Abstract
Similarity search methods face serious performance issues since similarity functions are rather expensive to compute. Many optimization techniques were designed to reduce the number of similarity computations, when a query is being resolved. Indexing methods, like pivot table prefiltering, based on the metric properties of feature space, are one of the most popular methods. They can increase the speed of query evaluation even by orders of magnitude. Another approach is to employ highly parallel architectures like GPUs to accelerate evaluation by unleashing their raw computational power. Unfortunately, resolving the k nearest neighbors (kNN) queries optimized with metric indexing is a problem that is serial in nature. In this paper, we explore the perils of kNN parallelization and we propose a new algorithm that basically converts kNN queries into range queries, which are perfectly parallelizable. We have experimentally evaluated all approaches using a highly parallel environment comprised of multiple GPUs. The new algorithm demonstrates more than 2× speedup to the naïve parallel implementation of kNN queries.
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References
Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. Journal of the ACM (JACM) 45(6), 891–923 (1998)
Barrientos, R., Gómez, J., Tenllado, C., Prieto, M.: Heap based k-nearest neighbor search on gpus. In: Congreso Espanol de Informática (CEDI), pp. 559–566 (2010)
Beecks, C., Lokoč, J., Seidl, T., Skopal, T.: Indexing the Signature Quadratic Form Distance for Efficient Content-Based Multimedia Retrieval. In: Proc. ACM Int. Conf. on Multimedia Retrieval, pp. 24:1–24:8 (2011)
Beecks, C., Uysal, M.S., Seidl, T.: Signature Quadratic Form Distances for Content-Based Similarity. In: Proc. 17th ACM Int. Conference on Multimedia (2009)
Beecks, C., Uysal, M.S., Seidl, T.: Signature Quadratic Form Distance. In: Proc. ACM International Conference on Image and Video Retrieval, pp. 438–445 (2010)
Berchtold, S., Böhm, C., Braunmüller, B., Keim, D.A., Kriegel, H.P.: Fast parallel similarity search in multimedia databases, vol. 26. ACM (1997)
Bustos, B., Deussen, O., Hiller, S., Keim, D.: A graphics hardware accelerated algorithm for nearest neighbor search. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds.) ICCS 2006, Part IV. LNCS, vol. 3994, pp. 196–199. Springer, Heidelberg (2006)
Galgonek, J., Kruliš, M., Hoksza, D.: On the parallelization of the sprot measure and the tm-score algorithm. In: Caragiannis, I., et al. (eds.) Euro-Par Workshops 2012. LNCS, vol. 7640, pp. 238–247. Springer, Heidelberg (2013)
Garcia, V., Debreuve, E., Barlaud, M.: Fast k nearest neighbor search using gpu. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2008, pp. 1–6. IEEE (2008)
Krulis, M., Skopal, T., Lokoc, J., Beecks, C.: Combining cpu and gpu architectures for fast similarity search. Distributed and Parallel Databases (2012)
Krulis, M., Falt, Z., Bednárek, D., Yaghob, J.: Task Scheduling in Hybrid CPU-GPU Systems. In: ITAT, pp. 17–24 (2012)
Kruliš, M., Lokoč, J., Beecks, C., Skopal, T., Seidl, T.: Processing the signature quadratic form distance on many-core gpu architectures. In: CIKM, pp. 2373–2376 (2011)
Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Soviet Physics Doklady 10, 707 (1966)
Lokoč, J., Hetland, M., Skopal, T., Beecks, C.: Ptolemaic indexing of the signature quadratic form distance. In: Proceedings of the Fourth International Conference on SImilarity Search and APplications, pp. 9–16. ACM (2011)
Moreno-Seco, F., Micó, L., Oncina, J.: Extending LAESA fast nearest neighbour algorithm to find the k nearest neighbours. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 718–724. Springer, Heidelberg (2002)
Rubner, Y., Tomasi, C., Guibas, L.J.: The Earth Mover’s Distance as a Metric for Image Retrieval. International Journal of Computer Vision 40(2), 99–121 (2000)
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Kruliš, M., Kirchhoff, S., Yaghob, J. (2014). Perils of Combining Parallel Distance Computations with Metric and Ptolemaic Indexing in kNN Queries. In: Traina, A.J.M., Traina, C., Cordeiro, R.L.F. (eds) Similarity Search and Applications. SISAP 2014. Lecture Notes in Computer Science, vol 8821. Springer, Cham. https://doi.org/10.1007/978-3-319-11988-5_12
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DOI: https://doi.org/10.1007/978-3-319-11988-5_12
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