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Scientific Data Management Center (ISIC)
http//sdmcenter.lbl.gov contains extensive
publication list
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Scientific Data Management Center
Participating Institutions
Center PI Arie Shoshani LBNL DOE
Laboratories co-PIs Bill Gropp, Rob
Ross ANL Arie Shoshani, Doron Rotem LBNL Terence
Critchlow, Chandrika Kamath LLNL Nagiza Samatova,
Andy White ORNL Universities co-PIs Mladen
Vouk North Carolina State Alok Choudhary
Northwestern Reagan Moore, Bertram Ludaescher
UC San Diego (SDSC) Calton Pu Georgia Tech Steve
Parker U of Utah (future)
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Phases of Scientific Exploration

• Data Generation
• From large scale simulations or experiments
• Fast data growth with computational power
• examples
• HENP 100 Teraops and 10 Petabytes by 2006
• Climate Spatial Resolution T42 (280 km) -gt T85
  (140 km) -gt T170 (70 km), T42 about 1 TB/100
  year run gt factor of 10-20
• Problems
• Cant dump the data to storage fast enough
  waste of compute resources
• Cant move terabytes of data over WAN robustly
  waste of scientists time
• Cant steer the simulation waste of time and
  resource
• Need to reorganize and transform data large
  data intensive tasks slowingprogress

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Phases of Scientific Exploration

• Data Analysis
• Analysis of large data volume
• Cant fit all data in memory
• Problems
• Find the relevant data need efficient indexing
• Cluster analysis need linear scaling
• Feature selection efficient high-dimensional
  analysis
• Data heterogeneity combine data from diverse
  sources
• Streamline analysis steps output of one step
  needs to match input of next

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Example Data Flow in TSI
Logistical Network
Courtesy John Blondin
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Goal Reduce the Data Management Overhead

• Efficiency
• Example parallel I/O, indexing, matching storage
  structures to the application
• Effectiveness
• Example Access data by attributes-not files,
  facilitate massive data movement
• New algorithms
• Example Specialized PCA techniques to separate
  signals or to achieve better spatial data
  compression
• Enabling ad-hoc exploration of data
• Example by enabling exploratory run and render
  capability to analyze and visualize simulation
  output while the code is running

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Approach

• Use an integrated framework that
• Provides a scientific workflow capability
• Supports data mining and analysis tools
• Accelerates storage and access to data
• Simplify data management tasks for the scientist
• Hide details of underlying parallel and
  indexingtechnology
• Permit assembly of modules using a simple
  graphical workflow description tool

SDM Framework
Scientific Process Automation Layer
Data Mining Analysis Layer
Scientific Application
Scientific Understanding
Storage Efficient Access Layer
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Technology Details by Layer
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AccomplishmentsStorage Efficient Access (SEA)
Shared memory communication
Parallel Virtual File System Enhancements and
deployment

• Developed Parallel netCDF
• Enables high performance parallel I/O to
  netCDF datasets
• Achieves up to 10 fold performance
  improvement over HDF5
• Enhanced ROMIO
• Provides MPI access to PVFS
• Advanced parallel file system interfaces for
  more efficient access
• Developed PVFS2
• Adds Myrinet GM and InfiniBand support
• improved fault tolerance
• asynchronous I/O
• offered by Dell and HP for Clusters
• Deployed an HPSS Storage Resource Manager (SRM)
  with PVFS
• Automatic access of HPSS files to PVFS through
  MPI-IO library
• SRM is a middleware component

After
Before
FLASH I/O Benchmark Performance (8x8x8 block
sizes)
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Robust Multi-file Replication

• Problem move thousands of files robustly
• Takes many hours
• Need error recovery
• Mass storage systems failures
• Network failures
• Use Storage Resource Managers (SRMs)
• Problem too slow
• Use parallel streams
• Use concurrent transfers
• Use large FTP windows
• Pre-stage files from MSS

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File tracking helps to identify bottlenecks
Shows that archiving is the bottleneck
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File tracking shows recovery from transient
failures
Total 45 GBs
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AccomplishmentsData Mining and Analysis (DMA)

• Developed Parallel-VTK
• Efficient 2D/3D Parallel Scientific
  Visualization for NetCDF and HDF files
• Built on top of PnetCDF
• Developed region tracking tool
• For exploring 2D/3D scientific databases
• Using bitmap technology to identify regions
  based on multi-attribute conditions
• Implemented Independent Component Analysis (ICA)
  module
• Used for accurate for signal separation
• Used for discovering key parameters that
  correlate with observed data
• Developed highly effective data reduction
• Achieves 15 fold reduction with high level of
  accuracy
• Using parallel Principle Component Analysis(PCA)
  technology
• Developed ASPECT
• A framework that supports a rich set ofpluggable
  data analysis tools
• Including all the tools above

Combustion region tracking
El Nino signal (red) and estimation (blue)
closely match
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ASPECT Analysis Environment
pVTK Tool
R Analysis Tool
Select Data
Take Sample
Data Mining Analysis Layer
Read Data (buffer-name) Write Data
Read Data (buffer-name) Write Data
Read Data (buffer-name)
Get variables (var-names, ranges)
Use Bitmap (condition)
Bitmap Index Selection
Storage Efficient Access Layer
PVFS
Parallel NetCDF
Hardware, OS, and MSS (HPSS)
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AccomplishmentsScientific Process Automation
(SPA)

• Unique requirements of scientific WFs
• Moving large volumes between modules
• Tightlly-coupled efficient data movement
• Specification of granularity-based iteration
• e.g. In spatio-temporal simulations a time
  step is a granule
• Support for data transformation
• complex data types (including file formats, e.g.
  netCDF, HDF)
• Dynamic steering of workflow by user
• Dynamic user examination of results
• Developed a working scientific work flow system
• Automatic microarray analysis
• Using web-wrapping tools developed by the center
• Using Kepler WF engine
• Kepler is an adaptation of the UC Berkeley tool,
  Ptolemy

workflow steps defined graphically
workflow results presented to user
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GUI for setting up and running workflows
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Re-applying Technology
SDM technology, developed for one application,
can be effectively targeted at many other
applications

• Technology
• Parallel NetCDF
• Parallel VTK
• Compressed bitmaps
• Storage Resource
• Managers
• Feature Selection
• Scientific Workflow

New Applications Climate Climate Combustion,
Astrophysics Astrophysics Fusion Astrophysics
(planned)
Initial Application Astrophysics
Astrophysics HENP HENP Climate Biology
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Broad Impact of the SDM Center

• Astrophysics
• High speed storage technology, parallel NetCDF,
  parallel VTK, and ASPECT integration software
  used for Terascale Supernova Initiative (TSI) and
  FLASH simulations
• Tony Mezzacappa ORNL, John Blondin NCSU,
  Mike Zingale U of Chicago, Mike Papka ANL
• Climate
• High speed storage technology, Parallel NetCDF,
  and ICA technology used for Climate Modeling
  projects
• Ben Santer LLNL, John Drake ORNL, John
  Michalakes NCAR
• Combustion
• Compressed Bitmap Indexing used for fast
  generation of flame regions and tracking their
  progress over time
• Wendy Koegler, Jacqueline Chen Sandia Lab

ASCI FLASH parallel NetCDF
Dimensionality reduction
Region growing
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Broad Impact (cont.)

• Biology
• Kepler workflow system and web-wrapping
  technology used for executing complex highly
  repetitive workflow tasks for processing
  microarray data
• Matt Coleman - LLNL
• High Energy Physics
• Compressed Bitmap Indexing and Storage Resource
  Managers used for locating desired subsets of
  data (events) and automatically retrieving data
  from HPSS
• Doug Olson - LBNL, Eric Hjort LBNL, Jerome
  Lauret - BNL
• Fusion
• A combination of PCA and ICA technology used to
  identify the key parameters that are relevant to
  the presence of edge harmonic oscillations in a
  Tokomak
• Keith Burrell - General Atomics

Building a scientific workflow
Dynamic monitoring of HPSS file transfers
Identifying key parameters for the DIII-D
Tokamak
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Goals for Years 4-5

• Fully develop the integrated SDM framework
• Implement the 3 layer framework on SDM center
  facility
• Provide a way to select only components needed
• Develop self-guiding web pages on the use of SDM
  components
• Use existing successful examples as guides
• Generalize components for reuse
• Develop general interfaces between components in
  the layers
• support loosely-coupled WSDL interfaces
• Support tightly-coupled components for efficient
  dataflow
• Integrate operation of components in the
  framework
• Hide details form user automate parallel access
  and indexing
• Develop a reusable library of components that can
  be selected for use in the workflow system