Condensed Interior-Point Methods for Scalable Nonlinear Programming on GPUs

This paper explores two variants of condensed-space interior-point methods designed for GPUs - HyKKT and LiftedKKT - by analyzing their numerical properties through error analysis and assessing their real-world performance via extensive numerical experiments with a fully GPU-resident software implementation.

Traditional implementations of interior-point methods (IPMs) involve solving indefinite augmented KKT systems repeatedly by utilizing direct sparse solvers based on the LBL factorization with sophisticated numerical pivoting strategies.

While this method achieves high performance and robustness on CPUs, the serial nature of numerical pivoting presents challenges for effective implementation on GPUs.

Recently, multiple condensed-space IPM strategies have emerged to address this issue by transforming the KKT system into a symmetric positive-definite matrix, which is more suitable for factorization on GPUs.

In this study, we demonstrate that although the condensed systems show increased ill-conditioning, the inherent structures of the condensed KKT system effectively counterbalance potential accuracy loss in the IPM.

Furthermore, we provide numerical results that thoroughly assess the capabilities of a fully GPU-resident nonlinear programming software stack, comprising MadNLP (a filter line-search IPM solver), cuDSS (a direct sparse solver leveraging Cholesky factorization), and ExaModels (a modeling framework), by benchmarking their performance against the pglib-opf and CUTEst libraries.

Our findings suggest that the GPU framework holds promise for solving highly sparse large-scale nonlinear programs, such as optimal power flow instances, except for diminished robustness and limited speedups for edge cases observed within CUTEst instances.