RegOT-CUDA (cuRegOT) is a CUDA-accelerated library for optimal transport computation, providing high-performance implementations of regularized optimal transport algorithms.

As a complement to this library, the RegOT-Python repository provides efficient CPU-based solvers for regularized optimal transport.

Currently RegOT-CUDA solves the entropic-regularized optimal transport problem:

where $a\in\mathbb{R}^n$ and $b\in\mathbb{R}^m$ are two given probability vectors with $a_i>0$, $b_j>0$, $\sum_{i=1}^n a_i=\sum_{j=1}^m b_j=1$, and $M\in\mathbb{R}^{n\times m}$ is a given cost matrix. The function $h(T)=\sum_{i=1}^{n}\sum_{j=1}^{m}T_{ij}(1-\log T_{ij})$ is the entropy term, and $\eta>0$ is a regularization parameter.

RegOT-CUDA is a work in progress. Currently we have implemented the block coordinate descent algorithm (BCD, equilavent to the well-known Sinkhorn algorithm) and the sparse-plus-low-rank quasi-Newton method (SPLR) for entropic-regularized optimal transport. More state-of-the-art solvers are under development, and a list of candidate algorithms can be found in the RegOT-Python package.

• Python >= 3.11
• NumPy >= 1.23.0
• PyTorch >= 2.10 (optional, for PyTorch interface)
• CUDA Toolkit >= 13.0
• The cuDSS library
• Compatible NVIDIA GPU
• C++ compiler (C++11 or higher, for building from source)

You can simply install cuRegOT using the pip command, and dependent CUDA runtime libraries will be automatically installed:

pip install curegot

The CUDA development environment is required to build this package from source. You can install the C++ compiler and CUDA libraries using Conda:

# Create CUDA development environment
conda create -n nvdev
conda activate nvdev
conda install python=3.12 gxx_linux-64
conda install -c nvidia cuda-toolkit=13.2 libcudss-dev cuda-nvtx-dev
pip install torch --index-url https://download.pytorch.org/whl/cu130
pip install numpy requests pybind11

You also need to set the CUDA_HOME environment variable, for example:

export CUDA_HOME=/usr/local/cuda

If you use the Conda installation method above, you can run the following command to set the environment variable for the virtual environment:

conda activate nvdev
conda env config vars set CUDA_HOME="<path_to_conda>/envs/nvdev/"

cd regot-cuda
pip install --no-build-isolation .

python -c "import curegot; print('RegOT-CUDA imported successfully')"

NumPy interface:

import numpy as np
import curegot

# Create data
np.random.seed(123)
n, m = 100, 80
M = np.random.rand(n, m)  # Cost matrix
a = np.random.rand(n)     # Source distribution
a = a / np.sum(a)         # Normalize
b = np.random.rand(m)     # Target distribution
b = b / np.sum(b)         # Normalize
reg = 0.1                 # Regularization parameter

# Call algorithm
result1 = curegot.numpy.sinkhorn_bcd(M, a, b, reg, tol=1e-6, max_iter=1000, verbose=0)
plan1 = result1["plan"]
print(plan1[:3, :3])

result2 = curegot.numpy.sinkhorn_splr(M, a, b, reg, tol=1e-6, max_iter=1000, verbose=0)
plan2 = result2["plan"]
print(plan2[:3, :3])

PyTorch interface:

import torch
import curegot

# Create data
torch.manual_seed(123)
n, m = 100, 80
device = "cuda" if torch.cuda.is_available() else "cpu"
M = torch.rand(n, m, device=device)  # Cost matrix
a = torch.rand(n, device=device)     # Source distribution
a = a / torch.sum(a)                 # Normalize
b = torch.rand(m, device=device)     # Target distribution
b = b / torch.sum(b)                 # Normalize
reg = 0.1                            # Regularization parameter

# Call algorithm
result1 = curegot.torch.sinkhorn_bcd(M, a, b, reg, tol=1e-6, max_iter=1000, verbose=0)
plan1 = result1["plan"]
print(plan1[:3, :3])

result2 = curegot.torch.sinkhorn_splr(M, a, b, reg, tol=1e-6, max_iter=1000, verbose=0)
plan2 = result2["plan"]
print(plan2[:3, :3])

cd regot-cuda/test
pip install regot
python test_sinkhorn_bcd.py
python test_sinkhorn_splr.py

The plot below shows the benchmark result for some popular GPU-based solvers. The horizontal axis represents the elapsed wall time, and the vertical axis is the optimization error on a logarithmic scale. Lower curves indicate better performance, achieving lower errors in less time.

More details on the benchmark can be found in the paper.

Please consider to cite our work if you find our algorithms or software useful in your research and applications.

@inproceedings{qiu2026curegot,
  title={{{cuRegOT}}: A {{GPU}}-Accelerated Solver for Entropic-Regularized Optimal Transport},
  author={Qiu, Yixuan},
  booktitle={Forty-third International Conference on Machine Learning},
  year={2026}
}