Implementing parallel Google map-reduce in Eden

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

Volumn 5704 LNCS, Issue , 2009, Pages 990-1002

  Berthold, Jost ^a   Dieterle, Mischa ^a   Loogen, Rita ^a  

^a PHILIPPS UNIVERSITY MARBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COORDINATION STRUCTURES; EFFICIENT IMPLEMENTATION; GENERAL PROGRAMMING MODEL; GOOGLE MAPS; HASKELL; LARGE DATASETS; PARALLEL IMPLEMENTATIONS; PERFORMANCE IMPLEMENTATION; RUN-TIME ANALYSIS; RUNTIMES; STREAM MERGING; STREAM PROCESSING;

ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; COORDINATION REACTIONS;

INTERNET;

EID: 70350630420     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-03869-3_91     Document Type: Conference Paper

References (20)

1. Cole, M.I.: Algorithmic Skeletons: Structured Management of Parallel Computation. Research Monographs in Parallel and Distributed Comp. MIT Press, Cambridge (1989)
2. Rabhi, F.A., Gorlatch, S. (eds.): Patterns and Skeletons for Parallel and Distributed Computing. Springer, Heidelberg (2003)
3. MPI-2: Extensions to the message-passing interface. Technical report, University of Tennessee, Knoxville (July 1997)
4. Gamma, E., Helm, R., Johnson, R.E., Vlissides, J.M.: Design patterns: Abstraction and reuse of object-oriented design. In: Nierstrasz, O. (ed.) ECOOP 1993. LNCS, vol. 707, pp. 406-431. Springer, Heidelberg (1993)
5. Mattson, T.G., Sanders, B.A., Massingill, B.L.: Patterns for Parallel Programming. SPS. Addison-Wesley, Reading (2005)
6. Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. In: OSDI 2004, Sixth Symp. on Operating System Design and Implementation (2004)
7. Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Communications of the ACM 51(1), 107-113 (2008)
8. Lämmel, R.: Google's MapReduce programming model - Revisited. Science of Computer Programming 70(1), 1-30 (2008)
9. Poldner, M., Kuchen, H.: Scalable farms. In: Proceedings of ParCo 2005. NIC Series, vol. 33 (2005)
10. Benoit, A.: ESkel - The Edinburgh Skeleton Library. University of Edinburgh (2007), http://homepages.inf.ed.ac.uk/abenoit1/eSkel/
11. Michaelson, G., Scaife, N., Bristow, P., King, P.: Nested Algorithmic Skeletons from Higher Order Functions. Parallel Algorithms and Appl. 16, 181-206 (2001)
12. Blelloch, G.: Programming Parallel Algorithms. Communications of the ACM 39(3), 85-97 (1996)
13. Danelutto, M., DiCosmo, R., Leroy, X., Pelagatti, S.: Parallel functional programming with skeletons: the OCamlP3L experiment. In: ACM workshop on ML and its applications (1998)
14. Loogen, R., Ortega-Mallén, Y., Peña-Marí, R.: Parallel Functional Programming in Eden. Journal of Functional Programming 15(3), 431-475 (2005)
15. Chu, C.T., Kim, S.K., Lin, Y.A., Yu, Y., Bradski, G., Ng, A.Y., Olukotun, K.: Mapreduce for machine learning on multicore. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) Advances in Neural Information Processing Systems, vol. 19. MIT Press, Cambridge (2007)
16. Ranger, C., Raghuraman, R., Penmetsa, A., Bradski, G., Kozyraki, C.: Evaluating mapreduce for multi-core and multiprocessor systems. In: HPCA 2007: Int. Symp. on High Performance Computer Architecture, pp. 13-24. IEEE Computer Society, Los Alamitos (2007)
17. Bayley, D., Barszcz, E., et al.: NAS Parallel Benchmarks. Technical Report RNR-94-007, NASA Advanced Supercomputing (NAS) Division (1994)
18. Apache Hadoop Project. Web page, http://hadoop.apache.org
19. de Kruijf, M., Sankaralingam, K.: MapReduce for the Cell B.E. Architecture. Technical Report TR1625, Computer Sc.Dept., Madison, WI (2007)
20. Yeung, J.H., Tsang, C., Tsoi, K., Kwan, B.S., Cheung, C.C., Chan, A.P., Leong, P.H.: Map-reduce as a programming model for custom computing machines. In: Proc. IEEE Symposium on Field-Programmable Custom Computing Machines (FCCM). IEEE Computer Society, Los Alamitos (2008)