Parallel cube computation on modern CPUs and GPUs

Journal of Supercomputing

Volumn 61, Issue 3, 2012, Pages 394-417

  Zhou, Guoliang ^a,b   Chen, Hong ^a  

^a RENMIN UNIVERSITY OF CHINA   (China)

^b Baoding Electric Power Vocation and Technology College   (China)

Author keywords

Cache conscious; Cube computation; GPUs; Multi core CPUs; OLAP; Real time data warehousing

Indexed keywords

BREADTH-FIRST; CACHE LOCALITY; CACHE MISS; CACHE-CONSCIOUS; COLUMN-STORE; DATA SCANNING; GENERAL-PURPOSE COMPUTING; GPUS; GRAPHICS PROCESSING UNIT; MULTI CORE; MULTICORE ARCHITECTURES; MULTIPLE THREADS; OLAP; ON-LINE ANALYTICAL PROCESSING; PARALLEL EXECUTIONS; REAL DATA SETS; SINGLE INSTRUCTION , MULTIPLE DATUM; THREAD-LEVEL PARALLELISM; TRANSLATION LOOKASIDE BUFFER;

ALGORITHMS; BUFFER STORAGE; COMPUTER GRAPHICS; DATA WAREHOUSES; GEOMETRY; PROGRAM PROCESSORS;

COMPUTER ARCHITECTURE;

EID: 84865331535     PISSN: 09208542     EISSN: 15730484     Source Type: Journal    
DOI: 10.1007/s11227-011-0575-7     Document Type: Article

References (43)

1. Abadi DJ, Madden SR, Ferreira M (2006) Integrating compression and execution in column-oriented database systems. In: Proceedings of ACMSIGMOD conference on management of data, pp 671-682 (Pubitemid 46946548)
2. Abadi DJ, Madden SR, Hachem N (2008) Column stores vs. row stores: how different are they really? In: Proceedings of ACM SIGMOD conference on management of data, pp 967-980
3. Ailamaki A, DeWitt DJ, Hill MD, Skounakis M (2001) Weaving relations for cache performance. In: Proceedings of the 27th international conference on very large data bases, pp 169-180
4. Beyer K, Ramakrishnan R (1999) Bottom-up computation of sparse and iceberg CUBEsA. In: Proceedings of ACM SIGMOD international conference on management of data, pp 359-370 (Pubitemid 129597349)
5. Blasgen M, Gray J, Mitoma M, Price T (1979) The convoy phenomenon. SIGOPS Oper Syst Rev 13(2):20-25
6. Bolosky WJ, Scott ML (1993) False sharing and its effect on shared memory performance. In: USENIX symposium on experiences with distributed and multiprocessor systems, vol 4. USENIX Association, pp 3-3
7. Boncz PA, Manegold S, Kersten ML (1999) Database architecture optimized for the new bottleneck: memory access. In: Proceedings of the 25th international conference on very large data bases, pp 54-65
8. Chen S, Gibbons PB, Mowry TC (2001) Improving index performance through prefetching. In: Proceedings of ACM SIGMOD conference on management of data, pp 235-246
9. Chen S, Ailamaki A, Gibbons PB, Mowry TC (2004) Improving hash join performance through prefetching. In: IEEE international conference on data engineering, pp 116-127
10. Chen Y, Dehne F, Eavis T (2005) PnP: parallel and external memory iceberg cube computation. In: Proceedings of IEEE international conference on data engineering, pp 576-577 (Pubitemid 41731182)
11. CUDPP (2010) CUDA Data Parallel Primitives Library. http://www.gpgpu.org/ developer/cudpp/
12. Fang W, Lu M, Xiao X, He B, Luo Q (2009) Frequent itemset mining on graphics processors. In: Proceedings of the fifth international workshop on data management on new hardware, pp 34-42
13. Fermi architecture (2011) http://www.nvidia.com/object/fermiarchitecture. html
14. Govindaraju NK, Lloyd B, Wang W, Lin M, Manocha D (2004) Fast computation of database operations using graphics processors. In: Proceedings of ACM SIGMOD international conference on management of data, pp 215-226
15. Govindaraju N, Gray J, Kumar R, Manocha D (2006) GPUTeraSort: high performance graphics coprocessor sorting for large database management. In: Proceedings of ACM SIGMOD conference on management of data, pp 325-336 (Pubitemid 46950863)
16. Hahn C, Warren S (2011) Extended edited synoptic cloud reports from ships and land stations over the globe. http://cdiac.ornl.gov/ftp/ndp026c
17. HarrisM(2007) Parallel prefix sum scan with CUDA. http://developer. download.nvidia.com/compute/cuda/1-1/Website/projects/scan/doc/scan.pdf
18. He B, Lu M, Yang K, Fang R, Govindaraju NK, Luo Q, Sander PV (2009) Relational query coprocessing on graphics processors. ACM Trans Database Syst 34(4):1-39
19. He B, Yang K, Fang R et al (2008) Relational joins on graphics processors. In: Proceedings of ACM SIGMOD conference on management of data, pp 511-524
20. IlliMine system package (2011) http://illimine.cs.uiuc.edu/
21. Intel Corporation (2009) Intel 64 and IA-32 architectures optimization reference manual. http://www.intel.com/Assets/ja-JP/PDF/manual/248966.pdf
22. Kim C, Sedlar E, Chhugani J, Kaldewey T (2009) Sort vs. hash revisited: fast join implementation on modern multi-core CPUs. In: Proceedings of 35th international conference on very large data bases, pp 1378-1389
23. Kim C, Chhugani J, Satish N, Sedlar E, Nguyen AD, Kaldewey T, Lee VW, Brandt SA, Dubey P (2010) FAST: fast architecture sensitive tree search on modern CPUs and GPUs. In: Proceedings of the international conference on management of data, pp 339-350
24. Lakshmanan LVS, Russakovsky A, Sashikanth V (2008) What-if OLAP queries with changing dimensions. In: Proceedings of IEEE 24th international conference on data engineering, pp 1334-1336
25. Langseth J (2004) Real-time data warehousing: challenges and solutions. http://dssresources.com/papers/features/langseth/langseth02082004.html
26. Lee R, Ding X, Chen F, Lu Q, Zhang X (2009) MCC-DB: minimizing cache conflicts in multi-core processors for databases. In: Proceedings of 35th international conference on very large data bases, pp 373-384
27. Lin J, Lu Q, Ding X, Zhang Z, Zhang X, Sadayappan P (2008) Gaining insights into multicore cache partitioning: bridging the gap between simulation and real systems. In: HPCA, pp 367-378
28. Luan H, Du X-Y, Wang S (2009) Cache-conscious data cube computation on a modern processor. J Comput Sci Technol (JCST) 24(4):708-722
29. Ma W, Agrawal G (2009) A translation system for enabling data mining applications on GPUs. In: Proceedings of the 23rd international conference on supercomputing, pp 400-409
30. Ng R, Wagner A, Yin Y (2001) Iceberg-cube computation with PC clusters. In: Proceedings of ACM SIGMOD conference on management of data, pp 25-36
31. Openmp (2011) http://www.openmp.org/
32. Qiao L, Raman V, Reiss F et al (2008) Main memory scan sharing for multi-core CPUs. In: Proceedings of international conference on very large data bases, pp 610-621
33. Rao J, Ross KA (1999) Cache conscious indexing for decision support in main memory. In: Proceedings of international conference on very large data bases, pp 78-89
34. Rao J, Ross KA (1999) Cache conscious indexing for decision support in main memory. In: Proceedings of international conference on very large data bases, pp 78-89
35. Satish N, Harris M, Garland M (2009) Designing efficient sorting algorithms for manycore GPUs. In: Proceedings of IEEE international symposium on parallel distributed processing, pp 1-10
36. Satish N, Kim C, Chhugani J, Nguyen AD, Lee VW, Kim D, Dubey P (2010) Fast sort on CPUs and GPUs: a case for bandwidth oblivious SIMD sort. In: Proceedings of the international conference on management of data, pp 351-362
37. Shao Z, Han JW, Xin D (2004) MM-Cubing: computing iceberg cubes by factorizing the lattice space. In: Proceedings of the 16th international conference on scientific and statistical database management, pp 213-222
38. Stonebraker M, Abadi DJ, Batkin A, Chen X et al (2005) C-Store: a column-oriented DBMS. In: Proceedings of the 31st VLDB conference, pp 553-564 (Pubitemid 43991033)
39. Sybase IQ (2011) http://www.sybase.com/products/datawarehousing/sybaseiq
40. The BI Survey 8 (2011) http://www.bi-survey.com/
41. Vertica (2011) http://www.vertica.com/
42. VTune (2011) http://software.intel.com/en-us/intel-vtune/
43. Xin D, Han J, Li X,Wah BW(2003) Star-Cubing: computing iceberg cubes by top-down and bottomup integration. In: Proceedings of ACM VLDB conference, pp 476-487