Query processing on cluster based sysems with multicore acelerators
Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, no. 2 (2012), pp. 59-67 Cet article a éte moissonné depuis la source Math-Net.Ru

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The paper is devoted to the problem of modeling query execution process in multiprocessors of parallel database systems. Original approaches to the query execution process on GPU, MIC and multicore CPU are presented. Based on this approach, a simulator of parallel DBMS is developed. Results of computational experiments are presented, and analysis of efficiency of the proposed approaches is performed.
Keywords: parallel query processing, database multiprocessor model.
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P. S. Kostenetskiy. Query processing on cluster based sysems with multicore acelerators. Vestnik Ûžno-Uralʹskogo gosudarstvennogo universiteta. Seriâ Vyčislitelʹnaâ matematika i informatika, no. 2 (2012), pp. 59-67. http://geodesic.mathdoc.fr/item/VYURV_2012_2_a5/

[1] R. Agrawal, A. Ailamaki, P.A. Bernstein et al., “The Claremont Report on Database Research”, Communications of the ACM, 52:6 (2009), 56–65

[2] P. Bakkum, K. Skadron, “Accelerating SQL Database Operations on a GPU with CUDA”, The 3rd Workshop on General-Purpose Computation on Graphics Processing Units (Pittsburgh, USA, March 14, 2010), ACM, 2010, 94–103

[3] A.D. Blas, T. Kaldewey, “Data Monster”, IEEE Spectrum, 46:9 (2009), 46–51 | DOI

[4] S. Ding, J. He, H. Yan, T. Suel, “Using Graphics Processors for High Performance IR Query Processing”, The 18th International Conference on World Wide Web (New York, USA, April 20–24, 2009), ACM, 2009, 421–430

[5] N. Govindaraju, B. Lloyd, W .Wang, et al., “Fast Computation of Database Operations Using Graphics Processors”, ACM SIGGRAPH 2005 Courses, ACM, New York, USA, 2005, 206 pp.

[6] M. Heimel, V. Markl, “A First Step Towards GPU-assisted Query Optimization”, The Third International Workshop on Accelerating Data Management Systems using Modern Processor and Storage Architectures (Istanbul, Turkey, August 27, 2012), 2012, 1–12

[7] P. Bakkum, K. Skadron, “Accelerating SQL Database Operations on a GPU with CUDA”, 3rd Workshop on General Purpose Computation on Graphics Processing Units (New York, USA, March 14, 2010), ACM, 2010, 94–103

[8] N. Bandi, C. Sun, D. Agrawal, A.E. Abbadi, “Hardware Acceleration in Commercial Databases: a Case Study of Spatial Operations”, Proceedings of the 30th International Conference on Very Large Data Bases (August 31 – September 3, 2004), v. 30, VLDB Endowment, 2004, 1021–1032

[9] B. He, M. Lu, K. Yang, R. Fang, et.al., “Relational Query Coprocessing on Graphics Processors”, ACM Transactions on Database Systems, 34:4 (2009), 21:1–21:39 | DOI

[10] B. He, Y.J. Xu, “Highthroughput Transaction Executionson Graphics Processors” (Seattle, Washington, USA, August 29 – September 3, 2011), Proceedings of the VLDB Endowment, 4:5 (2011), 314–325 | DOI

[11] C. Kim, J. Chhugani, N. Satish, “FAST: Fast Architecture Sensitive Tree Search on Modern CPUs and GPUs”, ACM SIGMOD International Conference on Management of Data, Indianapolis (USA, June 6–10, 2010), ACM, 2010, 339–350

[12] N. Satish, C. Kim, J. Chhugani, et. al., “Fast Sort on CPUs and GPUs: a Case for Bandwidth Oblivious SIMD Sort”, The 2010 ACM SIGMOD International Conference on Management of Data (New York, USA, 2010), ACM, 2010, 351–362

[13] U.R. Vitor, A GPU Operations Framework for WattDB. Technical Report, University of Kaiserslautern, Germany, Kaiserslautern, 2012, 44 pp.

[14] C.E. Hansen, M. Christiansen, CUDA DBMS. Technical Report, Aalborg University, Denmark, Copenhagen, 2009, 79 pp.

[15] P.S. Kostenetskii, A.V. Lepikhov, L.B. Sokolinskii, “Technologies of Parallel Database Systems for Hierarchical Multiprocessor Environments”, Automation and Remote Control, 68:5 (2007), 847–859 | DOI

[16] A.V. Lepikhov, L.B. Sokolinsky, “Query Processing in a DBMS for Cluster Systems”, Programming and Computer Software, 36:4 (2010), 205–215

[17] C.S. Pan, M.L. Zymbler, “Development of a Parallel Database Management System on the Basis of Open-Source PostgreSQL DBMS”, Bulletin of the South Ural State University. Series: Mathematical Modeling, Programming Computer Software, 12:18(277) (2012), 112–120

[18] L.B. Sokolinsky, “Organization of Parallel Query Processing in Multiprocessor Database Machines with Hierarchical Architecture”, Programming and Computer Software, 27:6 (2001), 297–308

[19] L.B. Sokolinsky, “Survey of Architectures of Parallel Database Systems”, Programming and Computer Software, 30:6 (2004), 337–346