Playdoh: A lightweight Python library for distributed computing and optimisation

Journal of Computational Science

Volumn 4, Issue 5, 2013, Pages 352-359

  Rossant, Cyrille ^a,b   Fontaine, Bertrand ^a,b   Goodman, Dan F M ^a,b  

^a UNIVERSITÉ PARIS DESCARTES   (France)

^b ECOLE NORMALE SUPÉRIEURE   (France)

Author keywords

Distributed computing; High performance computing; Optimisation; Parallel computing; Python

Indexed keywords

COMPUTING UNITS; HARDWARE AND SOFTWARE; HIGH PERFORMANCE COMPUTING; HIGH PERFORMANCE SCIENTIFIC COMPUTING; LOOSELY COUPLED; NUMERICAL INTEGRATIONS; OPTIMISATIONS; PYTHON;

DISTRIBUTED COMPUTER SYSTEMS; MONTE CARLO METHODS; OPTIMIZATION; PARALLEL ARCHITECTURES; PARALLEL PROCESSING SYSTEMS; PARTIAL DIFFERENTIAL EQUATIONS;

HIGH LEVEL LANGUAGES;

EID: 84883337081     PISSN: 18777503     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jocs.2011.06.002     Document Type: Article

References (30)

1. K. Asanovic, R. Bodik, B.C. Catanzaro, J.J. Gebis, P. Husbands, K. Keutzer, D.A. Patterson, W.L. Plishker, J. Shalf, S.W. Williams, The Landscape of Parallel Computing Research: A View from Berkeley. EECS Department, University of California, Berkeley, 2006 (Tech. Rep. UCB/EECS-2006-183, 2006-183).
2. Beazley D.M. Python Essential Reference 2009, Addison-Wesley Professional.
3. Brette R., Rudolph M., Carnevale T., Hines M., Beeman D., Bower J.M., Diesmann M., Morrison A., Goodman P.H., Harris F.C., Zirpe M., Natschlä ger T., Pecevski D., Ermentrout B., Djurfeldt M., Lansner A., Rochel O., Vieville T., Muller E., Davison A.P., Boustani S.E., Destexhe A. Simulation of networks of spiking neurons: a review of tools and strategies. Journal of Computational Neuroscience 2007, 23:349-398.
4. H. Bücker, O. Fortmeier, M. Petera, Solving a parameter estimation problem in a three-dimensional conical tube on a parallel and distributed software infrastructure, Journal of Computational Science (2011).
5. Choudhary A., Fox G., Hiranandani S., Kennedy K., Koelbel C., Ranka S., Saltz J. Software support for irregular and loosely synchronous problems. Computing Systems in Engineering 1992, 3(1-4):43-52.
6. Chrisochoides N., Houstis E., Rice J. Mapping algorithms and software environment for data parallel PDE iterative solvers. Journal of Parallel and Distributed Computing 1994, 21(1):75-95.
7. Goldberg D.E. Genetic Algorithms in Search, Optimization, and Machine Learning 1989, Addison-Wesley, Reading, Menlo Park.
8. Goodman D., Brette R. Brian: a simulator for spiking neural networks in Python. Frontiers in Neuroinformatics 2008, 2:5. PMC2605403.
9. Goodman D.F.M., Brette R. The Brian simulator. Frontiers in Neuroscience 2009, 3(2):192-197.
10. Gropp W., Lusk E., Skjellum A. Using MPI: Portable Parallel Programming with the Message-Passing Interface 1999, MIT Press.
11. Hansen N., Ostermeier A. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 2001, 9(2):159-195.
12. Kennedy J., Eberhart R.C. Particle swarm optimization. Proceedings of IEEE International Conference on Neural Networks, vol. 4 1995, 1942-1948.
13. A. Klöckner, N. Pinto, Y. Lee, B. Catanzaro, P. Ivanov, A. Fasih, PyCUDA: GPU Run-time Code Generation for High-performance Computing, Nov 2009, 0911.3456.
14. Kumar V. Introduction to Parallel Computing 2002, Addison-Wesley Longman Publishing Co. Inc.
15. Leist A., Playne D., Hawick K. Interactive visualisation of spins and clusters in regular and small-world Ising models with CUDA on GPUs. Journal of Computational Science 2010, 1(1):33-40.
16. Lim D., Ong Y., Jin Y., Sendhoff B., Lee B. Efficient hierarchical parallel genetic algorithms using grid computing. Future Generation Computer Systems 2007 May, 23(4):658-670.
17. McInnes L., Allan B., Armstrong R., Benson S., Bernholdt D., Dahlgren T., Diachin L., Krishnan M., Kohl J., Larson J. Parallel PDE-based simulations using the common component architecture. Numerical Solution of Partial Differential Equations on Parallel Computers 2006, 327-381.
18. Morton K.W., Mayers D.F. Numerical Solution of Partial Differential Equations: An Introduction 2005, Cambridge University Press.
19. Mueller C.L., Baumgartner B., Ofenbeck G., Schrader B., Sbalzarini I.F. pCMALib: a parallel Fortran 90 library for the evolution strategy with covariance matrix adaptation. Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation 2009, 1411-1418.
20. Nakasato N. Implementation of a parallel tree method on a GPU. Journal of Computational Science 2011.
21. Nilsen J., Cai X., Høyland B., Langtangen H. Simplifying the parallelization of scientific codes by a function-centric approach in Python. Computational Science & Discovery 2010, 3:015003.
22. Oliphant T.E. Python for scientific computing. Computing in Science & Engineering 2007, 10-20.
23. Owens J.D., Luebke D., Govindaraju N., Harris M., Krger J., Lefohn A.E., Purcell T.J. A survey of general purpose computation on graphics hardware. Proceedings of the Computer Graphics Forum, vol. 26 2007, 80-113.
24. Paszynski M., Pardo D., Paszynska A. Parallel multi-frontal solver for p adaptive finite element modeling of multi-physics computational problems. Journal of Computational Science 2010, 1(1):48-54.
25. Perez F., Granger B.E. IPython: a system for interactive scientific computing. Computing in Science & Engineering 2007, 21-29.
26. Rossant C., Goodman D.F.M., Platkiewicz J., Brette R. Automatic fitting of spiking neuron models to electrophysiological recordings. Frontiers in Neuroinformatics 2010, 4.
27. Rossant C., Goodman D.F.M., Fontaine B., Platkiewicz J., Magnusson A.K., Brette R. Fitting neuron models to spike trains. Frontiers in Neuroscience 2011, 5.
28. Schutte J.F., Reinbolt J.A., Fregly B.J., Haftka R.T., George A.D. Parallel global optimization with the particle swarm algorithm. International Journal for Numerical Methods in Engineering 2004, 61(13):2296-2315.
29. Vecchiola C., Pandey S., Buyya R. High-performance cloud computing: a view of scientific applications. 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks 2009, 4-16.
30. Whitley D., Rana S., Heckendorn R. The island model genetic algorithm: on separability, population size and convergence. Journal of Computing and Information Technology 1999, 7(1):33-47.