I am an Associate Professor (nor: FÃ¸rsteamanuensis) at OsloMet â€“ Oslo Metropolitan University, and a research scientist in the Department Mathematics and Cybernetics at SINTEF (a non-profit research organization in Norway with roughly 2000 researchers). My research interests include applied mathematics, numerical simulation, accelerated scientific computing, image processing, and real-time scientific visualization. I've been co-organizer and organizer of the Geilo Winter School in eScience since 2011, and have taught at the University of Oslo and the Norwegian School of Information Technology for several years. I've also been a guest lecturer for a high performance computing course at the University of Granada the last couple of years, and I enjoy teaching.
Cell: (+47) 45 61 90 70
Homing pidgeon available upon request.
A. R. Brodtkorb
Simplified Ocean Models on GPUs, Submitted, 2016.
This article describes the implementation of three different simplified ocean models on a GPU (graphics processing unit) using Python and PyOpenCL. The three models are all based on the solving the shallow water equations on Cartesian grids, and our work is motivated by the aim of running very large ensembles of forecast models for fully nonlinear data assimilation. The models are the linearized shallow water equations, the non-linear shallow water equations, and the two-layer non-linear shallow water equations, respectively, and they contain progressively more physical properties of the ocean dynamics. We show how these models are discretized to run efficiently on a graphics processing unit, discuss how to implement them, and show some simulation results. The implementation is available online under an open source license, and may serve as a starting point for others to implement similar oceanographic models.
T. Gierlinger, A.R. Brodtkorb, A. Stumpf, M. Weilera, and F. Michel.
Visualization of marine sand dune displacements utilizing modern GPU techniques, In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015. Paper (DOI)
Quantifying and visualizing deformation and material fluxes is an indispensable tool for many geoscientific applications at different scales comprising for example global convective models (Burstedde et al., 2013), co-seismic slip (Leprince et al., 2007) or local slope deformation (Stumpf et al., 2014b). Within the European project IQmulus (http://www.iqmulus.eu) a special focus is laid on the efficient detection and visualization of submarine sand dune displacements. In this paper we present our approaches on the visualization of the calculated displacements utilizing modern GPU techniques to enable the user to interactively analyse intermediate and final results
within the whole workflow.
M. L. SÃ¦tra, A. R. Brodtkorb, K-A. Lie,
Efficient GPU-Implementation of Adaptive Mesh Refinement for the Shallow-Water Equations, Journal of Scientific Computing, 2014.
[Draft (PDF)] | Paper (DOI)
The shallow-water equations model hydrostatic flow below a free surface for cases in which the ratio between the vertical and horizontal length scales is small and are used to describe waves in lakes, rivers, oceans, and the atmosphere. The equations admit discontinuous solutions, and numerical solutions are typically computed using
high-resolution schemes. For many practical problems, there is a need to increase the grid resolution locally to capture complicated structures or steep gradients in the solution. An efficient method to this end is adaptive mesh refinement (AMR), which recursively refines the grid in parts of the domain and adaptively updates the refinement as the simulation progresses. Several authors have demonstrated that the explicit stencil computations of high-resolution schemes map particularly well to many-core architectures seen in hardware accelerators such as graphics processing units (GPUs). Herein, we present the first full GPU-implementation of a block-based AMR method for the second-order Kurganovâ€“Petrova central scheme. We discuss implementation details, potential pitfalls, and key insights, and present a series of
performance and accuracy tests. Although it is only presented for a particular case herein, we believe our approach to GPU-implementation of AMR is transferable to other hyperbolic conservation laws, numerical schemes, and architectures similar to the GPU.
2017-06-12 -- 2016-06-16, Finite volume methods and GPU computing: past, present, and future, Finite Volumes for Complex Applications 8, Lille, France.
2017-05-25 -- 2017-05-26 Lecturer for phd course in high performance computing, University of Valladolid, Spain. [lecture notes to appear]
2017-05-22 -- 2017-05-23 Lecturer for master course in high performance computing, ProgramaciÃ³n GrÃ¡fica de Altas Prestaciones, University of Granada, Spain. [lecture notes to appear]
2014-09-19 Ph.D. opponent for Mattia Natali, Sketch-based Modelling and Conceptual Visualization of Geomorphological Processes for Interactive Scientific Communication, University of Bergen, Norway.
2013-06-01 -- 2013-06-05 International Program Committee member for the Third International Workshop on New Algorithms and Programming Models for the Manycore Era, Helsinki, Finland.
2013-03-18 -- 2013-03-21 T. A. Haufmann, A. R. Brodtkorb, A. Berge,
P0168: Real-time voxel carving with automatic synchronization, Poster, GPU Technology Conference, 2013.