Abstract
While graphics processing units (GPUs) show high performance for problems with regular structures, they do not perform well for irregular tasks due to the mismatches between irregular problem structures and SIMD-like GPU architectures. In this paper, we explore software approaches for improving the performance of irregular parallel computation on graphics processors. We propose general approaches that can eliminate the branch divergence and allow runtime load balancing. We evaluate the optimization rules and approaches with the n-queens problem benchmark. The experimental results show that the proposed approaches can substantially improve the performance of irregular computation on GPUs. These general approaches could be easily applied to many other irregular problems to improve their performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hussein, M., Abd-Almageed, W.: Efficnent Band Approximation of Gram Matrices for Large Scale Kernel Methods on GPUs. In: Conference on High Performance Computing Networking, Storage and Analysis, pp. 1–10. ACM Press, New York (2009)
Zhang, E.Z., Jiang, Y., Guo, Z., Shen, X.: Streamlining GPU applications on the fly: thread divergence elimination through runtime thread-data remapping. In: 24th ACM International Conference on Supercomputing (ICS), pp. 115–126. ACM Press, New York (2010)
Cederman, D., Tsigas, P.: On Dynamic Load Balancing on Graphics Processors. In: 23rd ACM SIGGRAPH/EUROGRAPHICS Symposium on Graphics Hardware, pp. 57–64. ACM Press, New York (2008)
Tzeng, S., Patney, A., Owens, J.D.: Task Management for Irregular-ParallelWorkloads on the GPU. In: High Performance Graphics 2010, pp. 29–37. ACM Press, New York (2010)
Aila, T., Laine, S.: Understanding the efficiency of ray traversal on GPUs. In: Proceedings of High Performance Graphics 2009, pp. 145–149. ACM Press, New York (2009)
Solomon, S., Thulasiraman, P.: Performance Study of Mapping Irregular Computations on GPUs. In: 2010 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW), pp. 1–8. IEEE Press, New York (2010)
Deng, Y., Wang, B.D., Mu, S.: Taming Irregular EDA Applications on GPUs. In: Proceedings of the 2009 International Conference on Computer-Aided Design, pp. 539–546. ACM Press, New York (2009)
Vuduc, R., Chandramowlishwaran, A., Choi, J.W., Guney, M.E., Shringarpure, A.: On the Limits of GPU Acceleration. In: Hot Topics in Parallelism (HotPar). USENIX Association, Berkeley (2010)
Lindholm, E., Nickolls, J., Oberman, S., Montrym, J.: NVIDIA Tesla: A Unified Graphics and Computing Architecture. J. IEEE Micro. 28, 39–55 (2008)
Fung, W.W.L., Sham, I., Yuan, G., Aamodt, T.M.: Dynamic Warp Formation and Scheduling for Efficient GPU Control Flow. In: 40th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO), pp. 407–420. IEEE Press, New York (2007)
Bell, J., Stevens, B.: A survey of known results and research areas for n-queens. J. Discrete Math. 309, 1–31 (2009)
Bozinovski, A., Bozinovski, S.: n-queenss pattern generation: an insight into space complexity of a backtracking algorithm. In: 2004 International Symposium on Information and Communication Technologies, pp. 281–286. Trinity College Dublin, Dublin (2004)
Khan, S., Bilal, M., Sharif, M., Sajid, M., Baig, R.: Solution of n-Queen Problem Using ACO. In: IEEE 13th International Multitopic Conference (INMIC), pp. 1–5. IEEE Press, New York (2009)
QUEESNTUD project, http://queens.inf.tu-dresden.de/
Shu, W., Wu, M.Y.: Asynchronous problems on SIMD parallel computers. J. IEEE Trans. on Parallel and Distributed Systems 6, 704–713 (1995)
Blas, A.D., Hughey, R.: Explicit SIMD Programming for Asynchronous Applications. In: IEEE International Conference on Application-Specific Systems, Architectures, and Processors, pp. 258–267. IEEE Press, New York (2000)
Cull, P., Pandey, R.: Isomorphism and the n-queenss problem. J. ACM SIGCSE Bulletin 26, 29–36 (1994)
NVIDIA CUDA C Programming Guide, http://developer.download.nvidia.com/compute/cuda/4_0_rc2/toolkit/docs/CUDA_C_Programming_Guide.pdf
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhang, T., Shu, W., Wu, MY. (2011). Optimization of N-Queens Solvers on Graphics Processors. In: Temam, O., Yew, PC., Zang, B. (eds) Advanced Parallel Processing Technologies. APPT 2011. Lecture Notes in Computer Science, vol 6965. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24151-2_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-24151-2_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24150-5
Online ISBN: 978-3-642-24151-2
eBook Packages: Computer ScienceComputer Science (R0)