Abstract
In this paper we propose new algorithmic techniques for massively data parallel computation of the Likelihood Ratio Test (LRT) on a large spatial data grid. LRT is the state-of-the-art method for identifying hotspots or anomalous regions in spatially referenced data. LRT is highly adaptable permitting the use of a large class of statistical distributions to model the data. However, standard sequential implementations of LRT may take several days on modern machines to identify anomalous regions even for moderately sized spatial grids.
This work claims three novel contributions. First, we devise a dynamic program with a pre-processing step of \(\mathcal O(n^2)\) that allows us to compute the statistic for any given region in \(\mathcal O(1)\), where n is the length of the grid. Second, we propose a scheme to accelerate the likelihood computation of a complement region using a bounding technique. Third, we provide a parallelization strategy for the LRT computation on GPGPUs. In concert all three contributions result in a speed up of nearly four hundred times reducing the LRT computation time of large spatial grids from several days to minutes.
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References
SatScan, http://www.SatScan.org
Agarwal, D., Phillips, J.M., Venkatasubramanian, S.: The hunting of the bump: On maximizing statistical discrepancy. In: SODA, pp. 1137–1146 (2006)
Beutel, A., Mølhave, T., Agarwal, P.K.: Natural neighbor interpolation based grid dem construction using a gpu. In: GIS 2010, pp. 172–181. ACM, New York (2010)
Gregerson, A.: Implementing fast mri gridding on gpus via. cuda. Nvidia Tech. Report on Medical Imaging using CUDA (2008)
Hong, S., Kim, S.K., Oguntebiv, T., Olukotun, K.: Efficient parallel graph exploration on multi-core cpu and gpu. In: Proceedings of the 16th ACM Symposium on Principles and Practice of Parallel Programming, PPoPP 2011 (2011)
Larew, S.G., Maciejewski, R., Woo, I., Ebert, D.S.: Spatial scan statistics on the gpgpu. In: Proceedings of the Visual Analytics in Healthcare Workshop at the IEEE Visualization Conference (2010)
Wu, M., Song, X., Jermaine, C., Ranka, S., Gums, J.: A LRT Framework for Fast Spatial Anomaly Detection. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2009), pp. 887–896 (2009)
Pang, L.X., Chawla, S., Liu, W., Zheng, Y.: On mining anomalous patterns in road traffic streams. In: Tang, J., King, I., Chen, L., Wang, J. (eds.) ADMA 2011, Part II. LNCS, vol. 7121, pp. 237–251. Springer, Heidelberg (2011)
Wilks, S.S.: The large sample distribution of the likelihood ratio for testing composite hypotheses. Annals of Mathematical Statistics (9), 60–62 (1938)
Vuduc, R., Chandramowlishwaranv, A., Choi, J., Guney, M., Shringarpure, A.: On the limits of gpu acceleration. In: HotPar 2010 Proceedings of the 2nd USENIX Conference on Hot Topics in Parallelism, pp. 237–251 (2010)
Wu, R., Zhang, B., Hsu, M.: Gpu-accelerated large scale analytics. In: IACM UCHPC 2009: Second Workshop on UnConventional High Performance Computing (2009)
Wu, R., Zhang, B., Hsu, M.C.: Clustering billions of data points using gpus. In: IACM UCHPC 2009: Second Workshop on UnConventional High Performance Computing (2009)
Zhao, S.S., Zhou, C.: Accelerating spatial clustering detection of epidemic disease with graphics processing unit. In: Proceedings of Geoinformatics, pp. 1–6 (2010)
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Pang, L.X., Chawla, S., Scholz, B., Wilcox, G. (2013). A Scalable Approach for LRT Computation in GPGPU Environments. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds) Web Technologies and Applications. APWeb 2013. Lecture Notes in Computer Science, vol 7808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37401-2_58
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DOI: https://doi.org/10.1007/978-3-642-37401-2_58
Publisher Name: Springer, Berlin, Heidelberg
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