A Grid infrastructure that permits the coordination of heterogeneous and distributed computing resources provides a natural testbed for demonstrating the effectiveness of computational steering.

1
Lattice Boltzmann methods for Complex Fluids
Computational Steering
Computational Steering Lattice Boltzmann

• Steering has proved useful for detecting and
  studying topological changes in vortex cores
• Once a change is detected we can return to the
  last checkpoint and improve either the spatial or
  temporal resolution of the simulation
• The figure below shows how steering through
  parameter space allows a computational scientist
  to uncover different binary phases

• The lattice Boltzmann method is a mesoscale
  method for simulating complex fluids. This is
  something traditional CFD cannot do.
• We have a parallel and efficient implementation
  of the lattice Boltzmann method.

• Simulations that require greater computational
  resources also require increasingly sophisticated
  and complex tools for the analysis and management
  of the output of the simulations.
• Computational Steering enables the user to
  influence and interact with the otherwise
  sequential simulation and analysis process.
• As a minimum, Computational Steering improves
  utilization of computational resources and
  enhances a scientist's productivity.
• A central theme of RealityGrid is the
  facilitation of distributed and interactive
  exploration of physical models and systems
  through computational steering of parallel
  simulation codes and simultaneous on-line,
  high-end visualisation.
• For more details on computational steering in
  computational science
• J Chin, J Harting, S Jha, P V Coveney, A R
  Porter S M Pickles, Contemporary Physics
  (2003), in press

A
Mesoscopic
HPCx Gold medal on 1024 processors. Can run 10243
cells. Gold medal will allow us to make full use
of HPCxs Capability Computing Initiative

• Can simulate
• flow in porous media industrial applications
  e.g. hydrocarbon recovery process (A). Using a
  10243 simulation we can now model 1cm3 of rock
• non-equilibrium process of self-assembly of
  amphiphilic fluids into equilibrium
  liquid-crystalline cubic mesophases.(e.g. gyroid
  phase B).
• sheared equilibrium mesophases (C).

B
C
Lattice Boltzmann Simulations of Vortex Knot
Evolution

• A Grid infrastructure that permits the
  coordination of heterogeneous and distributed
  computing resources provides a natural testbed
  for demonstrating the effectiveness of
  computational steering.

With Professor B. Boghosians group at Tufts
University.

• Vorticity is the curl of the hydrodynamic
  velocity, and is strongest at the core of a
  swirling region of fluid
• At high Reynolds number, regions of high
  vorticity tend to form filamentary structures
• We study the dynamical behaviour of vortex knots
  and links
• Implemented a multiple time-scale relaxation
  lattice Boltzmann (MTLB) model for a single phase
  fluid
• Developed a pseudospectral Navier-Stokes solver
• Both codes are fully parallelised using MPI and
  use both the VTK graphics library and the
  RealityGrid steering library

• Centre for Computational Science, UCL
  (established 2003)
• Research centre headed by Professor Peter V.
  Coveney (PI for RealityGrid)
• Also involved in the new EPSRC e-Science Pilot
  Project in Integrative Biology
• Approximately 15 full time members
• Range of problems theoretical computational
  science, computer science, distributed computing
• Our different computational techniques span time
  and length-scales from the macro-, through the
  meso- and to the nano- and microscales. We are
  committed to studying new approaches (e.g. the
  Grid) and techniques that bridge these scales.
• For more information, please see

Running applications on the UK Level 2 Grid
Mesoscopic
Evolution of a (2,3) torus knot using the MTLB
model on a 1003 grid with a viscosity of 0.0002
lattice units.
RealityGrid deployed on the Level 2 Grid
These and many other of our simulations are done
on the Pittsburgh Supercomputer Centres LeMieux,
a 3,000 processor machine via 200,000SU grant
Biomolecular modelling
A Hybrid Multiscale Modelling Scheme
Interfacing the Macro and Micro

• An atomistic description of biological molecules
  is necessary to model their dynamics and
  thermodynamics
• We use NAMD2 on large, tightly-coupled parallel
  machines to investigate several biomolecular
  problems

• We have developed and tested a new hybrid
  algorithm comprising
• Molecular Dynamics (MD)
• Computational Fluid Dynamics (CFD)
• Buffering region swap fluxes of mass, momentum
  and energy
• Validated hybrid model results against
  established MD (results below)

• However, there is currently little incentive to
  run applications on the L2G.
• Access to L2G resources is still far from
  transparent (the key feature of a grid)
• It is hard to get answers to simple questions
  such as which resources are available?
• Support is limited because most sysadmins do not
  have much experience with GLOBUS
• The system is still in prototype stage.
• Need to foster much more involvement from the
  user community if the grid is to take off.

• Systems under investigation
• MHC complex (Immune Response)
• DHPS (Drug resistance)
• DNA (Drug binding)

Microscopic
Macroscopic to Microscopic

• This hybrid algorithm models a single polymer
  tethered to a wall undergoing shear flow.
• Explicit solvent
• Polymer made of generic spherical monomers held
  together by non-linear springs
• see R.Delgado-Buscalioni P.V.Coveney J. Chem.
  Phys. 119 2 978-987 (2003)
• Our hybrid scheme and loosely coupled models in
  general are good candidates for solving on the
  grid

As you run into bumps in the road, remember that
you are a Grid pioneer. Do not expect all the
roads to be paved (do not expect roads). Grids do
not yet run smoothly. From the Globus
Quickstart Guide

• NAMD2 has been successfully interfaced with the
  RealityGrid steering library