68 citations · 78 across the 11 of their papers we have counts for
12 papers
ExaModels.jl: an Algebraic Modeling System for Nonlinear Programming on GPUs
Sungho Shin, Michel Schanen, François Pacaud +2
Large-scale nonlinear programs almost always exhibit partially separable and repetitive structure, yet most existing algebraic modeling systems do not take advantage of it. A nonli…
Harnessing GPU Acceleration in Large-Scale Process Optimization
Boxun Huang, David Y. Shu, Michel Schanen +3
This paper presents a proof-of-concept workflow for equation-oriented process optimization that runs entirely on a GPU. Process optimization models often incorporate complex interc…
Scalable Multi-Period AC Optimal Power Flow Utilizing GPUs with High Memory Capacities
Sungho Shin, Vishwas Rao, Michel Schanen +2
This paper demonstrates the scalability of open-source GPU-accelerated nonlinear programming (NLP) frameworks -- ExaModels.jl and MadNLP.jl -- for solving multi-period alternating…
Condensed Interior-Point Methods for Scalable Nonlinear Programming on GPUs
François Pacaud, Sungho Shin, Alexis Montoison +2
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…
Parallel Interior-Point Solver for Block-Structured Nonlinear Programs on SIMD/GPU Architectures
François Pacaud, Michel Schanen, Sungho Shin +2
We investigate how to port the standard interior-point method to new exascale architectures for block-structured nonlinear programs with state equations. Computationally, we decomp…
Condensed interior-point methods: porting reduced-space approaches on GPU hardware
François Pacaud, Sungho Shin, Michel Schanen +2
The interior-point method (IPM) has become the workhorse method for nonlinear programming. The performance of IPM is directly related to the linear solver employed to factorize the…
On automatic differentiation for the Matérn covariance
Oana Marin, Christopher Geoga, Michel Schanen
To target challenges in differentiable optimization we analyze and propose strategies for derivatives of the Matérn kernel with respect to the smoothness parameter. This problem is…
Batched Second-Order Adjoint Sensitivity for Reduced Space Methods
François Pacaud, Michel Schanen, Daniel Adrian Maldonado +4
This paper presents an efficient method for extracting the second-order sensitivities from a system of implicit nonlinear equations on upcoming graphical processing units (GPU) dom…
Fitting Matérn Smoothness Parameters Using Automatic Differentiation
Christopher J. Geoga, Oana Marin, Michel Schanen +1
The Matérn covariance function is ubiquitous in the application of Gaussian processes to spatial statistics and beyond. Perhaps the most important reason for this is that the smoot…
A Globally Convergent Distributed Jacobi Scheme for Block-Structured Nonconvex Constrained Optimization Problems
Anirudh Subramanyam, Youngdae Kim, Michel Schanen +2
Motivated by the increasing availability of high-performance parallel computing, we design a distributed parallel algorithm for linearly-coupled block-structured nonconvex constrai…
A Feasible Reduced Space Method for Real-Time Optimal Power Flow
François Pacaud, Daniel Adrian Maldonado, Sungho Shin +2
We propose a novel feasible-path algorithm to solve the optimal power flow (OPF) problem for real-time use cases. The method augments the seminal work of Dommel and Tinney with sec…
On the Strong Scaling of the Spectral Element Solver Nek5000 on Petascale Systems
Nicolas Offermans, Oana Marin, Michel Schanen +7
The present work is targeted at performing a strong scaling study of the high-order spectral element fluid dynamics solver Nek5000. Prior studies indicated a recommendable metric f…