
Open access
Date
2020-10Type
- Journal Article
Abstract
The alternating direction method of multipliers (ADMM) is a powerful operator splitting technique for solving structured convex optimization problems. Due to its relatively low per-iteration computational cost and ability to exploit sparsity in the problem data, it is particularly suitable for large-scale optimization. However, the method may still take prohibitively long to compute solutions to very large problem instances. Although ADMM is known to be parallelizable, this feature is rarely exploited in real implementations. In this paper we exploit the parallel computing architecture of a graphics processing unit (GPU) to accelerate ADMM. We build our solver on top of OSQP, a state-of-the-art implementation of ADMM for quadratic programming. Our open-source CUDA C implementation has been tested on many large-scale problems and was shown to be up to two orders of magnitude faster than the CPU implementation. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000420254Publication status
publishedExternal links
Journal / series
Journal of Parallel and Distributed ComputingVolume
Pages / Article No.
Publisher
ElsevierSubject
Graphics processing unit; GPU computing; Quadratic programming; Alternating direction method of multipliersOrganisational unit
03751 - Lygeros, John / Lygeros, John
Funding
787845 - Optimal control at large (EC)
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