Abstract
State of the art graphics processors provide high processing power and furthermore, the high programmability of GPUs offered by frameworks like CUDA increases their usability as high-performance co-processors for general-purpose computing. Sorting is well-investigated in Computer Science in general, but (because of this new field of application for GPUs) there is a demand for high-performance parallel sorting algorithms that fit to the characteristics of modern GPU-architecture.
We present a high-performance in-place implementation of Batcher’s bitonic sorting networks for CUDA-enabled GPUs. We adapted bitonic sort for arbitrary input length and assigned compare/exchange-operations to threads in a way that decreases low-performance global-memory access and thereby greatly increases the performance of the implementation.
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References
Kapasi, U.J., Dally, W.J., Rixner, S., Mattson, P.R., Owens, J.D., Khailany, B.: Efficient conditional operations for data-parallel architectures. In: MICRO 33: Proceedings of the 33rd Annual ACM/IEEE International Symposium on Microarchitecture, pp. 159–170. ACM, New York (2000)
Purcell, T.J., Donner, C., Cammarano, M., Jensen, H.W., Hanrahan, P.: Photon mapping on programmable graphics hardware. In: HWWS 2003: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS Conference on Graphics Hardware, Aire-la-Ville, Switzerland, pp. 41–50. Eurographics Association (2003)
Kipfer, P., Segal, M., Westermann, R.: Uberflow: a gpu-based particle engine. In: HWWS 2004: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS Conference on Graphics Hardware, pp. 115–122. ACM, New York (2004)
Govindaraju, N., Raghuvanshi, N., Henson, M., Manocha, D.: A cache-efficient sorting algorithm for database and data mining computations using graphics processors. Technical report, University of North Carolina-Chapel Hill (2005)
Greb, A., Zachmann, G.: Gpu-abisort: optimal parallel sorting on stream architectures. In: 20th International on Parallel and Distributed Processing Symposium, IPDPS 2006 (2006)
Batcher, K.: Sorting networks and their applications. In: AFIPS Spring Joint Comput. Conf. (1967)
Harris, M., Sengupta, S., Owens, J.D.: Parallel prefix sum (scan) with cuda. In: GPU Gems 3. Addison-Wesley, Reading (2007)
Grand, S.L.: Broad-phase collision detection with cuda. In: GPU Gems, vol. 3, Addison-Wesley, Reading (2007)
He, B., Govindaraju, N.K., Luo, Q., Smith, B.: Efficient gather and scatter operations on graphics processors. In: SC 2007: Proceedings of the 2007 ACM/IEEE Conference on Supercomputing, pp. 1–12. ACM, New York (2007)
Sengupta, S., Harris, M., Zhang, Y., Owens, J.D.: Scan primitives for gpu computing. In: GH 2007: Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS Symposium on Graphics Hardware, Aire-la-Ville, Switzerland, pp. 97–106. Eurographics Association (2007)
Cederman, D., Tsigas, P.: A practical quicksort algorithm for graphics processors. In: Halperin, D., Mehlhorn, K. (eds.) Esa 2008. LNCS, vol. 5193, pp. 246–258. Springer, Heidelberg (2008)
Sintorn, E., Assarsson, U.: Fast parallel gpu-sorting using a hybrid algorithm, Orlando, FL, USA, vol. 68, pp. 1381–1388. Academic Press, Inc., London (2008)
Satish, N., Harris, M., Garland, M.: Designing efficient sorting algorithms for manycore gpus. In: Proceedings 23rd IEEE International Parallel and Distributed Processing Symposium (2009)
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Peters, H., Schulz-Hildebrandt, O., Luttenberger, N. (2010). Fast In-Place Sorting with CUDA Based on Bitonic Sort. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Wasniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2009. Lecture Notes in Computer Science, vol 6067. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14390-8_42
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DOI: https://doi.org/10.1007/978-3-642-14390-8_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14389-2
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