Projects

Highlights The group has pioneered several innovative techniques in parallel processing on CMPs and FPGAs. Mixing coarse-grained MPI cluster level parallelism and fine-grained co-processor parallelism, we contributed to a GPU accelerated FEM package that features a minimally invasive HW-SW integration and tested scalability up to 1 billion unknowns (Link). Our co-development of mixed precision methods for parallel co-processors overcame their initial single precision limitation and still offers faster results of equal accuracy compared to a direct double precision implementation (Link). We took part in the design and development of the Glift library for random access GPU data structures that enabled higher level programming and data parallel execution of complex data adaptive algorithms on the GPU (Link). At a time when GPUs had still a fixed function pipeline and operated in 8 bit precision, we demonstrated their early potential for scientific computing by implementing the first iterative solvers for PDEs (Link). Comparisons to an FPGA and a tile-based CMP followed (Link).
	Parallel Scientific Computing Scientific simulations have higher accuracy requirements than multimedia processing applications. With the introduction of optimized floating point processing units in graphics processors and reconfigurable hardware these devices are now also attractive as powerful scientific co-processors.
	Mixed Precision Methods To obtain a result of high accuracy it is not necessary to compute all intermediate results with high precision. Mixed precision methods apply high precision computations only where necessary and save space or time without decreasing the accuracy of the final solution.
	Reconfigurable Computing This projects investigates how the enormous parallelism of reconfigurable hardware can be harnessed to accelerate PDE solvers. Both fine- and coarse-grained architectures are examined. The performance is very convincing but for complex problems higher level programming languages for these devices are required.
	Computer Vision on GPUs Although graphics processor units (GPUs) are still very restricted in data handling some strategies allow the focusing of processing on data-dependent regions of interest. Thus computer vision algorithms which require computations on changing regions of interest can already benefit from the high GPU performance. Current implementations comprise the Generalized Hough Transform, skeleton computation and motion estimation.
	Image Processing on GPUs The data parallelism in typical image processing algorithms is very well suited for data-stream-based architectures. PDE based methods for image denoising, segmentation and registration have been thus accelerated on graphics cards.
	Visualization The choice of visualization methods and parameters is already a part of the interpretation process of the data, as it emphasizes certain structures and subdues others. This can lead to positive effects uncovering otherwise unconceivable relations in the data, but may also produce false evidence. Combinations of multiple methods, and data based parameter controls try to limit this danger.

Teaching

Applications

Publications

Extensive Articles: book chapters and journals

• Dominik Göddeke, Robert Strzodka, and Stefan Turek. Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations. International Journal of Parallel, Emergent and Distributed Systems (IJPEDS), Special issue: Applied parallel computing, 22(4):221–256, January 2007. (PDF)

• Aaron E. Lefohn, Joe Kniss, Robert Strzodka, Shubhabrata Sengupta, and John D. Owens. Glift: An abstraction for generic, efficient GPU data structures. ACM Transactions on Graphics, 25(1):1–37, Jan 2006. (PDF)

• Martin Rumpf and Robert Strzodka. Graphics processor units: New prospects for parallel computing. In Are Magnus Bruaset and Aslak Tveito, editors, Numerical Solution of Partial Differential Equations on Parallel Computers, volume 51 of Lecture Notes in Computational Science and Engineering, pages 89–134. Springer, 2005. (PDF)

Articles: book chapters and journals

• Dominik Göddeke, Hilmar Wobker, Robert Strzodka, Jamaludin Mohd-Yusof, Patrick McCormick, and Stefan Turek. Co-processor acceleration of an unmodified parallel solid mechanics code with FEASTGPU. International Journal of Computational Science and Engineering (IJCSE), 2009. to appear. (PDF)

• Nicolas Cuntz, Andreas Kolb, Robert Strzodka, and Daniel Weiskopf. Particle level set advection for the interactive visualization of unsteady 3D flow. Computer Graphics Forum, 27(3):719–726, May 2008. (PDF)

• Dominik Göddeke, Robert Strzodka, Jamaludin Mohd-Yusof, Patrick McCormick, Hilmar Wobker, Christian Becker, and Stefan Turek. Using GPUs to improve multigrid solver performance on a cluster. International Journal of Computational Science and Engineering (IJCSE), 4(1):36–55, 2008. (PDF)

• Dominik Göddeke, Robert Strzodka, Jamaludin Mohd-Yusof, Patrick McCormick, Sven H.M. Buijssen, Matthias Grajewski, and Stefan Turek. Exploring weak scalability for FEM calculations on a GPU-enhanced cluster. Parallel Computing, Special issue: High-performance computing using accelerators, 33(10–11):685–699, November 2007. (PDF)

• Robert Strzodka, Michael Doggett, and Andreas Kolb. Scientific computation for simulations on programmable graphics hardware. Simulation Modelling Practice and Theory, Special Issue: Programmable Graphics Hardware, 13(8):667–680, Nov 2005. (PDF)

• Robert Strzodka, Marc Droske, and Martin Rumpf. Image registration by a regularized gradient flow - a streaming implementation in DX9 graphics hardware. Computing, 73(4):373–389, November 2004. (PDF)

• Robert Strzodka, Marc Droske, and Martin Rumpf. Fast image registration in DX9 graphics hardware. Journal of Medical Informatics and Technologies, 6:43–49, Nov 2003. (PDF)

• Udo Diewald, Tobias Preusser, Martin Rumpf, and Robert Strzodka. Diffusion models and their accelerated solution in computer vision applications. Acta Mathematica Universitatis Comenianae (AMUC), 70(1):15–31, 2001. (PDF)

• J. Becker, D. Bürkle, R.-T. Happe, T. Preusser, M. Rumpf, M. Spielberg, and R. Strzodka. Aspects on data analysis and visualization for complex dynamical systems. In Bernold Fiedler, editor, Ergodic Theory, Analysis, and Efficient Simulation of Dynamical Systems, pages 417–430. Springer, 2000. (PDF)

Articles: conference proceedings

• Robert Strzodka and Dominik Göddeke. Pipelined mixed precision algorithms on FPGAs for fast and accurate PDE solvers from low precision components. In IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM 2006), pages 259–268, April 2006. (PDF)

• Alexandru Telea and Robert Strzodka. Multiscale image based flow visualization. In Proc. of SPIE-IS&T Electronic Imaging, Visualization and Data Analysis (VDA) 2006, volume 6060, pages 1–11, Jan 2006. (PDF)

• Dominik Göddeke, Robert Strzodka, and Stefan Turek. Accelerating double precision FEM simulations with GPUs. In Proceedings of ASIM 2005 - 18th Symposium on Simulation Technique, Sep 2005. (PDF)

• Robert Strzodka and Christoph Garbe. Real-time motion estimation and visualization on graphics cards. In Proceedings IEEE Visualization 2004, pages 545–552, 2004. (PDF)

• Robert Strzodka and Alexandru Telea. Generalized Distance Transforms and skeletons in graphics hardware. In Proceedings of EG/IEEE TCVG Symposium on Visualization (VisSym '04), pages 221–230, 2004. (PDF)

• Robert Strzodka, Ivo Ihrke, and Marcus Magnor. A graphics hardware implementation of the Generalized Hough Transform for fast object recognition, scale, and 3d pose detection. In Proceedings of IEEE International Conference on Image Analysis and Processing (ICIAP'03), pages 188–193, 2003. (PDF)

• Robert Strzodka. Virtual 16 bit precise operations on RGBA8 textures. In Proceedings of Vision, Modeling, and Visualization (VMV'02), pages 171–178, 2002. (PDF)

• Steffen Klupsch, Markus Ernst, Sorin A. Huss, Martin Rumpf, and Robert Strzodka. Real time image processing based on reconfigurable hardware acceleration. In Proceedings of IEEE Workshop Heterogeneous reconfigurable Systems on Chip, 2002. (PDF)

• Martin Rumpf and Robert Strzodka. Using graphics cards for quantized FEM computations. In Proceedings of IASTED Visualization, Imaging and Image Processing Conference (VIIP'01), pages 193–202, 2001. (PDF)

• Martin Rumpf and Robert Strzodka. Level set segmentation in graphics hardware. In Proceedings of IEEE International Conference on Image Processing (ICIP'01), volume 3, pages 1103–1106, 2001. (PDF)

• Martin Rumpf and Robert Strzodka. Nonlinear diffusion in graphics hardware. In Proceedings of EG/IEEE TCVG Symposium on Visualization (VisSym '01), pages 75–84, 2001. (PDF)

• Michael Dellnitz, Oliver Junge, Martin Rumpf, and Robert Strzodka. The computation of an unstable invariant set inside a cylinder containing a knotted flow. In B. Fiedler, K. Gröger, and J. Sprekels, editors, Proceedings of the Equadiff'99, pages 1015–1020. World Scientific, 2000. (PDF)

Extended Abstracts

• Robert Strzodka and Dominik Göddeke. Mixed precision methods for convergent iterative schemes. In Proceedings of the 2006 Workshop on Edge Computing Using New Commodity Architectures, pages D–59–60, May 2006. (PDF)

• Aaron Lefohn, Shubhabrata Sengupta, Joe Kniss, Robert Strzodka, and John D. Owens. Dynamic adaptive shadow maps on graphics hardware. In ACM SIGGRAPH 2005 Conference Abstracts and Applications, Aug 2005. (PDF)

• Joe Kniss, Aaron Lefohn, Robert Strzodka Shubhabrata Sengupta, and John D. Owens. Octree textures on graphics hardware. In ACM SIGGRAPH 2005 Conference Abstracts and Applications, Aug 2005. (PDF)

Miscellaneous

• Dominik Göddeke and Robert Strzodka. Performance and accuracy of hardware-oriented native-, emulated- and mixed-precision solvers in FEM simulations (part 2: Double precision GPUs). Technical report, Technical University Dortmund, 2008. (PDF)

• Robert Strzodka. Image processing on the XPP. http://www.mpi-inf.mpg.de/%7Estrzodka/papers/public/St02XPP.pdf, Aug 2002. Evaluation study. (PDF)

Thesis

• Robert Strzodka. Hardware Efficient PDE Solvers in Quantized Image Processing. PhD thesis, University of Duisburg-Essen, 2004. (PDF)

Tutorials

• Dominik Göddeke, Simon Green, and Robert Strzodka. GPGPU and CUDA tutorials. http://www.mathematik.uni-dortmund.de/%7Egoeddeke/arcs2008/, February 2008. Tutorials at the International Conference on Architecture of Computing Systems ARCS 2008, Dresden, Germany.

• B. Scott Michel, Ian Buck, Frederica Darema, Dominik Göddeke, Mary Hall, Allen McPherson, Dinesh Manocha, Matthew Papakipos, Michael Paolini, Ryan N. Schneider, Mark Segal, Burton Smith, Robert Strzodka, Marc Tremblay, and John Turner. General-purpose GPU computing: Practice and experience. http://www.gpgpu.org/sc2006/workshop/, November 2006. Workshop at IEEE/ACM Supercomputing 2006, Tampa, FL.

• Dominik Göddeke and Robert Strzodka. Scientific computing on graphics hardware. http://www.mathematik.uni-dortmund.de/%7Egoeddeke/iccs/, May 2006. Tutorial at the International Conference on Computational Science (ICCS) 2006, Reading, UK.

• Aaron Lefohn, Ian Buck, Patrick McCormick, John D. Owens, Tim Purcell, and Robert Strzodka. GPGPU: General-purpose computation on graphics processors. http://www.gpgpu.org/vis2005/, October 2005. Tutorial in IEEE Visualization 2005, Minneapolis, MN.

• Aaron Lefohn, Ian Buck, John D. Owens, and Robert Strzodka. GPGPU: General-purpose computation on graphics processors. http://www.gpgpu.org/vis2004/, October 2004. Tutorial in IEEE Visualization 2004, Austin, TX.