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).
2010-
	Iterative Stencil Computations Iterative stencil computations are ubiquitous in scientific computing and the exponential growth of cores in current processors leads to a bandwidth wall problem where limited off-chip bandwidth severely restricts their performance. In this project we aim at overcoming these problems by new algorithms that scale mainly with the aggregate cache bandwidth rather than the system bandwidth.
	Sparse Format Specialization The processing time of sparse representations of local discrete operators depends heavily on the storage format. The requirements of high accuracy, minimal memory footprint, high data locality, parallelism friendly layout, wide applicability, and easy modifiability are contradicting and therefore only case specific choices lead to satisfactory results.
	Parallel Adaptive Data Structures While GPUs and other highly parallel devices excel in processing of regularly structured data their large SIMD width and high number of cores quickly leads to inefficiencies in fine-granular branches and complex synchronization. However, adaptive data structures that cause such problems are indispensable to capture multi-scale phenomena. We must rethink our data arrangement in order to reconcile parallel and adaptive requirements.
	Balanced Multigrid Solvers Neither solvers with best numerical convergence nor solvers with best parallel efficiency are the best choice for the fast solution of PDE problems in practice. The fastest solvers require a delicate balance between their numerical and hardware characteristics. Often different tradeoffs must be chosen for fine and coarse grained parallelism.
	HPC Programming Patterns Although in-hardware processing has always been parallel, traditional programming languages create the illusion of sequential execution. This hurts performance and readability of the code, however, compatibility and maintainability reasons hinder the adaption of new languages. With a little thought, at least some of the desirable HPC programming patterns like switching between array of structs and struct of arrays layouts or passing of run-time values as template parameters can also be realized in traditional languages.
2005-2009
	GPU-Cluster Computing A single GPU already offers two levels of parallelism, but similar to CPUs, demand for higher performance and larger problem sizes leads to the utilization of GPU-clusters, in which every cluster node is equipped with GPUs. This adds the intra-node and inter-node parallelism. The main challenge for these heterogeneous systems is the enormous discrepancy in the bandwidth between the two finer and two coarser levels of parallelism.
	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.
	GPGPU Geometric Refinement GPUs process data of the same resolution very quickly with massive data parallel execution. But even the massive parallelism cannot compete with adaptive methods when the data size grows cubically under uniform refinement. This project develops parallel refinement strategies with grids and particles that allow to introduce higher resolution in only parts of the computational domain.
	GPGPU 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.
2000-2004
	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.
	GPGPU Computer Vision 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.
	GPGPU Image Processing 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

Lectures

• Robert Strzodka, Ross Walker, Eduardo Bringa, Ezequiel Ferrero, Carlos Bederián, and Nicolás Wolovick. GPGPU computing for scientific applications. http://www.famaf.unc.edu.ar/grupos/GPGPU/EscuelaGPGPU2011/, 2011. Lecture & course at the University of Córdoba, Argentina, SS 2011.
• Timothy Lanfear, Hendrik Lensch, and Robert Strzodka. Scientific GPU computing. http://gpulab.imm.dtu.dk/PhDschool/, 2010. Lecture & course at the Technical University of Denmark, Lyngby, Denmark, SS 2010.
• Hendrik Lensch and Robert Strzodka. Massively parallel computing with CUDA. http://www.mpi-inf.mpg.de/%7Estrzodka/lectures/ParCo08/, 2008. Lecture & course at the Saarland University, Saarbrücken, Germany, WS 2008/2009.

Tutorials/Workshops

• Emmanuel Agullo, Fran c cois Bodin, Denis Caromel, Jack Dongarra, Florent Duchaine, Luigi Genovese, Judith Gimenez, Dominik Göddeke, Michael Heroux, Manfred Liebmann, Raymond Namyst, Enrique Quintana Ortí, Robert Strzodka, Marc Tajchman, Tim Warburton, and Felix Wolf. Toward petaflop numerical simulation on parallel hybrid architectures. http://www-sop.inria.fr/manifestations/cea-edf-inria-2011/index_en.html, June 2011. CEA-EDF-INRIA summer school, Sophia-Antipolis, France.
• Robert Strzodka, Dominik Göddeke, and Dominik Behr. GPUs, OpenCL and scientific computing. http://www.gpgpu.org/ppam2009/, September 2009. Tutorial at the International Conference on Parallel Processing and Applied Mathematics PPAM 2009, Wroclaw, Poland.
• Dominik Göddeke, Robert Strzodka, and Christian Sigg. Practical GPU programming. http://www.speedup.ch/workshops/w38_2009/tutorial.html, September 2009. Tutorial at the SPEEDUP Workshop on High-Performance Computing 2009, Lausanne, Switzerland.
• 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.
• Maya Gokhale, Pat McCormick, Robert Strzodka, Zach Baker, Yang Liu, Paul Henning, Matthew Papakipos, Jeff Inman, Justin Tripp, Yuan Zhao, Matt Sottile, and Ron Minnich. Heterogeneous computing: Architectures, tools, applications. http://nis-www.lanl.gov/%7Emaya/papers/lacsi-06-heterogeneous-computing/abs.html, October 2006. Workshop at Los Alamos Computer Science Institute (LACSI) Symposium 2006, Santa Fe, NM.
• 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.

Publications

Extensive Articles: collections 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: collections and journals

• Robert Strzodka. Abstraction for AoS and SoA layout in C++. In Wen mei W. Hwu, editor, GPU Computing Gems: Jade Edition. Morgan Kaufmann, September 2011.
• Dominik Göddeke and Robert Strzodka. Cyclic reduction tridiagonal solvers on GPUs applied to mixed precision multigrid. IEEE Transactions on Parallel and Distributed Systems (TPDS), Special Issue: High Performance Computing with Accelerators, 22(1):22–32, January 2011. (PDF) (doi:10.1109/TPDS.2010.61)
• Dominik Göddeke and Robert Strzodka. Mixed precision GPU-multigrid solvers with strong smoothers. In Jack J. Dongarra, David A. Bader, and Jakub Kurzak, editors, Scientific Computing with Multicore and Accelerators, pages 131–147. CRC Press, December 2010. (PDF)
• 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), 4(4):254–269, November 2009. (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, Mohammed Shaheen, Dawid Pajak, and Hans-Peter Seidel. Cache accurate time skewing in iterative stencil computations. In Proceedings of the International Conference on Parallel Processing (ICPP), pages 571–581. IEEE Computer Society, September 2011. (PDF) (doi:10.1109/ICPP.2011.47)
• Robert Strzodka, Mohammed Shaheen, Dawid Pajak, and Hans-Peter Seidel. Cache oblivious parallelograms in iterative stencil computations. In ICS '10: Proceedings of the 24th ACM International Conference on Supercomputing, pages 49–59. ACM, June 2010. (PDF) (doi:10.1145/1810085.1810096)
• M. Shaheen, J. Gall, R. Strzodka, L. Van Gool, and H.-P. Seidel. A comparison of 3d model-based tracking approaches for human motion capture in uncontrolled environments. In IEEE Workshop on Applications of Computer Vision (WACV'09), pages 1–8, December 2009. (PDF)
• 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, Mohammed Shaheen, and Dawid Pajak. Time skewing made simple. In Proceedings ACM symposium on principles and practice of parallel programming, PPoPP '11, February 2011. (PDF)
• Robert Strzodka, Mohammed Shaheen, and Dawid Pajak. Overcoming bandwidth limitations in visual computing. In Proceedings of Visual Computing Research Conference, Saarbrücken, Germany, December 2009. (PDF)
• 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 E. Lefohn, Shubhabrata Sengupta, Joe Kniss, Robert Strzodka, and John D. Owens. Glift: Generic data structures for the GPU. In Proceedings of the 2006 Workshop on Edge Computing Using New Commodity Architectures, pages D–15–16, 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)

Thesis

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