winder 0.1.0b2

Fast Differentiable Winding Numbers on GPU

winder

Building massively parallel, GPU-accelerated C++/CUDA spatial structures to evaluate geometric winding numbers with extreme performance.

⚠️ Active Beta Warning winder is currently in active pre-release development. The API is unstable and subject to breaking changes. Advanced features—such as analytical field derivatives—are currently being implemented. Use with caution in production environments.

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💡 The Concept

winder is designed to compute 3D geometric winding numbers across massive query grids entirely on the GPU. By leveraging highly optimized spatial data structures (such as specialized 8-ary Bounding Volume Hierarchies), the library collapses the typical $\mathcal{O}(N \times M)$ brute-force complexity of mesh evaluation down to high-speed logarithmic bounds.

Whether you are evaluating solid angles of open sheets, identifying inside/outside boundaries of complex closed manifolds, or computing continuous volumetric fields for graphics and physical simulations, winder handles millions of primitives and queries seamlessly. Integration to torch, tensorflow, jax, etc works via DLPack-capsules.

For an in-depth dive into the underlying mathematical principles, spatial layout partitioning, and performance benchmarks, read our formal write-up:

👉 Read the Technical Report

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🚀 Installation

Because winder is currently in beta, releases are published to PyPI as pre-releases. To install it, you must explicitly pass the --pre flag to your package manager:

pip install winder --pre

System Requirements

• OS: Linux (ManyLinux 2.28+ compliant distributions)
• Compute: NVIDIA GPU
• Driver: NVIDIA Driver supporting CUDA 13.3 or newer (the wheel bundles its own CUDA 13 runtime components, but relies on your host system driver).

🛠️ Quick Start & Usage

winder integrates directly with PyTorch tensors, executing heavy compute pipelines in CUDA space without unnecessary host-to-device memory roundtrips.

1. Interactive Documentation You can explore the available internal module bindings, arguments, and function structures directly through Python's built-in inspection tool:

import winder
# View the full module documentation, signatures, and docstrings
help(winder)

2. Basic Example: Brute-Force vs. Accelerated Compute

Here is a minimal script generating spatial queries and evaluating their winding numbers using both the exact baseline evaluator and the optimized hierarchical tree system:

import torch
import winder

# Verify CUDA availability
if not torch.cuda.is_available():
    print("No cuda device found. Returning.")
    exit()

device = torch.device("cuda:0")
print(f"Running winder on device: {device}")

#  Generate a batch of spatial query points (e.g., 1000,000 points in 3D space)
queries = torch.rand((1000000, 3), dtype=torch.float32, device=device)

# Define a simple triangle mesh layout [num_triangles, 3_vertices, 3_coordinates]
# Here we define a single right-angle triangle positioned on the XY plane
triangles = torch.tensor([
    [[0.0, 0.0, 0.0], 
     [1.0, 0.0, 0.0], 
     [0.0, 1.0, 0.0]]
], dtype=torch.float32, device=device)

# Create winder enginge
engine = winder.WinderEngine(triangles)

print("Evaluating winding fields...")
# Method A: Calculate using the exact O(N*M) brute force method
winding_brute = engine.brute_force(queries)

# Method B: Calculate using the optimized accelerated tree structure
winding_fast = engine.compute(queries)

# Load DLPack-capsules into torch tensors
winding_brute = torch.from_dlpack(winding_brute)
winding_fast = torch.from_dlpack(winding_fast)

# Compare results
print(f"Brute-force output shape: {winding_brute.shape}")
print(f"Accelerated output shape: {winding_fast.shape}")
# ... do whatever you want

📜 License

This project is licensed under the MIT License - see the LICENSE file for details.