A general model for the generation and scheduling of parameter sweep experiments in computational grid environments

Abstract Parameter sweep experiments (PSE) involve several issues. In this work, we consider two of them: the generation of the parameters space and the scheduling of the associated tasks. Thus, we propose a general model to generate the parameters space of any PSE applying the Nested Summation Symbol operator. On the other hand, for the scheduling of these kinds of problems, we test an adaptive scheduling approach with fault tolerance. This approach has been implemented, using the DRMAA-C version for GridWay, to allocate tasks in a Grid environment. In the tests, the scheduler shows a good performance. Moreover, the CPU usage of the scheduler is quite low.

[1]  Hyoungwoo Park,et al.  A Model of Problem Solving Environment for Integrated Bioinformatics Solution on Grid by Using Condor , 2004, GCC.

[2]  Emili Besalú,et al.  Nested summation symbols and perturbation theory , 1993 .

[3]  David J. Lilja.  Exploiting the parallelism available in loops , 1994, Computer.

[4]  Alfonso Niño,et al.  An Adaptive Approach to Task Scheduling Optimization in Dynamic Grid Environments , 2009, GCA.

[5]  Mohamed Ould-Khaoua,et al.  Performance Evaluation of Parallel And Distributed Systems , 2007 .

[6]  David Abramson,et al.  High performance parametric modeling with Nimrod/G: killer application for the global grid? , 2000, Proceedings 14th International Parallel and Distributed Processing Symposium. IPDPS 2000.

[7]  Edith Schonberg,et al.  Factoring: a method for scheduling parallel loops , 1992 .

[8]  R. Axelrod.  The Dissemination of Culture , 1997 .

[9]  Rosa M. Badia,et al.  Performance of computationally intensive parameter sweep applications on Internet‐based Grids of computers: the mapping of molecular potential energy hypersurfaces , 2007, Concurr. Comput. Pract. Exp..

[10]  Alfonso Niño,et al.  A Fault Tolerant Adaptive Method for the Scheduling of Tasks in Dynamic Grids , 2009, 2009 Third International Conference on Advanced Engineering Computing and Applications in Sciences.

[11]  John Darlington,et al.  Using ICENI to run parameter sweep applications across multiple Grid resources , 2004 .

[12]  Eduardo Huedo,et al.  Experiences on adaptive grid scheduling of parameter sweep applications , 2004, 12th Euromicro Conference on Parallel, Distributed and Network-Based Processing, 2004. Proceedings..

[13]  Peifeng Li,et al.  An Adaptive Chunk Self-Scheduling Scheme on Service Grid , 2008, 2008 IEEE Asia-Pacific Services Computing Conference.

[14]  Emili Besalú,et al.  Generalized Rayleigh-Schrödinger perturbation theory in matrix form , 1994 .

[15]  CONSTANTINE D. POLYCHRONOPOULOS,et al.  Guided Self-Scheduling: A Practical Scheduling Scheme for Parallel Supercomputers , 1987, IEEE Transactions on Computers.

[16]  S. Duane,et al.  Hybrid Monte Carlo , 1987 .

[17]  Raül Sirvent,et al.  Monitoring and steering Grid applications with GRID superscalar , 2010, Future Gener. Comput. Syst..

[18]  Anthony T. Chronopoulos,et al.  Implementation of Distributed Loop Scheduling Schemes on the TeraGrid , 2007, 2007 IEEE International Parallel and Distributed Processing Symposium.

[19]  Rosa M. Badia,et al.  Performance of computationally intensive parameter sweep applications on Internet-based Grids of computers: the mapping of molecular potential energy hypersurfaces: Research Articles , 2007 .

[20]  Sebastián Reyes,et al.  A Heuristic Approach to the Allocation of Different Workloads in Computational Grid Environments , 2009 .

[21]  L.M. Ni,et al.  Trapezoid Self-Scheduling: A Practical Scheduling Scheme for Parallel Compilers , 1993, IEEE Trans. Parallel Distributed Syst..

[22]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[23]  Daniel Grosu,et al.  Analysis of Performance Behaviors of Grid Connected Clusters , 2005 .

[24]  David Abramson,et al.  Scheduling parameter sweep applications on global Grids: a deadline and budget constrained cost–time optimization algorithm , 2005, Softw. Pract. Exp..

[25]  Sebastián Reyes,et al.  Derivation of self-scheduling algorithms for heterogeneous distributed computer systems: Application to internet-based grids of computers , 2009, Future Gener. Comput. Syst..

[26]  Miron Livny,et al.  Harnessing the Capacity of Computational Grids for High Energy Physics , 2000 .

[27]  Eduardo Huedo,et al.  A modular meta-scheduling architecture for interfacing with pre-WS and WS Grid resource management services , 2007, Future Gener. Comput. Syst..

[28]  Emili Besalú,et al.  Definition, Mathematical Examples and Quantum Chemical Applications of Nested Summation Symbols and Logical Kronecker Deltas , 1994, Comput. Chem..

[29]  Jason M. Daida,et al.  Parameter sweeps for exploring GP parameters , 2005, GECCO '05.

[30]  Aaron Striegel,et al.  GIPSE: streamlining the management of simulation on the grid , 2005, 38th Annual Simulation Symposium.

[31]  Jesús Labarta,et al.  Graph-Based Task Replication for Workflow Applications , 2009, 2009 11th IEEE International Conference on High Performance Computing and Communications.

[32]  Thomas L. Casavant,et al.  A Taxonomy of Scheduling in General-Purpose Distributed Computing Systems , 1988, IEEE Trans. Software Eng..

[33]  Rajkumar Buyya,et al.  The Virtual Laboratory: Enabling Molecular Modeling for Drug Design on the World Wide Grid , 2001 .

[34]  Scott S. Hampton,et al.  Shadow hybrid Monte Carlo: an efficient propagator in phase space of macromolecules , 2004 .

[35]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..