Mahout on heterogeneous clusters using HadoopCL

2nd Workshop on Parallel Programming for Analytics Applications, PPAA 2015 - Proceedings

Volumn , Issue , 2015, Pages 9-16

  Li, Xiangyu ^a   Grossman, Max ^b   Kaeli, David ^a  

^a Northeastern University   (United States)

^b Rice University   (United States)

Author keywords

APU; GPGPU; HSA; Machine learning; MapReduce

Indexed keywords

AUXILIARY POWER SYSTEMS; CLUSTER COMPUTING; DIGITAL STORAGE; DISTRIBUTED DATABASE SYSTEMS; LARGE DATASET; LEARNING SYSTEMS; MACHINE LEARNING; OPEN SOURCE SOFTWARE; OPEN SYSTEMS; PARALLEL PROGRAMMING; PROGRAM PROCESSORS;

DATA-INTENSIVE APPLICATION; GPGPU; HETEROGENEOUS CLUSTERS; HETEROGENEOUS PLATFORMS; LARGE AMOUNTS OF DATA; MACHINE LEARNING TECHNIQUES; MAP-REDUCE; PROGRAMMING FLEXIBILITY;

RECOMMENDER SYSTEMS;

EID: 84942516625     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2726935.2726940     Document Type: Conference Paper

References (15)

1. Apache mahout website. http://mahout.apache.org/.
2. D. A. Alcantara, V. Volkov, S. Sengupta, M. Mitzenmacher, J. D. Owens, and N. Amenta. Building an efficient hash table on the GPU. In W. W. Hwu, editor, GPU Computing Gems, volume 2, chapter 4, pages 39-53. Morgan Kaufmann, Oct. 2011. .
3. B. Catanzaro, N. Sundaram, and K. Keutzer. A map reduce framework for programming graphics processors. In In Workshop on Software Tools for MultiCore Systems, 2008. URL http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.141.1644.
4. L. Chen, X. Huo, and G. Agrawal. Accelerating mapreduce on a coupled cpu-gpu architecture. In Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC '12, pages 25:1- 25:11, Los Alamitos, CA, USA, 2012. IEEE Computer Society Press. ISBN 978-1-4673-0804-5. URL http://dl.acm.org/citation.cfm?id=2388996.2389030.
5. J. Dean and S. Ghemawat. Mapreduce: simplified data processing on large clusters. Commun. ACM, 51(1):107-113, Jan. 2008. ISSN 0001-0782. . URL http://doi.acm.org/10. 1145/1327452.1327492.
6. B. Gaster, L. Howes, D. Kaeli, P. Mistry, and D. Schaa. Heterogeneous computing with OpenCL, 2nd Edition. Morgan Kaufmann, 2011.
7. G.Frost. Aparapi website. https://code.google.com/p/ aparapi/.
8. M. Grossman, M. Breternitz, and V. Sarkar. Hadoopcl2: Motivating the design of a distributed, heterogeneous programming system with machine-learning applications. Currently under review for publication IEEE Transactions on Parallel and Distributed Systems.
9. B. He, W. Fang, Q. Luo, N. K. Govindaraju, and T. Wang. Mars: A mapreduce framework on graphics processors. In Proceedings of the 17th International Conference on Parallel Architectures and Compilation Techniques, PACT '08, pages 260-269, New York, NY, USA, 2008. ACM. ISBN 978- 1-60558-282-5. . URL http://doi.acm.org/10.1145/ 1454115.1454152.
10. J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl. Grouplens: Applying collaborative filtering to usenet news. Commun. ACM, 40(3):77-87, Mar. 1997. ISSN 0001-0782. . URL http://doi.acm.org/10. 1145/245108.245126.
11. Y. Lin, S. Okur, and C. Radoi. Hadoop+aparapi: Making heterogenous mapreduce programming easier. 2012.
12. G. Linden, B. Smith, and J. York. Amazon.com recommendations: item-to-item collaborative filtering. Internet Computing, IEEE, 7(1):76-80, Jan 2003. ISSN 1089-7801. .
13. NVIDIA Corporation. NVIDIA CUDA C Programming Guide, June 2011.
14. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web, WWW '01, pages 285-295, New York, NY, USA, 2001. ACM. ISBN 1-58113-348-0. . URL http://doi.acm.org/10.1145/371920.372071.
15. J. A. Stuart and J. D. Owens. Multi-gpu mapreduce on gpu clusters. In Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium, IPDPS '11, pages 1068-1079, Washington, DC, USA, 2011. IEEE Computer Society. ISBN 978-0-7695-4385-7. . URL http://dx.doi: 10.1109/IPDPS.2011.102.