1978. CuClarabel: GPU Acceleration for a Conic Optimization Solver
Invited abstract in session TB-43: Continuous solvers, stream Software for Optimization.
Tuesday, 10:30-12:00Room: Newlyn GR.07
Authors (first author is the speaker)
| 1. | Yuwen Chen
|
| EPFL STI IGM LA3, EPFL |
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, then handles other conic constraints in parallel. This mixed parallel computing strategy currently supports linear, second-order cone, exponential cone, and power cone constraints. 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.
Keywords
- Convex Optimization
- Large Scale Optimization
- Interior Point Methods
Status: accepted
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