Iterative Methods for MPC on Graphical Processing Units

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The high oating point performance and memory bandwidth of Graphical Processing Units (GPUs) makes them ideal for a large number of computations which often arises in scientic computing, such as matrix operations. GPUs achieve this performance by utilizing massive par- allelism, which requires reevaluating existing algorithms with respect to this new architecture. This is of particular interest to large-scale constrained optimization problems with real-time requirements. The aim of this study is to investigate dierent methods for solving large-scale optimization problems with focus on their applicability for GPUs. We examine published techniques for iterative methods in interior points methods (IPMs) by applying them to simple test cases, such as a system of masses connected by springs. Iterative methods allows us deal with the ill-conditioning occurring in the later iterations of the IPM as well as to avoid the use of dense matrices, which may be too large for the limited memory capacity of current graphics cards.
Original languageEnglish
Title of host publicationProceedings of the 17th Nordic Process Control Workshop
EditorsJohn Bagterp Jørgensen, Jakob Kjøbsted Huusom, Gürkan Sin
Place of PublicationKogens Lyngby
PublisherTechnical University of Denmark (DTU)
Publication date2012
ISBN (Print)978-87-643-0946-1
Publication statusPublished - 2012
Event17th Nordic Process Control Workshop - Kongens Lyngby, Denmark
Duration: 25 Jan 201227 Jan 2012
Conference number: 17


Conference17th Nordic Process Control Workshop
CityKongens Lyngby
Internet address

    Research areas

  • Graphical Processing Unit, Model based control, Iterative methods, Predictive control, Optimization

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