A "minimal disruption" skeleton experiment: Seamless map & reduce embedding in OCaml

Procedia Computer Science

Volumn 9, Issue , 2012, Pages 1837-1846

  Danelutto, M ^a   Di Cosmo, R ^b  

^a UNIVERSITY OF PISA   (Italy)

^b UNIVERSITÉ PARIS DESCARTES   (France)

Author keywords

Algorithmic skeletons; Map; Reduce; Structured parallel programming

Indexed keywords

EID: 84885413322     PISSN: 18770509     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/j.procs.2012.04.202     Document Type: Conference Paper

References (18)

1. T. Mattson, B. Sanders, B. Massingill, Patterns for parallel programming, Addison-Wesley Professional, 2004.
2. K. Asanovic, R. Bodik, J. Demmel, T. Keaveny, K. Keutzer, J. Kubiatowicz, N. Morgan, D. Patterson, K. Sen, J. Wawrzynek, D. Wessel, K. Yelick, A view of the parallel computing landscape, Commun. ACM 52 (10) (2009) 56-67.
3. D. Goswami, A. Singh, B. R. Preiss, From design patterns to parallel architecture skeletons, Journal of Parallel and Distributed Computing 62 (4) (2002) 669-695.
4. M. Cole, Bringing skeletons out of the closet: A pragmatic manifesto for skeletal parallel programming, Parallel Computing 30 (3) (2004) 389-406.
5. M. Aldinucci, M. Danelutto, P. Dazzi, Muskel: an expandable skeleton environment, Scalable Computing: Practice and Experience 8 (4) (2007) 325-341.
6. P. Ciechanowicz, H. Kuchen, Enhancing Muesli's Data Parallel Skeletons for Multi-Core Computer Architectures, in: Proc. of the 10th Intl. Conference on High Performance Computing and Communications (HPCC), IEEE, Los Alamitos, CA, USA, 2010, pp. 108-113.
7. R. Di Cosmo, Z. Li, S. Pelagatti, P. Weis, Skeletal parallel programming with ocamlp31 2.0, Parallel Processing Letters 18 (1) (2008) 149-164.
8. J. Enmyren, C. W. Kessler, SkePU: A Multi-Backend Skeleton Programming Library for Multi-GPU Systems, in: HLPP 10: Proceedings of the fourth international workshop on High-level parallel programming and applications, 2010, pp. 5-14, baltimore, Maryland, USA.
9. M. Leyton, J. M. Piquer, Skandium: Multi-core programming with algorithmic skeletons, in: Proc. of Intl. Euromicro PDP 2010: Parallel Distributed and network-based Processing, IEEE, Pisa, Italy, 2010, pp. 289-296.
10. H. Tanno, H. Iwasaki, Parallel skeletons for variable-length lists in sketo skeleton library, in: Proceedings of the 15th International Euro-Par Conference on Parallel Processing, Euro-Par '09, Springer-Verlag, Berlin, Heidelberg, 2009, pp. 666-677.
11. N. Thomas, G. Tanase, O. Tkachyshyn, J. Perdue, N. M. Amato, L. Rauchwerger, A framework for adaptive algorithm selection in stapl, in: Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming, PPoPP '05, ACM, New York, NY, USA, 2005, pp. 277-288.
12. M. Danelutto, R. Di Cosmo, X. Leroy, S. Pelagatti, Parallel functional programming with skeletons: the OCamlP3L experiment, in: Proceedings ACM workshop on ML and its applications, Cornell University, 1998.
13. J.-C. Filliâtre, K. Kalyanasundaram, Functory: A Distributed Computing Library for Objective Caml, in: Trends in Functional Programming, Madrid, Spain, 2011.
14. P. W. Trinder, R. F. Loidl, Hans-Wolfgang Pointon, Parallel and distributed Haskells, Journal of Functional Programming 12 (4 & 5) (2002) 469-510.
15. F. Cesarini, S. Thompson, Erlang programming: a concurrent approach to software development, O Réilly Media, 2009.
16. J. W. Backus, Can programming be liberated from the von neumann style? a functional style and its algebra of programs, Commun. ACM 21 (8) (1978) 613-641.
17. D. B. Skillicorn, The Bird-Meertens formalism as a parallel model, in: Software for Parallel Computation, Volume 106 of NATO ASI Series F, Springer, 1993, pp. 120-133.
18. J. Dean, S. Ghemawat, Mapreduce: simplified data processing on large clusters, Commun. ACM 51 (2008) 107-113.