Abstract
Data mining algorithms are designed to extract information from a huge amount of data in an automatic way. The datasets that can be analysed with these techniques are gathered from a variety of domains, from business related fields to HPC and supercomputers. The datasets continue to increase at an exponential rate, so research has been focusing on parallelizing different data mining techniques. Recently, GPU hybrid architectures are starting to be used for this task. However the data transfer rate between CPU and GPU is a bottleneck for the applications dealing with large data entries exhibiting numerous dependencies. In this paper we analyse how efficient data mining algorithms can be mapped on these architectures by extracting the common characteristics of these methods and by looking at the communication patterns between the main memory and the GPU’s shared memory. We propose an experimental study for the performance of memory systems on GPU architectures when dealing with data mining algorithms and we also advance performance model guidelines based on the observations.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: International Conference on Very Large Data Bases, pp. 487–499 (1994)
Han, J., et al.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery 8(1) (2004)
Fang, W., et al.: Wenbin Fang and all: Frequent Itemset Mining on Graphics Processors (2009)
Liu, L., et al.: Optimization of Frequent Itemset Mining on Multiple-Core Processor. In: International Conference on Very Large Data Bases, pp. 1275–1285 (2007)
Shalom, A., et al.: Efficient k-means clustering using accelerated graphics processors. In: International Conference on Data Warehousing and Knowledge Discovery, pp. 166–175 (2008)
Cao, F., Tung, A.K.H., Zhou, A.: Scalable clustering using graphics processors. In: Yu, J.X., Kitsuregawa, M., Leong, H.-V. (eds.) WAIM 2006. LNCS, vol. 4016, pp. 372–384. Springer, Heidelberg (2006)
Liao, Q., et al.: Accelerated Support Vector Machines for Mining High-Throughput Screening Data. J. Chem. Inf. Model. 49(12), 2718–2725 (2009)
Wu, X., et al.: Top 10 algorithms in data mining. Knowledge and Information Systems 14(1) (2007)
Lastra, A., Lin, M., Manocha, D.: Gpgp: General purpose computation using graphics processors. In: ACM Workshop on General Purpose Computing on Graphics Processors (2004)
Li, J., et al.: Parallel Data Mining Algorithms for Association Rules and Clustering. In: International Conference on Management of Data (2008)
Carpenter, A.: CuSVM A cuda implementation of support vector classification and regression (2009), http://patternsonascreen.net/cuSVM.html
Pramudiono, I., et al.: Tree structure based parallel frequent pattern mining on PC cluster. In: International Conference on Database and Expert Systems Applications, pp. 537–547 (2003)
Pramudiono, I., Kitsuregawa, M.: Tree structure based parallel frequent pattern mining on PC cluster. In: Mařík, V., Štěpánková, O., Retschitzegger, W. (eds.) DEXA 2003. LNCS, vol. 2736, pp. 537–547. Springer, Heidelberg (2003)
Garcia, V., et al.: Fast k nearest neighbor search using GPU. In: Computer Vision and Pattern Recognition Workshops (2008)
Oh, K.-S., et al.: GPU implementation of neural networks. Journal of Pattern Recognition 37(6) (2004)
Domeniconi, C., et al.: An Efficient Density-based Approach for Data Mining Tasks. Journal of Knowledge and Information Systems 6(6) (2004)
Domeniconi, C., et al.: OpenMP to GPGPU: a compiler framework for automatic translation and optimization. In: Symposium on Principles and Practice of Parallel Programming, pp. 101–110 (2009)
Wang, Q.: Divergence estimation of continuous distributions based on data-dependent partitions. IEEE Transactions on Information Theory, 3064–3074 (2005)
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
Gainaru, A., Slusanschi, E., Trausan-Matu, S. (2011). Mapping Data Mining Algorithms on a GPU Architecture: A Study. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2011. Lecture Notes in Computer Science(), vol 6804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21916-0_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-21916-0_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21915-3
Online ISBN: 978-3-642-21916-0
eBook Packages: Computer ScienceComputer Science (R0)