Dynamic Data Driven Applications Systems

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Dynamic Data Driven Applications Systems

• Joel Saltz
• Chair and Professor
• Biomedical Informatics Department
• The Ohio State University

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Parameter Study Application Scenarios

• Clinical imaging studies
• Determine tumor characteristics via segmentation,
  texture analysis of medical imagery
• Test and refine algorithms by invoking test
  algorithms on distributed datasets of gt1000
  dynamic contrast MR studies
• Simulation parameter studies
• 1000s of oil reservoir simulations used to
  determine how to optimize oil production

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Parameter Study Data Analyses

• Compare dataset contents
• Compare features
• Spatially based comparisons
• Map datasets between mesh/coordinate systems

MicroCT Osteoporosis Study Kim Powell, Cleveland
Clinic Don Stredney, OSC
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Canonical Services

• Component Framework for Combined Task/Data
  Parallelism
• Data Aggregation, generalized reductions
• Crucial and ubiquitous in data analysis
• Integrated with Globus/NWS/SRB etc (NPACkage)
  OGSA integration underway

• Canonical services carried out by Data Parallel
  Components
• Data Cluster/Decluster/Spatial Indexing/Range
  Query Service (Inherited from Active Data
  Repository)
• Super-Semantic Data Cache when carrying out
  parameter studies, use caching to eliminate
  redundant computations (Andrade SC2002)
• Grid Generalized Reduction (Ferreira ICS2002

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Clinical Studies using Dynamic Contrast Imaging

• 1000s of dynamic images per research study
• Iterative investigation of image quantification,
  image registration and image normalization
  techniques
• Assess techniques ability to correctly
  characterize anatomy and pathophysiology
• Ground truth assessed by
• Biopsy results
• Changes in tumor structure and activity over time
  with treatment
• Images from many sites including NIH, Heidelberg,
  Oklahoma, Ohio State
• Collaboration with Michael Knopp, MD

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prior to therapy
1370
1370
after 2 cycles
1421
1421
1421
after 4 cycles
1438
1438
Knopp M, OSU Radiology / dkfz
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DCE-MR Analyses

• Fit pharmacokinetic model ODEs
• Tumor characterization using texture analysis and
  feature detection techniques
• Register images from consecutive studies
• Register images within single time dependent
  study to correct for patient motion
• Images obtained with varying time/space
  resolution -- interpolate onto common time/space
  mesh

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• A Data Intense Challenge
• The Instrumented Oilfield of the Future
• Participants
• University of Texas at Austin
• CSM Wheeler, Dawson, Peszynska
• IG Sen, Stoffa
• PGE Torres-Verdin
• University of ChicagoCS Stevens, Papka
• University of MarylandCS Sussman
• Ohio StateCS Saltz, Kurc
• RutgersECE Parashar
• MITEngineering Haines

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• A Data Intense Challenge
• The Instrumented Oilfield of the Future
• Industrial Support (Data)
• British Petroleum (BP)
• Chevron
• International Business Machines (IBM)
• Landmark
• Shell
• Schlumberger

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Production Simulation via Reservoir Modeling
Monitor Production by acquiring Time Lapse
Observations of Seismic Data
Revise Knowledge of Reservoir Model via Imaging
and Inversion of Seismic Data
Modify Production Strategy using an Optimization
Criteria
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Example Scenario (SC2001)
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Software Support

• Component Framework for Combined Task/Data
  Parallelism
• Use defines sequence of pipelined components --
  filter group
• User directive tells preprocessor/runtime system
  to generate and instantiate copies of filters
• Many filter groups can be simultaneously active
• Integration proceeding with Globus/Network
  Weather Service
• SC 2002, HCW2002, Parallel Computing 2001

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DataCutter

• Components
• Embarrasingly Parallel
• Generalized Reduction
• Wrapped MPI
• Flow control between components
• Schedulers place filters on grid processors
  (scheduler API)
• Stream based communication being upgraded to
  OSGA model
• Data Parallel Compiler Prototype
• NPACkage

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Integrating DataCutter with existing Grid
toolkits SRB (done), Globus, NWS (ongoing)

• SRB integration Subset and filter datasets
• Globus integration DataCutter uses Globus
  resource discovery, resource allocation,
  authentication, and authorization services.
• Network Weather Service (NWS) integration NWS
  for used for system monitoring.

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Cannonical Services

• Canonical services carried out by Data Parallel
  Components
• Data Cluster/Decluster/Spatial Indexing/Range
  Query Service (Inherited from Active Data
  Repository)
• Super-Semantic Data Cache (Andrade SC2002)
• Grid Generalized Reduction (Ferreira ICS2002)

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Clustering/Declustering Datasets

• Partition dataset into data chunks -- each chunk
  contains a set of data elements
• Each chunk is associated with a bounding box
• DataCutter Data Loading Service
• Distributes chunks across the disks in the system
• Constructs an R-tree index using bounding boxes
  of the data chunks

Disk Farm
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Super-Semantic Data Cache
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Advantage of Using Cached Intermediate Results
(Virtual Microscope)
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Grid Generalized Reduction
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Other Biomedical Grid Applications
Virtual Microscope

• Grid based clinical research support
• 1000s of clinical research sites
• Different studies involve different subsets of
  sites
• Ad-hoc federated databases
• Lots of data naming issues
• Support for anonymization
• Role based data access
• Support for authentication, encryption
• Support for image analysis
• NCI Cancer Center Support

versus
query
images
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DataCutter Development Group
University of Maryland Alan Sussman Henrique
Andrade Christian Hansen
Ohio State University Joel Saltz Tahsin Kurc Umit
Catalyurek Gagan Agrawal Renato Ferreira