GPU-based computing of repeated range queries over moving objects

Proceedings - 2014 22nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2014

Volumn , Issue , 2014, Pages 640-647

  Silvestri, Claudio ^a   Lettich, Francesco ^a   Orlando, Salvatore ^a   Jensen, Christian S ^b  

^a CA' FOSCARI UNIVERSITY OF VENICE   (Italy)

^b AARHUS UNIVERSITY   (Denmark)

Author keywords

bitmap; gpgpu; gpu workload distribution; lock free data structures; moving objects; repeated range queries; uniform grids

Indexed keywords

PROGRAM PROCESSORS;

BITMAP; GPGPU; LOCK-FREE DATA STRUCTURES; MOVING OBJECTS; RANGE QUERY; UNIFORM GRIDS; WORK-LOAD DISTRIBUTION;

PROBLEM SOLVING;

EID: 84899417710     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/PDP.2014.27     Document Type: Conference Paper

References (19)

1. Sowell B., Vaz Salles M., Cao T., Demers A., and Gehrke J. Indexing framework. http://www.cs.cornell.edu/~sowell/indexing/.
2. Sowell B., Salles M.V., Cao T., Demers A., and Gehrke J. An experimental analysis of iterated spatial joins in main memory. Submitted.
3. Yu B., Kim H., Choi W., and Kwon D. Parallel range query processing on R-tree with graphics processing unit. In IEEE Conf. on Dependable, Autonomic and Secure Computing, pages 1235-1242, 2011.
4. Sun C., Agrawal D., and El Abbadi A. Hardware acceleration for spatial selections and joins. In Proc. of ACM SIGMOD Conf., pages 455-466, 2003.
5. Merrill D. and Grimshaw A.S. High performance and scalable radix sorting: a case study of implementing dynamic parallelism for GPU computing. Parallel Processing Letters, 21(2): 245- 272, 2011.
6. Šidlauskas D., Ross K.A., Jensen C.S., and Šaltenis S. Thread-level parallel indexing of update intensive moving-object workloads. In Proc. of SSTD Conf., pages 186-204, 2011.
7. Šidlauskas D., Šaltenis S., and Jensen C.S. Parallel main-memory indexing for moving-object query and update workloads. In Proc. of ACM SIGMOD Conf., pages 37-48, 2012.
8. Dittrich J., Blunschi L., and Vaz Salles M.A. MOVIES: indexing moving objects by shooting index images. Geoinformatica, 15(4):727-767, 2011.
9. Gray J. and Reuter A. Transaction processing: concepts and techniques. Morgan Kaufmann Publishers, 1993.
10. Zhang J. and You S. Speeding up large-scale point-in-polygon test based spatial join on GPUs. In Proc. of ACM SIGSPATIAL Intl. Wksp. on Analytics for Big Geospatial Data, pages 23-32, 2012.
11. Bentley J.L. and Friedman J.H. Data structures for range searching. ACM Comput. Surv., 11(4):397-409, 1979.
12. Hennessy J.L. and Patterson D.A. Computer Architecture-A Quantitative Approach (5. ed.). Morgan Kaufmann, 2012.
13. Luo L., Wong M.D.F., and Leong L. Parallel implementation of r-trees on the gpu. In IEEE Conf. on Design Automation, pages 353-358, 2012.
14. Kornacker M., Mohan C., and Hellerstein J.M. Concurrency and recovery in generalized search trees. In Proc. of ACM SIGMOD Conf., pages 62-72, 1997.
15. NVIDIA. CUDA C Programming Guide 5.0. October 2012.
16. Hong S. and Kim H. An analytical model for a GPU architecture with memory-level and thread-level parallelism awareness. ACM SIGARCH Computer Architecture News, 37(3):152-163, 2009.
17. Sengupta S., Harris M., Zhang Y., and Owens J.D. Scan primitives for GPU computing. In Proc. of ACM SIGGRAPH Symposium on Graphics Hardware, pages 97-106, 2007.
18. Brinkhoff T. A framework for generating network-based moving objects. GeoInformatica, 6(2):153-180, 2002.
19. Lee V.W., Kim C., Chhugani J., Deisher M., Kim D., Nguyen A.D., Satish N., Smelyanskiy M., Chennupaty S., and Hammarlund P. Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU. In ACM SIGARCH Computer Architecture News, volume 38, pages 451-460, 2010.