Photo of me, summer 2014
André R. Brodtkorb is a research scientist in the Department of Applied Mathematics at SINTEF, a non-profit research organization in Norway with roughly 2000 researchers. His research interests include numerical simulation, accelerated scientific computing, image processing, and real-time scientific visualization. He has co-organized and organized the Geilo Winter School in eScience since 2011, and has been lecturing at the University of Oslo and the Norwegian School of Information Technology for several years. He has also been a guest lecturer for a course at the http://www.ugr.es/? for the last couple of years, and enjoys teaching. You can find some of his work online on his Github and Youtube accounts.
Cell: (+47) 45 61 90 70
SINTEF, 103 - Dept. Appl. Math.,
Pb. 124 Blindern,
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.
T. A. Haufmann, A. Berge, A. R. Brodtkorb,K. Kaspersen and A. Kim,
Real-time online camera synchronization for volume carving on GPU, IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2013.
[Draft (PDF)] | [Paper (DOI)]
Volume carving is a well-known technique for reconstructing a 3D scene from a set of 2D images, using features, usually foreground estimations, detected in individual cameras, and camera parameters to backproject the 2D images into 3D. Spatial calibration of the cameras is trivial, but the resulting carved volume is very sensitive to temporal offsets between the cameras. Automatic synchronization between the cameras is therefore desired. In this paper, we present a highly efficient implementation of volume carving and synchronization on a heterogeneous system fitted with commodity GPUs.
An online, real-time synchronization system is described and evaluated on surveillance video of an indoor scene. Improvements to the state of the art CPU-based algorithms are described.
A. Berge, A. R. Brodtkorb, T. A. Haufmann, K. Kaspersen and A. Kim,
Recommendations and guidelines for image processing on heterogeneous hardware, Technical report, 2013.
This report gives an introduction to using GPUs for computer vision. We start by giving an introduction to GPUs, followed by a state-of-the art survey of computer vision on GPUs. We then present our implementation of a real-time system for running low-level image processing algorithms on the GPU, based on live H.264 data originating from commodity-level IP cameras.
2015-04-16 --2015-04-17 Lecturer for master course in high performance computing, ProgramaciÃ³n GrÃ¡fica de Altas Prestaciones, University of Granada, Spain.Lecture 1 (PDF) Lecture 2 (PDF) Lecture 3 (PDF)
2014-04-08 Desktop supercomputing: Harnessing the power of accelerators, Seminar, University of Granada, Spain. Slides (PDF)
2014-09-19 Parallell computing towards exascale, Visual Computing Forum, University of Bergen, Norway. Slides (PDF)
2014-08-12 Data compression with Huffman and LZW. Slides (PDF)
2014-06-22 GPU and Heterogeneous Computing in Discrete Optimization, Tutorial, VeRoLog 2014, Norway. Slides (PDF)
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.
2013-01-29 A. R. Brodtkorb, Winter School on Reproducible Research, Blog post, [Article (PDF)]
2012-12-10 -- 2012-12-14 Participant ICERM Workshop on Reproducibility in Computational and Experimental Mathematics, Brown University, Providence, Rhode Island, USA.
[Workshop report (PDF)]
Graphics Cards Save the Day in Flood Crisis Management, News article, Computer Power User, July 2012, Nevada, USA.
2012-06-17 -- 2012-06-21
Advances in Heterogeneous Computing for Water Resources, special session organizer together with Wen-Mei Hwu, University of Illinois, 2012 International Conference on Computational Methods in Water Resources, University of Illinois at Urbana-Champaign, USA.
Simulering av Flom (English: Simulation of Floods), TV appearance, SchrÃ¶dingers Katt, Norwegian Broadcasting Corporation, Norway.
Flooding the system - improved flood simulation technology, News article, Materials World, March 2012, United Kingdom.