/Deep Learning Patterns
| Pattern | Variants | Use Cases | Confidence |
|---------|----------|-----------|------------|
| **Attention** | `flash_attention`, `flash_attention_v2`, `multi_head`, `causal`, `cross` | Transformer models | High |
| **Normalization** | `layernorm`, `rmsnorm`, `batchnorm`, `groupnorm`, `instancenorm` | Neural network layers | High |
| **Convolution** | `conv1d`, `conv2d`, `conv3d`, `depthwise`, `grouped`, `winograd`, `im2col` | CNNs, signal processing | High |
| **Pooling** | `max_pool_2d`, `avg_pool_2d`, `global_avg`, `global_max`, `adaptive` | Feature downsampling | High |
| **Embedding** | `lookup`, `embedding_bag`, `positional` | NLP, recommender systems | Medium |
### LLM/Transformer-Specific Patterns
| Pattern | Variants | Use Cases | Confidence |
|---------|----------|-----------|------------|
| **RoPE** | `standard`, `neox`, `cached` | Rotary position embeddings | High |
| **KV Cache** | `append`, `paged`, `prefix`, `gqa` | LLM inference optimization | High |
| **Quantization** | `int8`, `int4`, `fp8`, `dequantize` | Model compression | Medium |
| **Fused** | `matmul_activation`, `matmul_bias_activation`, `layernorm_residual` | Kernel fusion | Medium |
### Specialized Patterns
| Pattern | Variants | Use Cases | Confidence |
|---------|----------|-----------|------------|
| **FFT** | `radix2`, `radix4`, `radix8`, `inverse`, `real` | Signal processing | High |
| **Sparse** | `spmv_csr`, `spmv_csr_warp`, `spmv_coo`, `spmv_ell`, `spmm`, `sddmm` | Sparse matrix operations | Medium |
| **Histogram** | `atomic`, `privatized`, `multipass`, `weighted`, `2d` | Data distribution, statistics | Medium |
| **Sorting** | `bitonic`, `bitonic_shared`, `radix`, `merge` | Parallel sorting | Medium |
<br>
## Example
**Input: CUDA SIMT Kernel**
```cuda
__global__ void vectorAdd(float* a, float* b, float* c, int n) {
int idx = blockIdx.x * blockDim.x + threadIdx.x;
if (idx < n) {
c[idx] = a[idx] + b[idx];
}
}
```
**Output: cuTile Python**
```python
import cuda_tile as ct
import cupy
TILE_SIZE = 256
@ct.kernel
def vector_add(a, b, c, n: ct.Constant[int], tile_size: ct.Constant[int]):
"""
Elementwise kernel - auto-transpiled from CUDA
Original: vectorAdd
Confidence: 100%
"""
pid = ct.bid(0)
# Load input tiles
a_tile = ct.load(a, index=(pid,), shape=(tile_size,))
b_tile = ct.load(b, index=(pid,), shape=(tile_size,))
# Compute
result = a_tile + b_tile
# Store result
ct.store(c, index=(pid,), tile=result)
def launch_vector_add(a, b, c):
"""Launch the vector_add kernel"""
n = a.shape[0]
grid = (ct.cdiv(n, TILE_SIZE), 1, 1)
stream = cupy.cuda.get_current_stream()
ct.launch(stream, grid, vector_add, (a, b, c, TILE_SIZE))
```
### Flash Attention Example
**Input: Flash Attention CUDA Kernel**
```cuda
__global__ void flash_attention_kernel(
float* Q, float* K, float* V, float* O,
int seq_len, int head_dim, float scale
) {
// Complex multi-phase attention implementation
// with online softmax and tiled matrix multiply
...
}
```
**Output: cuTile Python (Flash Attention)**
```python
import cuda_tile as ct
import cupy
BLOCK_Q = 64
BLOCK_KV = 64
@ct.kernel
def flash_attention(
Q, K, V, O,
seq_len_q: ct.Constant[int],
seq_len_kv: ct.Constant[int],
head_dim: ct.Constant[int],
scale: ct.Constant[float],
block_q: ct.Constant[int],
block_kv: ct.Constant[int]
):
"""
Flash Attention kernel - auto-transpiled from CUDA
Confidence: 95%
Variant: flash_attention_v2
"""
block_q_idx = ct.bid(0)
head_idx = ct.bid(1)
# Initialize output accumulator and softmax stats
acc = ct.zeros((block_q, head_dim), dtype=ct.float32)
m_i = ct.full((block_q,), float('-inf'), dtype=ct.float32)
l_i = ct.zeros((block_q,), dtype=ct.float32)
# Load Q tile (stays in registers)
q_tile = ct.load(Q, index=(head_idx, block_q_idx), shape=(block_q, head_dim))
# Iterate over K,V blocks with online softmax
for block_kv_idx in range(0, ct.cdiv(seq_len_kv, block_kv)):
k_tile = ct.load(K, index=(head_idx, block_kv_idx), shape=(block_kv, head_dim))
v_tile = ct.load(V, index=(head_idx, block_kv_idx), shape=(block_kv, head_dim))
# QK^T with scaling
qk = ct.tile_matmul(q_tile, ct.transpose(k_tile)) * scale
# Online softmax update
m_ij = ct.reduce(qk, op=ct.max, axis=1)
m_new = ct.maximum(m_i, m_ij)
alpha = ct.exp(m_i - m_new)
acc = acc * alpha[:, None]
l_i = l_i * alpha
p = ct.exp(qk - m_new[:, None])
l_ij = ct.reduce(p, op=ct.sum, axis=1)
l_i = l_i + l_ij
# Accumulate output
acc = acc + ct.tile_matmul(p, v_tile)
m_i = m_new
# Normalize and store
out = acc / l_i[:, None]
ct.store(O, index=(head_idx, block_q_idx), tile=out)
```
<br>
## API Usage
Use the transpiler programmatically:
```typescript
import { transpile } from './lib/transpiler';
const result = await transpile(cudaCode);
// Access results
result.tileCode // Generated cuTile Python code
result.pattern.archetype // Detected pattern (e.g., 'attention', 'gemm')
result.pattern.confidence // Confidence score (0-1)
result.pattern.variant // Specific variant (e.g., 'flash_attention_v2')
result.validation.isValid // Validation status
result.diagnostics // Warnings and suggestions
result.memoryAnalysis // Memory access analysis
result.semanticAnalysis // Semantic analysis results
```
### REST API
```bash
curl -X POST http://localhost:3000/api/transpile \
-H "Content-Type: application/json" \
-d '{"code": "__global__ void add(float* a, float* b, float* c, int n) { ... }"}'
```
<br>
## Project Structure
```
rightnow-tile/
├── app/
│ ├── api/transpile/ # REST API endpoint
│ ├── components/ # React components
│ │ ├── ScientificVisualization.tsx
│ │ ├── ThemeProvider.tsx
│ │ └── ThemeToggle.tsx
│ ├── page.tsx # Main UI
│ └── globals.css # Styling
├── lib/
│ ├── ast/ # AST extraction & semantic analysis
│ │ ├── extractor.ts # Kernel parsing
│ │ ├── semantic-analyzer.ts
│ │ ├── memory-analyzer.ts
│ │ ├── phase-analyzer.ts # Multi-phase kernel detection
│ │ └── types.ts # 18 archetypes, 60+ variants
│ ├── parser/
│ │ └── intrinsics.ts # 150+ CUDA intrinsics
│ ├── patterns/ # Pattern matchers (18 patterns)
│ │ └── matchers/
│ │ ├── attention.ts # Flash Attention, MHA
│ │ ├── fused.ts # Fused kernels
│ │ ├── fft.ts # FFT variants
│ │ ├── gemm.ts # Matrix multiply
│ │ ├── reduction.ts # Reductions
│ │ ├── scan.ts # Prefix sums
│ │ ├── stencil.ts # Stencil patterns
│ │ ├── sparse.ts # Sparse matrix ops
│ │ ├── histogram.ts # Histogram
│ │ ├── convolution.ts # CNN convolutions
│ │ ├── sorting.ts # Sorting algorithms
│ │ ├── pooling.ts # Pooling layers
│ │ ├── normalization.ts # Norm layers
│ │ ├── embedding.ts # Embeddings
│ │ ├── rope.ts # Rotary embeddings
│ │ ├── kvcache.ts # KV cache ops
│ │ ├── quantization.ts # Quantization
│ │ └── elementwise.ts
│ ├── ir/ # Intermediate representation
│ │ ├── builder.ts # 11 specialized IR types
│ │ ├── optimizer.ts
│ │ └── types.ts
│ ├── codegen/ # Code generation
│ │ ├── generator.ts # Routes to all 18 archetypes
│ │ └── templates/ # 14 template files
│ │ ├── attention.ts
│ │ ├── fused.ts
│ │ ├── sparse.ts
│ │ ├── histogram.ts
│ │ ├── convolution.ts
│ │ ├── sorting.ts
│ │ ├── pooling.ts
│ │ ├── normalization.ts
│ │ ├── embedding.ts
│ │ ├── rope.ts
│ │ ├── kvcache.ts
│ │ ├── quantization.ts
│ │ ├── reduction.ts
│ │ └── stencil.ts
│ ├── validation/ # Validation & diagnostics
│ └── transpiler.ts # Main entry point
├── docs/ # Documentation
└── public/ # Static assets
```
<br>
## Tech Stack
- **Framework**: [Next.js 16](https://nextjs.org/) with Turbopack
- **Language**: [TypeScript 5.9](https://www.typescriptlang.org/)
- **UI**: [React 19](https://react.dev/), [Tailwind CSS](https://tailwindcss.com/), [Framer Motion](https://www.framer.com/motion/)
- **Editor**: [Monaco Editor](https://microsoft.github.io/monaco-editor/)
- **Target**: [NVIDIA cuTile](https://docs.nvidia.com/cuda/cutile-python/)
<br>
## Requirements
- **Node.js** 18+
- **npm** or **yarn**
- For running generated code: **NVIDIA Blackwell GPU** (compute capability 10.x+)
<br>
## Production Deployment
```bash
# Build for production
npm run build
# Start production server
npm start
```
Deploy to Vercel, AWS, or any Node.js hosting platform.
<br>
## Contributing
We welcome contributions! Here's how to get started:
1. **Fork** the repository
2. **Create** your feature branch (`git checkout -b feature/amazing-feature`)
3. **Commit** your changes (`git commit -m 'Add amazing feature'`)
4. **Push** to the branch (`git push origin feature/amazing-feature`)
5. **Open** a Pull Request
### Development
```bash
# Run development server
npm run dev
# Type checking
npx tsc --noEmit
# Build
npm run build
```
<br>
## Roadmap
- [x] Support for 18 CUDA patterns with 60+ variants
- [x] Flash Attention and Transformer-specific patterns
- [x] LLM inference patterns (RoPE, KV Cache, Quantization)
- [x] Comprehensive convolution support (Winograd, im2col)
- [ ] Batch transpilation for multiple kernels
- [ ] Performance benchmarking comparisons
- [ ] VS Code extension integration
- [ ] CLI tool for CI/CD pipelines
- [ ] CUDA to Triton transpilation
<br>
## License
This project is licensed under the **MIT License** — see the [LICENSE](LICENSE) file for details.
<br>
## Links
<p align="center">
<a href="https://rightnowai.co"><strong>RightNow AI</strong></a> · GPU Kernel Code Editor<br><br>
<a href="https://tile.rightnowai.co">Live Demo</a> •
<a href="https://docs.nvidia.com/cuda/cutile-python/">cuTile Docs</a> •
<a href="https://discord.gg/sSJqgNnq6X">Discord</a> •
<a href="https://github.com/RightNow-AI/RightNow-Tile/issues">Issues</a>
</p>
---
<p align="center">
Made with ♥ by <a href="https://rightnowai.co">RightNow AI</a>
</p>