Towards performance control on the Grid

Advances in computational Grid technologies are enabling the development of simulations of complex biological and physical systems. Such simulations can be assembled from separate components—separately deployable computation units of well-defined functionality. Such an assemblage can represent an application composed of interacting simulations or might comprise multiple instances of a simulation executing together, each running with different simulation parameters. However, such assemblages need the ability to cope with heterogeneous and dynamically changing execution environments, particularly where such changes can affect performance. This paper describes the design and implementation of a prototype performance control system (PerCo), which is capable of monitoring the progress of simulations and redeploying them so as to optimize performance. The ability to control performance by redeployment is demonstrated using an assemblage of lattice Boltzmann simulations running with and without control policies. The cost of using PerCo is evaluated and it is shown that PerCo is able to reduce overall execution time.

[1]  Fred Douglis,et al.  Transparent process migration: Design alternatives and the sprite implementation , 1991, Softw. Pract. Exp..

[2]  Peter M. A. Sloot,et al.  Lattice-Boltzmann hydrodynamics on parallel systems , 1998 .

[3]  P. Español,et al.  Statistical Mechanics of Dissipative Particle Dynamics. , 1995 .

[4]  R. Templer,et al.  Chapter 3 - Polymorphism of Lipid-Water Systems , 1995 .

[5]  Hirotada Ohashi,et al.  Immiscible real-coded lattice gas , 2000 .

[6]  Daniel A. Reed,et al.  The Autopilot Performance-Directed Adaptive Control System , 1997 .

[7]  Peter V. Coveney,et al.  Large-scale lattice Boltzmann simulations of complex fluids: advances through the advent of computational Grids , 2005, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[8]  Sathish S. Vadhiyar,et al.  A performance oriented migration framework for the grid , 2003, CCGrid 2003. 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings..

[9]  Peter V. Coveney,et al.  A PARALLEL LATTICE-BOLTZMANN METHOD FOR LARGE SCALE SIMULATIONS OF COMPLEX FLUIDS , 2001 .

[10]  Graham D. Riley,et al.  Performance control of scientific coupled models in Grid environments , 2005, Concurr. Pract. Exp..

[11]  Eduardo Huedo,et al.  A framework for adaptive execution in grids , 2004, Softw. Pract. Exp..

[12]  A. Stephen McGough,et al.  ICENI: Optimisation of component applications within a Grid environment , 2002, Parallel Comput..

[13]  H. W. Veen,et al.  Handbook of Biological Physics , 1996 .

[14]  Yeomans,et al.  Lattice Boltzmann simulation of nonideal fluids. , 1995, Physical review letters.

[15]  Hirotada Ohashi,et al.  Formation of micelle in the real-coded lattice gas , 2000 .

[16]  Raymond Kapral,et al.  Continuous-velocity lattice-gas model for fluid flow , 1998 .

[17]  Francine Berman,et al.  Adaptive Computing on the Grid Using AppLeS , 2003, IEEE Trans. Parallel Distributed Syst..

[18]  P. V. Coveney,et al.  Simulations of amphiphilic fluids using mesoscale lattice-Boltzmann and lattice-gas methods , 2003 .

[19]  Evgenia Smirni,et al.  The next frontier: interactive and closed loop performance steering , 1996, 1996 Proceedings ICPP Workshop on Challenges for Parallel Processing.

[20]  Michael E. Cates,et al.  Simulation of amphiphilic mesophases using dissipative particle dynamics , 1999 .

[21]  Graham D. Riley,et al.  GCF : a general coupling framework , 2006 .

[22]  P. Coveney,et al.  Steering in computational science: Mesoscale modelling and simulation , 2003, physics/0307061.

[23]  A. Lamura,et al.  A lattice Boltzmann model of ternary fluid mixtures , 1995 .

[24]  A. Stephen McGough,et al.  Optimisation of component-based applications within a grid environment , 2001, SC '01.

[25]  Jean-Pierre Rivet,et al.  Lattice Gas Hydrodynamics , 1987 .

[26]  J. Boon The Lattice Boltzmann Equation for Fluid Dynamics and Beyond , 2003 .

[27]  Graham D. Riley,et al.  The design of a performance steering system for component-based grid applications , 2003 .

[28]  Flekkoy,et al.  Foundations of dissipative particle dynamics , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[29]  M Nekovee,et al.  Lattice-Boltzmann simulations of self-assembly of a binary water-surfactant system into ordered bicontinuous cubic and lamellar phases. , 2001, Journal of the American Chemical Society.

[30]  Peter V Coveney,et al.  Coarsening dynamics of ternary amphiphilic fluids and the self-assembly of the gyroid and sponge mesophases: Lattice-Boltzmann simulations. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[31]  Francine Berman,et al.  Toward a framework for preparing and executing adaptive grid programs , 2002, Proceedings 16th International Parallel and Distributed Processing Symposium.

[32]  I-Hsin Chung,et al.  Active Harmony: Towards Automated Performance Tuning , 2002, ACM/IEEE SC 2002 Conference (SC'02).

[33]  Peter V Coveney,et al.  Three-dimensional lattice-Boltzmann simulations of critical spinodal decomposition in binary immiscible fluids. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[34]  Miron Livny,et al.  Checkpoint and Migration of UNIX Processes in the Condor Distributed Processing System , 1997 .

[35]  Peter V. Coveney,et al.  A ternary lattice Boltzmann model for amphiphilic fluids , 2000, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[36]  Peter V. Coveney,et al.  Self-assembly of the gyroid cubic mesophase: Lattice-Boltzmann simulations , 2003, cond-mat/0310390.

[37]  Shan,et al.  Lattice Boltzmann model for simulating flows with multiple phases and components. , 1993, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.