Compact data structure and scalable algorithms for the sparse grid technique

ACM SIGPLAN Notices

Volumn 46, Issue 8, 2011, Pages 25-34

  Muraraşu, Alin ^a   Weidendorfer, Josef ^a   Buse, Gerrit ^a   Butnaru, Daniel ^a   Pflüger, Dirk ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

GPU; Performance optimization; Sparse grids

Indexed keywords

BIJECTIVE MAPPING; COMPACT DATA STRUCTURE; COMPLEX DATA STRUCTURES; GPU; HASH TABLE; HIGHER-DIMENSIONAL; MULTI CORE; NATURAL NUMBER; PERFORMANCE OPTIMIZATIONS; RECURSIONS; SCALABLE ALGORITHMS; SPARSE GRID;

ALGORITHMS; OPTIMIZATION; PROGRAM PROCESSORS;

DATA STRUCTURES;

EID: 80053968380     PISSN: 15232867     EISSN: None     Source Type: Journal    
DOI: 10.1145/2038037.1941559     Document Type: Conference Paper

References (22)

1. C. Zenger. Sparse grids. In Wolfgang Hackbusch, editor, Parallel Algorithms for Partial Differential Equations, volume 31 of Notes on Numerical Fluid Mechanics, pages 241-251. Vieweg, 1991.
2. H.-J. Bungartz and M. Griebel. Sparse grids. Acta Numerica, 13(-1):147-269, 2004. (Pubitemid 38773594)
3. D. Pflüger. Spatially Adaptive Sparse Grids for High-Dimensional Problems. Dissertation, Institut für Informatik, Technische Universität München, February 2010.
4. H. Hacker, C. Trinitis, J. Weidendorfer, and M. Brehm. Considering GPGPU for HPC Centers: Is it Worth the Effort? In Proceedings of "Facing the Multicore-Challenge 2010", volume 6310 of LNCS. Springer, 2010.
5. V. Volkov and J. Demmel. Benchmarking GPUs to tune dense linear algebra. In SC, page 31, 2008.
6. E. Lindholm, J. Nickolls, S. F. Oberman, and J. Montrym. NVIDIA Tesla: A Unified Graphics and Computing Architecture. IEEE Micro, 28(2):39-55, 2008. (Pubitemid 351796170)
7. NVIDIA. CUDA Programming Guide 2.2.1, 2009.
8. S. Ryoo, C. I. Rodrigues, S. S. Baghsorkhi, S. S. Stone, D. B. Kirk, andW.W. Hwu. Optimization principles and application performance evaluation of a multithreaded GPU using CUDA. In PPOPP, pages 73-82, 2008.
9. H. Wong, M. M. Papadopoulou, M. Sadooghi-Alvandi, and A. Moshovos. Demystifying gpu microarchitecture through microbenchmarking. In ISPASS, pages 235-246, 2010.
10. Hewlett-Packard Company. Standard Template Library Programmer's Guide, 1994.
11. NVIDIA. CUDA SDK. http://developer.nvidia.com/object/cuda-2-2-downloads. html.
12. M. May and T. Schiekofer. An Abstract Data Type for Parallel Simulations based on Sparse Grids. In A. Bode, J. Dongarra, T. Ludwig, and V. Sunderam, editors, Proceedings of the Third European PVM Conference, volume 1156 of Lecture Notes in Computer Science, pages 59-67. Springer Verlag, 1997.
13. M. Griebel. Adaptive sparse grid multilevel methods for elliptic PDEs based on finite differences. Computing, 61(2):151-179, 1998. (Pubitemid 128508199)
14. C. Feuersänger. Dünngitterverfahren für hochdimensionale elliptische partielle Differentialgleichungen. Diplomarbeit, Institut für Numerische Simulation, Universität Bonn, 2005.
15. S. Dirnstorfer. Adaptive numerische Quadratur höherer Ordnung auf dünnen Gittern. Diplomarbeit, Fakultät für Informatik, Technische Universität München, 2000.
16. M. Griebel. The Combination Technique for the Sparse Grid Solution of PDE's on Multiprocessor Machines. Parallel Processing Letters, 2:61-70, 1992.
17. M. Griebel. A parallelizable and vectorizable multi-level algorithm on sparse grids. In Parallel algorithms for partial differential equations, Notes Numer. Fluid Mech. 31, pages 94-100. Vieweg, Wiesbaden, 1991.
18. C. Teitzel, M. Hopf, and T. Ertl. Scientific visualization on sparse grids. Technical report, Universitt Erlangen-Nrnberg, Lehrstuhl fr Graphische Datenverarbeitung (IMMD IX), 2000.
19. A. Gaikwad and I. M. Toke. GPU based sparse grid technique for solving multidimensional options pricing PDEs. In WHPCF '09: Proceedings of the 2nd Workshop on High Performance Computational Finance, pages 1-9, New York, NY, USA, 2009. ACM.
20. V. Gaede and O. Günther. Multidimensional Access Methods. ACM Comput. Surv., 30(2):170-231, 1998. (Pubitemid 128485203)
21. A. E. Lefohn, S. Sengupta, J. Kniss, R. Strzodka, and J. D. Owens. Glift: Generic, efficient, random-access GPU data structures. ACM Trans. Graph., 25(1):60-99, 2006.
22. NVIDIA. NVIDIA's Next Generation CUDA Compute Architecture: Fermi. White paper, 2009.