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Exploring graphics processing units as parallel coprocessors for online aggregation

Published: 30 October 2010 Publication History

Abstract

Multidimensional aggregation is one of the most important computational building blocks and hence also a potential performance bottleneck in Online Analytic Processing (OLAP). In order to deliver fast query responses for interactive operations such as slicing, dicing, roll-up and drill-down, it is essential that aggregates along the relevant dimensions of a data cube can be calculated as efficiently as possible. General-purpose computing on graphics processing units (GPGPU) is a recent trend used in many computing domains with the potential for tremendous speedups through the massively data-parallel computation available on such devices. We present a GPU-based cube data structure and algorithms for fast multidimensional aggregation, implemented using Nvidia's CUDA framework. Our experimental tests show a substantial speedup over state-of-the-art sequential algorithms. Moreover, the performance gain is particularly high in cases exposing the weaknesses of traditional algorithms, i.e. when the number of base cells involved in an aggregation is large.

References

[1]
Akal, F., Böhm, K., and Schek, H.-J. 2002. OLAP query evaluation in a database cluster: a performance study on intra-query parallelism. In Proceedings of ADBIS (Bratislava, Slovakia, September 8-11, 2002), 218--231.
[2]
ATI. 2010. Stream SDK website. http://developer.amd.com/gpu/ATIStreamSDK/ (2010.
[3]
Azvine, B., Cui, Z., and Nauck, D. D. 2005. Towards real-time business intelligence. BT Technology Journal 23, 3 (July 2005), Springer Netherlands, 214--225.
[4]
Böhm, C., Noll, R., Plant, C., Wackersreuther, B., and Zherdin, A. 2009. Data mining using graphics processing units. Transactions on Large-Scale Data- and Knowledge-Centered Systems I (2009), Springer LNCS vol. 5740, 63--90.
[5]
Chaudhuri, S. and Dayal, U. 1997. Data warehousing and OLAP for decision support. In Proceedings of SIGMOD (Tucson, AZ, May 13--15, 1997). ACM, New York, NY.
[6]
Dehne, F., Eavis, T., and Rau-Chaplin, A. 2006. The cgmCUBE project: optimizing parallel data cube generation for ROLAP. Distributed and Parallel Databases 19, 1 (2006), 29--62.
[7]
Dehne, F., Eavis, T., and Rau-Chaplin, A. 2003. Parallel multi-dimensional ROLAP indexing. In Proceedings of the 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid (Tokyo, Japan, May 12-15, 2003), IEEE, 86--93.
[8]
Golfarelli, M., Rizzi, S., and Proli. A. 2006. What-if analysis: towards a methodology. In Proceedings of DOLAP (Arlington, VA, November 2006), ACM, New York, NY.
[9]
Govindaraju, N. K., Lloyd, B., Wang, W., Lin, M., and Manocha, D. 2004. Fast computation of database operations using graphics processors. In Proceedings of SIGMOD (Paris, France, June 2004), ACM, New York, NY, 206--217.
[10]
Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., and Pirahesh, H. 1997. Data cube: a relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data Mining and Knowledge Discovery 1, 1 (1997), 29--53.
[11]
Harris, M., Sengupta, S., and Owens, J.D. 2007. Parallel prefix sum (scan) with CUDA. In: Nguyen, H. (ed.). GPU Gems 3, Addison Wesley (2007), 851--876.
[12]
He, B., Lu, M., Yang, K., Fang, R., Govindaraju, N. K., Luo, Q., and Sander, P.V. 2009. Relational query coprocessing on graphics processors. Transactions on Database Systems 34, 4 (2009), ACM, New York, N.
[13]
Horn, D. 2005. Stream reduction operations for GPGPU applications. In: Pharr, M. (ed.). GPU Gems 2, Addison Wesley (2005), 573--589.
[14]
Hu, K-F., Ling, C., Jie, S., Qi, G., and Tang, X.-L. 2005. Computing high dimensional MOLAP with parallel shell mini-cubes. In Proceedings of FSKD (Changsha, China, August 27--29, 2005) LNCS vol. 3613, 1192--1196.
[15]
IBM. 2010. Cognos TM1 product page. http://www.ibm.com/software/data/cognos/products/tm1.
[16]
Infor. 2010. PM 10 Corporate Performance Management. http://www.infor.com/solutions/pm/pm10.
[17]
Jedox AG. 2010. Palo Suite product page. http://www.jedox.com/en/products/Palo-Suite/palo-olap-server.htm.
[18]
Kotowski, N., Lima, A., Pacitti, E., Valduriez, P., and Mattoso, M. 2008. Parallel query processing for OLAP in grids. Concurrency and Computation: Practice and Experience, vol. 20 (2008), 2039--2048.
[19]
Khronos Group. 2010. OpenCL website. http://www.khronos.org/opencl.
[20]
Lauer, T., Datta, A., and Khadikov, Z. 2009. Palo+GPU: a CUDA-powered in-memory OLAP server. In: GPU Technology Conference (San Jose, CA, September 30 - October 2, 2009).
[21]
Nvidia Corporation. 2010. CUDA zone website. http://www.nvidia.com/object/cuda_home_new.html.
[22]
Pendse, N. and Creeth, R. 2010. The OLAP Report, Available online at: http://www.bi-verdict.com/.

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cover image ACM Conferences
DOLAP '10: Proceedings of the ACM 13th international workshop on Data warehousing and OLAP
October 2010
112 pages
ISBN:9781450303835
DOI:10.1145/1871940
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 30 October 2010

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Author Tags

  1. cuda
  2. gpgpu
  3. graphics processing unit
  4. multidimensional aggregation
  5. olap

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Overall Acceptance Rate 29 of 79 submissions, 37%

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Cited By

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  • (2023)Novel insights on atomic synchronization for sort-based group-by on GPUsDistributed and Parallel Databases10.1007/s10619-023-07424-241:3(387-409)Online publication date: 24-Apr-2023
  • (2022)A Collaborative Grouping Aggregation Query Scheme on Heterogeneous Computing Systems2022 7th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA)10.1109/ICCCBDA55098.2022.9778888(53-61)Online publication date: 22-Apr-2022
  • (2021)An Investigation of Atomic Synchronization for Sort-Based Group-By Aggregation on GPUs2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW)10.1109/ICDEW53142.2021.00016(48-53)Online publication date: Apr-2021
  • (2018)Building Textual OLAP Cubes Using Real-Time Intelligent Heterogeneous ApproachInternational Journal of Intelligent Information Technologies10.4018/IJIIT.201807010514:3(83-108)Online publication date: 1-Jul-2018
  • (2018)Efficient OLAP algorithms on GPU-accelerated Hadoop clustersDistributed and Parallel Databases10.1007/s10619-018-7239-z37:4(507-542)Online publication date: 31-Jul-2018
  • (2015)A GPU Query Accelerator for Geospatial Coordinates ComputationProceedings of the 2015 International Conference on Cloud Computing Research and Innovation (ICCCRI)10.1109/ICCCRI.2015.26(166-172)Online publication date: 26-Oct-2015
  • (2015)Big Data Conditional Business Rule Calculations in Multidimensional In-GPU-Memory OLAP DatabasesNew Trends in Databases and Information Systems10.1007/978-3-319-23201-0_32(291-304)Online publication date: 28-Aug-2015
  • (2015)GPU-Accelerated Quantification Filters for Analytical Queries in Multidimensional DatabasesNew Trends in Database and Information Systems II10.1007/978-3-319-10518-5_18(229-242)Online publication date: 2015
  • (2014)Soft Real-Time OLAPProceedings of the 2014 43rd International Conference on Parallel Processing Workshops10.1109/ICPPW.2014.24(85-94)Online publication date: 9-Sep-2014
  • (2014)Parallel online spatial and temporal aggregations on multi-core CPUs and many-core GPUsInformation Systems10.1016/j.is.2014.01.00544(134-154)Online publication date: Aug-2014
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