kNN-Borůvka-GPU: A fast and scalable MST construction from kNN graphs on GPU

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7333 LNCS, Issue PART 1, 2012, Pages 71-86

  Arefin, Ahmed Shamsul ^a   Riveros, Carlos ^a   Berretta, Regina ^a   Moscato, Pablo ^a  

^a UNIVERSITY OF NEWCASTLE   (Australia)

Author keywords

CUDA; GPU; Minimum spanning tree; nearest neighbors; parallel computing

Indexed keywords

CABLE TV; COMPUTATION ALGORITHM; CUDA; DATA SETS; GPU; GRAPH STRUCTURES; K-NEAREST NEIGHBORS; MINIMUM SPANNING TREES; NEAREST NEIGHBORS; NETWORK DESIGN; REAL WORLD DATA; ROAD NETWORK; SCALABLE SOLUTION; VLSI LAYOUT;

ALGORITHMS; COMPUTER VISION; GENERAL PURPOSE COMPUTERS; PARALLEL ARCHITECTURES; PARALLEL PROCESSING SYSTEMS; PROGRAM PROCESSORS; TELEPHONE CIRCUITS;

PROBLEM SOLVING;

EID: 84863904223     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-31125-3_6     Document Type: Conference Paper

References (30)

1. NVIDIA CUDA - Programming Guide (2007), http://developer.nvidia.com/
2. Abello, J., Buchsbaum, A.L., Westbrook, J.: A Functional Approach to External Graph Algorithms. In: Bilardi, G., Pietracaprina, A., Italiano, G.F., Pucci, G. (eds.) ESA 1998. LNCS, vol. 1461, pp. 332-343. Springer, Heidelberg (1998)
3. Arefin, A.S., Inostroza-Ponta, M., Mathieson, L., Berretta, R., Moscato, P.: Clustering Nodes in Large-Scale Biological Networks Using External Memory Algorithms. In: Xiang, Y., Cuzzocrea, A., Hobbs, M., Zhou, W. (eds.) ICA3PP 2011, Part II. LNCS, vol. 7017, pp. 375-386. Springer, Heidelberg (2011)
4. Arefin, A.S., Riveros, C., Berretta, R., Moscato, P.: Gpu-fs-knn: A fast and scalable knn computation technique using gpu. Faculty Postgrad Research Poster, The University of Newcastle, Australia (September 2011), http://www.newcastle.edu.au/Resources/Faculties/ Faculty%20of%20Engineering%20and%20Built%20Environment/Research/Arefin.pdf
5. Bader, D.A., Cong, G.: Fast shared-memory algorithms for computing the minimum spanning forest of sparse graphs. J. P. Dist. Comp. 66, 1366-1378 (2006)
6. Bader, D.A., Madduri, K.: GTgraph: A suite of synthetic graph generators (2006)
7. Barr, R.S., Helgaon, R.V., Kennington, J.L.: Minimal spanning trees: an empirical investigation of parallel algorithms. Parallel Computing 12(1), 45-52 (1989)
8. Bentley, J.L., Friedman, J.H.: Fast algorithms for constructing minimal spanning trees in coordinate spaces. IEEE Trans. Comput. 27, 97-105 (1978)
9. Boruvka, O.: O Jistém Problému Minimálním (About a Certain Minimal Problem) (in Czech, German summary). Práce Mor. Prírodoved. Spol. v Brne III 3 (1926)
10. Chung, S., Condon, A.: Parallel implementation of borvka's minimum spanning tree algorithm. In: Proceedings of the 10th International Parallel Processing Symposium, IPPS 1996, pp. 302-308 (1996)
11. Dehne, F., Götz, S.: Practical parallel algorithms for minimum spanning trees. In: Symposium on Reliable Distributed Systems, pp. 366-371 (1998)
12. Demetrescu, C., Goldberg, A., Johnson, D.: 9th DIMACS implementation challenge - shortest paths (2006), http://www.dis.uniroma1.it/~challenge9/
13. Dyen, I., Kruskal, J.B., Black, P.: An indoeuropean classification: A lexicostatistical experiment. Trans. of the American Phil. Soc. 82(5), iii-iv, 1-132
14. Graham, R.L., Hell, P.: On the history of the minimum spanning tree problem. IEEE Ann. Hist. Comput. 7, 43-57 (1985)
15. Harish, P., Narayanan, P.J.: Accelerating Large Graph Algorithms on the GPU Using CUDA. In: Aluru, S., Parashar, M., Badrinath, R., Prasanna, V.K. (eds.) HiPC 2007. LNCS, vol. 4873, pp. 197-208. Springer, Heidelberg (2007)
16. Harris, M.: Optimizing parallel reduction in cuda. NVIDIA Developer Tech. (2008)
17. Hoberock, J., Bell, N.: Thrust - a productivity-oriented library for cuda. Template library, Version 1.4 (2010), http://code.google.com/p/thrust/
18. Inostroza-Ponta, M.: An Integrated and Scalable Approach Based on Combinatorial Optimization Techniques for the Analysis of Microarray Data. Ph.D. thesis, School of Electrical Engineering and Computer Science, The University of Newcastle, Australia (2008)
19. Johnson, D.B., Metaxas, P.: A parallel algorithm for computing minimum spanning trees. In: Proceedings of the Fourth Annual ACM Symposium on Parallel Algorithms and Architectures, SPAA 1992, pp. 363-372 (1992)
20. Kang, S., Bader, D.A.: An efficient transactional memory algorithm for computing minimum spanning forest of sparse graphs. In: Proc. of the 14th ACM SIGPLAN Symp., Principles and Practice of Parallel Programming, PPoPP, pp. 15-24 (2009)
21. Moret, B.M.E., Shapiro, H.D.: An empirical assessment of algorithms for constructing a minimum spanning tree. DIMACS Monographs (1994)
22. Murtagh, F.: A survey of recent advances in hierarchical clustering algorithms. Comput. J. 26(4), 354-359 (1983)
23. Nesetril, J., Milkov, E., Nesetrilov, H.: Otakar boruvka on minimum spanning tree problem translation of both the 1926 papers, comments, history. Discrete Mathematics 233(1-3), 3-36 (2001)
24. Nobari, S.H., Bressan, S., Rassi, C.: A data parallel minimum spanning tree algorithm for most graphics processing units. In: Proc. of the Int. Con. ADPC (2010)
25. Rostrup, S., Srivastava, S., Singhal, K.: Fast and memory-efficient minimum spanning tree on the gpu (2011)
26. Siek, J., Lee, L.Q., Lumsdaine, A.: Boost random number library (June 2000), http://www.boost.org/libs/graph/
27. Sintorn, E., Assarsson, U.: Fast parallel gpu-sorting using a hybrid algorithm. J. Parallel Distrib. Comput. 68, 1381-1388 (2008)
28. Tepper, M., Mejail, M., Musé, P., Almansa, A.: Boruvka Meets Nearest Neighbors (2011), http://hal.archives-ouvertes.fr/docs/00/58/31/20/PDF/ preprint.pdf
29. van de Vijver, M.J., He, Y.D., et al.: A gene-expression signature as a predictor of survival in breast cancer. The New England JM 347(25), 1999-2009 (2002)
30. Vineet, V., Harish, P., Patidar, S., Narayanan, P.J.: Fast minimum spanning tree for large graphs on the gpu. In: Proc. of Conf. on HPG, pp. 167-171. ACM (2009)