André Rigland Brodtkorb

Short bio

André R. Brodtkorb received his Ph.D. from the University of Oslo in 2010, after having completed his M.Sc. in 2007. He is currently a research scientist at SINTEF, a non-profit research organization in Norway with roughly 2000 researchers, where he works on GPU acceleration and algorithm design. He also holds a 20% position associate professor (førsteamanuensis) at the Norwegian School of Information Technology, where he teaches two courses: PG430 - Introduction to Graphics Programming, and PG612 - Advanced Graphics Programming.

His research interests include GPU/heterogeneous computing, simulation of partial differential equations (PDEs), and real-time visualization, and you can find videos of some of his work on Youtube.

Contact

Email: Andre.Brodtkorb@sintef.no
Office: (+47) 22 06 75 48
Cell: (+47) 45 61 90 70

Snailmail:
SINTEF, 9011 - Dept. Appl. Math.,
Pb. 124 Blindern,
NO-0314 Oslo,
Norway

Linkedin profile
SINTEF profile

Recent Publications

A. R. Brodtkorb, M. L. Sætra and T. R. Hagen, GPU Programming Strategies and Trends in GPU Computing, [in review, May 2011].

Abstract: Over the last decade, there has been a growing interest in the use of graphics processing units (GPUs) for non-graphics applications. From early academic proof-of-concept papers around the year 2000, the use of GPUs has now matured to a point where there are countless industrial applications. Together with the expanding use of GPUs, we have also seen a tremendous development in the programming languages and tools, and getting started programming GPUs has never been easier. However, whilst getting started with GPU programming can be simple, being able to fully utilize GPU hardware is an art that can take months and years to master. In this article, we give an overview of GPU programming strategies, with a focus on efficient hardware utilization. We give general advice in addition to step-by-step approaches to locating and removing bottlenecks through profile driven development. We conclude the article with our view on current and future trends.

M. L. Sætra and A. R. Brodtkorb, Shallow Water Simulations on Multiple GPUs, Proceedings of the Para 2010 Conference Part II, Lecture Notes in Computer Science 7134 (2012), pp 56--66, Springer.

Abstract: We present a state-of-the-art shallow water simulator running on multiple GPUs. Our implementation is based on an explicit high-resolution finite volume scheme for the shallow water equations, suitable for modeling dam breaks and flooding. We use row domain decomposition to enable multi-GPU computations, and perform traditional CUDA block decomposition within each GPU for further parallelism. Our implementation shows near perfect weak and strong scaling, and enables simulation of domains consisting of up-to 378 million cells at a rate of almost 400 megacells per second on the four GPUs of a Tesla S1070. Our experiments with the more recent Fermi architecture gives an estimate of over 1 gigacells per second performance.

A. R. Brodtkorb, Scientific Computing on Heterogeneous Architectures, Ph.D. thesis, University of Oslo, ISSN 1501-7710, No. 1031, 2010.
[Thesis (PDF)] [Slides (PDF)]

Abstract: The CPU has traditionally been the computational work horse in scientific computing, but we have seen a tremendous increase in the use of accelerators, such as Graphics Processing Units (GPUs), in the last decade. These architectures are used because they consume less power and offer higher performance than equivalent CPU solutions. They are typically also far less expensive, as more CPUs, and even clusters, are required to match their performance. Even though these accelerators are powerful in terms of floating point operations per second, they are considerably more primitive in terms of capabilities. For example, they cannot even open a file on disk without the use of the CPU. Thus, most applications can benefit from using accelerators to perform heavy computation, whilst running complex tasks on the CPU. This use of different compute resources is often referred to as heterogeneous computing, and we explore the use of heterogeneous architectures for scientific computing in this thesis. Through six papers, we present qualitative and quantitative comparisons of different heterogeneous architectures, the use of GPUs to accelerate linear algebra operations in MATLAB, and efficient shallow water simulation on GPUs. Our results show that the use of heterogeneous architectures can give large performance gains.

Full list

Recent Talks

2011-03-23 Efficient Shallow Water Simulations on GPUs, 2011 SIAM Conference on Mathematical & Computational Issues in the Geosciences, Long Beach, California, USA. [Slides (PDF)]

2010-12-17, Scientific Computing on Heterogeneous Architectures, PhD Thesis Defense, University of Oslo, Norway. [Slides (PDF)]

2010-12-17, Cloud Computing - How is it Different from Grid Computing, Trial Lecture, University of Oslo, Norway. [Slides (PDF)]

2010-09-21, Evacuate Now? Faster than Real-Time Shallow Water Simulation, NVIDIA GPU Technology Conference, San Jose, California, USA. [Slides (PDF)] Streaming Video Video

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Other

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.

2012-01-22 -- 2012-01-27 12th Geilo Winter School in eScience: Continuum Mechanics, co-organizer, Geilo, Norway.

2012-01-25 A. R. Brodtkorb, M. L. Sætra, GPUs for Shallow Water Simulations, Poster, 12th Geilo Winter School in eScience, January 2012. [Draft (PDF)]

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