CuClarabel: GPU Acceleration for a Conic Optimization Solver
4 affiliations
- 1Department of Engineering Science, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland and School of Engineering, École Polytechnique Fédérale De Lausanne, Lausanne, Switzerland
- 2Department of Computer Science, Stanford University, Stanford, California, USA
- 3Department of Electrical Engineering, Stanford University, Stanford, California, USA
- 4Department of Engineering Science, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland
Abstract
We present the GPU implementation of the general-purpose interior-point solver Clarabel for convex optimization problems with conic constraints. We introduce a mixed parallel computing strategy that processes linear constraints first and then handles other conic constraints in parallel. The GPU solver currently supports linear equality and inequality constraints, second-order cones, exponential cones, power cones, and positive semidefinite cones of the same dimensionality. We demonstrate that integrating a mixed parallel computing strategy with GPU-based direct linear system solvers enhances the performance of GPU-based conic solvers, surpassing their CPU-based counterparts across a wide range of conic optimization problems. We also show that employing mixed-precision linear system solvers can potentially achieve additional acceleration without compromising solution accuracy.
Related
- Clarabel: An interior-point solver for conic programs with quadratic objectives
- dOPT: Differentiating Conic Optimization via Geometric Reduction
- Towards Heterogeneous Exascale CFD with a Single Fortran Code Base: OpenMP Target Acceleration of the High-Order Unstructured Solver UCNS3D
- Exact certification for conic optimization over quadratic modules: partitions of unity and recession directions
- Conic-Lifting for Learning-rate-free Convex Optimization
BibTeX
@article{chen2026cuclarabel,
title = {{CuClarabel: GPU Acceleration for a Conic Optimization Solver}},
author = {Yuwen Chen and Danny Tse and Parth Nobel and Paul J. Goulart and Stephen Boyd},
journal = {ACM Transactions on Mathematical Software},
year = {2026},
month = may,
doi = {10.1145/3815420},
url = {https://doi.org/10.1145/3815420},
note = {CC BY},
}
RIS
TY - JOUR
TI - CuClarabel: GPU Acceleration for a Conic Optimization Solver
AU - Yuwen Chen
AU - Danny Tse
AU - Parth Nobel
AU - Paul J. Goulart
AU - Stephen Boyd
PY - 2026
DA - 2026/05/21
JO - ACM Transactions on Mathematical Software
DO - 10.1145/3815420
UR - https://doi.org/10.1145/3815420
AB - We present the GPU implementation of the general-purpose interior-point solver Clarabel for convex optimization problems with conic constraints. We introduce a mixed parallel computing strategy that processes linear constraints first and then handles other conic constraints in parallel. The GPU solver currently supports linear equality and inequality constraints, second-order cones, exponential cones, power cones, and positive semidefinite cones of the same dimensionality. We demonstrate that integrating a mixed parallel computing strategy with GPU-based direct linear system solvers enhances the performance of GPU-based conic solvers, surpassing their CPU-based counterparts across a wide range of conic optimization problems. We also show that employing mixed-precision linear system solvers can potentially achieve additional acceleration without compromising solution accuracy.
SN - 0098-3500
SN - 1557-7295
ER -