Supporting dynamic parameter sweep in adaptive and user-steered workflows

WORKS'11 - Proceedings of the 6th Workshop on Workflows in Support of Large-Scale Science, Co-located with SC'11

Volumn , Issue , 2011, Pages 31-36

  Dias, Jonas ^a   Ogasawara, Eduardo ^a,b   De Oliveira, Daniel ^b   Porto, Fabio ^c   Coutinho, Alvaro L G A ^a   Mattoso, Marta ^a  

^a FEDERAL UNIVERSITY OF RIO DE JANEIRO   (Brazil)

^b CENTRO FEDERAL DE EDUCAÇÃO TECNOLÓGICA CELSO SUCKOW DA FONSECA   (Brazil)

^c LABORATÓRIO NACIONAL DE COMPUTAÇÃO CIENTÍFICA   (Brazil)

Author keywords

Design; Management

Indexed keywords

COMPUTATIONAL SCIENCE; CONTROL STRUCTURE; DISTRIBUTED EXECUTION; DYNAMIC MECHANISM; DYNAMIC PARAMETERS; EXECUTION TIME; LANCZOS ALGORITHM; LARGE SCALE EXPERIMENTS; LARGE SPACES; PARAMETER COMBINATION; PARAMETER SPACES; PROCESS MANAGEMENT; PROOF OF CONCEPT; SCIENTIFIC WORKFLOWS; WORK-FLOWS;

COBALT COMPOUNDS; DESIGN; EXPERIMENTS; ITERATIVE METHODS; MANAGEMENT; RATING; WORK SIMPLIFICATION;

INFORMATION MANAGEMENT;

EID: 84857972266     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2110497.2110502     Document Type: Conference Paper

References (23)

1. Y.L. Simmhan, B. Plale, e D. Gannon, 2005, A survey of data provenance in e-science, ACM SIGMOD Record, v. 34, n. 3, p. 31-36. (Pubitemid 43152529)
2. I. Altintas, C. Berkley, E. Jaeger, M. Jones, B. Ludascher, e S. Mock, 2004, Kepler: an extensible system for design and execution of scientific workflows, In: Scientific and Statistical Database Management, p. 423-424, Greece.
3. S.P. Callahan, J. Freire, E. Santos, C.E. Scheidegger, C.T. Silva, e H.T. Vo, 2006, VisTrails: visualization meets data management, In: SIGMOD, p. 745-747, Chicago, Illinois, USA. (Pubitemid 46946568)
4. A.M.R. Dávila, P.N. Mendes, G. Wagner, D.A. Tschoeke, R.R.C. Cuadrat, F. Liberman, L. Matos, T. Satake, K.A.C.S. Ocaña, et al., 2008, ProtozoaDB: dynamic visualization and exploration of protozoan genomes, Nucleic Acids Research, v. 36, n. Database issue, p. D547-D552. (Pubitemid 351149783)
5. E. Ogasawara, D. Oliveira, F. Chirigati, C.E. Barbosa, R. Elias, V. Braganholo, A. Coutinho, e M. Mattoso, 2009, Exploring many task computing in scientific workflows, In: MTAGS, p. 1-10, Portland, Oregon.
6. Y. Gil, E. Deelman, M. Ellisman, T. Fahringer, G. Fox, D. Gannon, C. Goble, M. Livny, L. Moreau, et al., 2007, Examining the Challenges of Scientific Workflows, Computer, v. 40, n. 12, p. 24-32.
7. E. Ogasawara, J. Dias, D. Oliveira, F. Porto, P. Valduriez, e M. Mattoso, 2011, An Algebraic Approach for Data-Centric Scientific Workflows, Proceedings of the VLDB Endowment, v. 4, n. 12, p. 1328-1339.
8. D. Oliveira, K. Ocana, E. Ogasawara, J. Dias, F. Baião, e M. Mattoso, 2011, A Performance Evaluation of X-ray Crystallography Scientific Workflow using SciCumulus, In: International Conference on Cloud ComputingInternational Conference on Cloud Computing, Washington D.C.
9. G. Guerra, F. Rochinha, R. Elias, D. Oliveira, E. Ogasawara, J. Dias, M. Mattoso, e A.L.G.A. Coutinho, 2011, Uncertainty Quantification in Computational Predictive Models for Fluid Dynamics Using Workflow Management Engine, International Journal for Uncertainty Quantification, accepted
10. M. Heath, 2002, Scientific computing?: an introductory survey. 2 ed. Boston, McGraw-Hill.
11. Y. Zhao, M. Hategan, B. Clifford, I. Foster, G. von Laszewski, V. Nefedova, I. Raicu, T. Stef-Praun, e M. Wilde, 2007, Swift: Fast, Reliable, Loosely Coupled Parallel Computation, In: Proc. of the 3rd IEEE World Congress on Services, p. 206, 199, Salt Lake City, USA.
12. B. de S. Leite Pires de Lima, B. Pinheiro Jacob, e N. Francisco Favilla Ebecken, 2005, A hybrid fuzzy/genetic algorithm for the design of offshore oil production risers, International Journal for Numerical Methods in Engineering, v. 64, n. 11, p. 1459-1482.
13. X. Ma e N. Zabaras, 2009, An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations, J. Comput. Phys., v. 228, p. 3084-3113.
14. A.L.G.A. Coutinho, L. Landau, L.C. Wrobel, e N.F.F. Ebecken, 1989, Modal solution of transient heat conduction utilizing Lanczos algorithm, International Journal for Numerical Methods in Engineering, v. 28, n. 1, p. 13-25.
15. S. Wang, E. de Sturler, e G.H. Paulino, 2007, Large-scale topology optimization using preconditioned Krylov subspace methods with recycling, International Journal for Numerical Methods in Engineering, v. 69, n. 12, p. 2441-2468. (Pubitemid 46421594)
16. D. Amsallem e C. Farhat, 2008, Interpolation Method for Adapting Reduced-Order Models and Application to Aeroelasticity, AIAA Journal, v. 46, p. 1803-1813.
17. M.L. Parks, E. de Sturler, G. Mackey, D.D. Johnson, e S. Maiti, 2006, Recycling Krylov Subspaces for Sequences of Linear Systems, SIAM Journal on Scientific Computing, v. 28, n. 5, p. 1651.
18. M.E. Kilmer e E. de Sturler, 2006, Recycling Subspace Information for Diffuse Optical Tomography, SIAM Journal on Scientific Computing, v. 27, n. 6, p. 2140.
19. S.W. Sorde, S.K. Aggarwal, J. Song, M. Koh, e S. See, 2007, Modeling and Verifying Non-DAG Workflows for Computational Grids, In: Services, IEEE Congress on, p. 237-243, Los Alamitos, CA, USA.
20. M. Mattoso, A. Coutinho, R. Elias, D. Oliveira, e E. Ogasawara, 2010, Exploring Parallel Parameter Sweep in Scientific Workflows, In: WCCM - World Congress on Computational Mechanics, Australia.
21. J. Dean e S. Ghemawat, 2008, MapReduce: simplified data processing on large clusters, Commun. ACM, v. 51, n. 1, p. 107-113.
22. D. Abramson, B. Bethwaite, C. Enticott, S. Garic, e T. Peachey, 2011, Parameter Exploration in Science and Engineering Using Many-Task Computing, IEEE Trans. Parallel Distrib. Syst., v. 22, n. 6 (jun.), p. 960-973.
23. V.S. Kumar, P. Sadayappan, G. Mehta, K. Vahi, E. Deelman, V. Ratnakar, J. Kim, Y. Gil, M. Hall, et al., 2009, An integrated framework for performance-based optimization of scientific workflows, In: Proceedings of the 18th ACM international symposium on High performance distributed computing, p. 177-186, New York, NY, USA.