Virtual Laboratory for eScience

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Robert G. Belleman, PhD Computer Architectures
and Parallel Systems GroupDepartment of Computer
Science, Universiteit van Amsterdam Email
robbel_at_science.uva.nl
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Outline

• Grids
• The VL-e project

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The Grid

• A computational grid is a hardware and software
  infrastructure that provides, dependable,
  consistent, pervasive and inexpensive access to
  high-end computational capabilities.
• I. Foster, C. Kesselman, The Grid Blueprint for
  a new computing infrastructure.

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The Grid

• The Computational Grid is analogous to the
  Electricity (power) Grid and the vision is to
  offer an (almost) dependable, consistent,
  pervasive and inexpensive access to high-end
  resources irrespective their location of physical
  existence and the location of access.
• D. Laforenza, CNUCE/CNR, Pisa (Italy).

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The Grid .vs. The Internet

• The Internet is about sharing information.
• The Grid is about sharing resources.

databases, web pages, high performance computing
systems, networks, data acquisition equipment,
data storage / archiving and retrieval
facilities, algorithms, search engines, images,
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The Grid .vs. The Internet

• The Internet is about sharing information.
• The Grid is about sharing resources.

Applications
Grid services layer
Grid middleware
Grid fabric layer
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Grid layers
Application Toolkit Layer
Portals
APIs
PSEs
Grid Services Layer
Fault recovery
Data transport
Resource discovery
Resource allocation
Load balancing
AAAS
QoS
Data storage devices
HPC systems
Control interfaces
Grid Fabric Layer
Algorithms
Data acquisition devices
Protocols
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The real problem

• Coordinated resource sharing and problem solving
  in a dynamic, multi-institutional virtual
  organization.
• Without sacrificing local autonomy - each
  organization has their own set of rules that must
  be respected.

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Checklist

• A Grid is a system that
• coordinates resources that are not subject to
  centralized control,
• uses standard, open, general-purpose protocols
  and interfaces,
• delivers non-trivial qualities of service.

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The Grid summary

• The Grid is the middleware that creates a
  virtual organization to seamlessly integrate
  resources from distributed sources.
• An emerging technology with standards under
  development (WSRF).
• No turnkey solutions, specialist knowledge
  required (Globus).

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The VL-e project

• The VL-e project will develop the necessary
  knowledgefor the e-Science infrastructure in the
  Netherlands.
• The mission of the VL-E project is
• To boost e-Science by the creation of an
  e-Science environment and doing research on
  methodologies.
• The strategy will be
• To carry out concerted research along the
  complete e-Science technology chain, ranging from
  applications to networking, focused on new
  methodologies and reusable components.
• The essential components of the total e-Science
  technology chain are
• e-Science development areas,
• a Virtual Laboratory development area,
• a Large Scale Distributed computing development
  area, consisting of high performance networking
  and grid parts.

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Specific goals of VL-e

• Development of application specific Problem
  Solving Environments (PSE) (medical apps, physics
  apps, )
• Improve reusability/sharing across application
  domains (generic features of applications are
  integrated in the VL toolkit)
• VL-e is an evolving environment

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Data Intensive Science
Medical Diagnosis Imaging
Food Informatics
Dutch Telescience
Bio- Informatics
Bio- Diversity
VL Application Oriented Services
Management of comm. computing
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Experiment definition in VL

• Ontology definitions
• Structured definition of experimental data (OWL)
• Work flow definitions
• Recreate complex experiments into a Process Flow
  Template (PFT)
• Analyses process definitions
• Topologies of Grid-enabled data processing
  modules

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Components in a VL experiment

• Process-Flow Templates
• Graphical representation of data elements and
  processing steps in an experimental procedure
• Information to support context-sensitive
  assistance

• Study
• Descriptions of experimental steps
  represented as an instance of a PFT with
  references to experiment topologies

• Experiment Topology
• Graphical representation of self-contained data
• processing modules attached to each other in a
  workflow

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Medical Diagnosis and Imaging PSE
filtering
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AMC and VUmc
Philips Intera 3T MRI scannerAMC, Amsterdam
MEG scannerVUmc, Amsterdam
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Eddy current reduction

• Shear, magnification and translation as a result
  of residual currents in DWI
• 2D matching to correct
• Computationally expensive
• Parallelization throughdomain decomposition
• Computing cycles via Grid
• Integrated PACS solution

Effects of residual eddy currents on Philips 3T
Intera with DWI.Figure by Erik-Jan Vlieger, AMC.
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Matched Masked Bone Elimination

• MMBE method
• Matching of CT scans
• Computationally expensive
• Within VL-E
• Computing cycles from the Grid
• Integrated PACS solution

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Brain Imaging and Fiber Tractography

• Diffusion Weighted Imaging (DWI)
• Restricted Brownian motion results in anisotropy
  that can be measured
• gt 6 measurements, reduced to tensor per voxel
• Largest eigenvectors give diffusion vector
• Whole volume fiber tracking can takemany hours
• Depends on size of volume andnumber of
  measurements per voxel
• Suitable for parallelization
• Visualization techniques

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MR Virtual Colonoscopy

• CT virtual colonoscopy exists
• Minimally invasive
• Use of MR has strong and weakpoints
• No X-ray(more suitable for screening)
• Worse Signal/Noise than CT(requires powerfull
  segmentationtechniques)

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MEG data analysis

• Inverse modeling and non-linear systems modeling
  of brain activity
• Parallelization of iterative solver

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Data storage, retrieval and sharing

• fMRI and MEG are scarce but complementary
  modalities
• Access to each others resources
• Shared data access
• Data sizes are 101 to 104 MB per scan
• High capacity, reliable and dependable storage
• Online and near-line access patterns
• Time/location independent access
• Collaborative scientific research
• Information sharing
• Metadata modeling
• Ownership, privacy regulations, AAAS

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Interactive 3D medical data visualization

• Innovative display solutions
• Co-located data visualization throughaugmented
  reality (AR)
• Animated (4D) datarepresentation
• Image guided surgery

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Conclusions

• VL-e science portal for experimental science
• Ontology, workflow and analysis support for
  various scientific areas
• Pervasive access to distributed resources across
  different institutions
• Based on Globus 2.4
• VL-e complements functionality that is missing at
  the Grid layer

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Participants

• Universiteit van Amsterdam
• Vrije Universiteit
• Vrije Universiteit medisch centrum
• Academisch Medisch Centrum
• Philips Research
• Philips Medical Systems
• IBM
• LogicaCMG
• TU Delft
• NIKHEF

• Unilever
• AMOLF
• SARA
• CWI
• Surfnet
• DSM
• KNMI
• FEI
• TNO-TPD
• TNO-Voeding

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More information on the VL-e project

• VL-e home page
• http//www.vl-e.nl