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Aashish Chaudhary
aashish.chaudhary@kitware.com
Technical Leader
with
Patrick O’Leary, Petr Votava (NASA),
Dr. Rama Nemani (NASA), Chris Harris,
Chris Kotfila, Doruk Aztek, Andrew Michaelis (NASA)
Scientific Computing and
Visualization using HDF
July 19th 2016
Durham, NC
What We
Do at
Kitware?
0pen Source
and Open Data
is strongly
encouraged and
practised
at Kitware
It started with VTK
Parallel Processing and Rendering - Paraview
Computer Vision
Images,
Video,
Point
Clouds
Recognition
by Function
Content-
based
Retrieval
Event &
Activity
Recognition
Anomaly
Detection
3D Extraction
and
Compression
Detection
& Tracking
Medical Computing
Quantitative imaging Electronic health records
Vascular analysis
Surgical guidance
And simulation
Digital pathology Orthopedic analysis
Longitudinal and
population shape
analysis
Interactive medical applications
and visualizations
Community Adaptation
HDF at Kitware
Climate Community High Performance Computing
Extensible Data Model and Format
- Developed to exchange
scientific data between HPC
codes and tools
- Heavy data is stored using
HDF5
Network Common
Data Form
(NetCDF)
- Most projects
use NetCDF4
Medical Community Vision Community
Leading-edge
algorithms for
registering and
segmenting
multidimensional data
ACME
The Accelerated Climate Modeling for Energy
(ACME) project is sponsored by the Earth System
Modeling (ESM) program (Biological and
Environmental Research) with eight national
laboratories and six partner institutions to develop
and apply the most complete, leading-edge climate
and Earth system models to challenging and
demanding climate-change research imperatives.
Most commonly used data format - NetCDF4
Data streaming using OpenDAP
Python Interface for most of the tools
OpenNEX
NEX is a platform for scientific
collaboration, knowledge sharing and
research for the Earth science com
Global Daily Downscaled Projections (NEX-
GDDP, NetCDF4)
MODIS-Land and Atmosphere (HDF)
Web VisualizationData processing
Gaia
Gaia
Web VisualizationData processing
Pure JS?
HDF5 File Organization
Preprocessing Simulation Postprocessing
Scientific Computing and Visualization using HDF
Possible Improvements
Streaming and Big Data analytics
- Any useful ingestion of HDF data
into cluster requires ETL pipeline
- For some tools, computation cannot
move close to the data, streaming
support is necessary in such cases
- Optimal read/write on cloud storage
Web-Support
- More tools and projects are moving
to support web-enabled data
analysis and visualization
- Pure JS implementation if possible
Summary
● HDF is widely data format for scientific computing, climate/geospatial
visualization, and in other domains at Kitware
● Recently we have started using HDF for information visualization
● We are looking forward to HDF usage on cloud and web-environment
● Kitware is always looking for strong open source collaborations and is
committed to push open-source scientific computing to its next level
Information
Aashish Chaudhary: aashish.chaudhary@kitware.com
LinkedIn: www.linkedin.com/in/aachaudhary
Kitware: http://www.kitware.com
NASA-NEX: https://nex.nasa.gov/nex
Kitware-AIST: https://github.com/OpenGeoscience/nex
HPC Cloud : http://www.kitware.com/publications/item/view/1784
HPCloud Github: https://github.com/Kitware/HPCCloud

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Scientific Computing and Visualization using HDF

  • 1. Aashish Chaudhary aashish.chaudhary@kitware.com Technical Leader with Patrick O’Leary, Petr Votava (NASA), Dr. Rama Nemani (NASA), Chris Harris, Chris Kotfila, Doruk Aztek, Andrew Michaelis (NASA) Scientific Computing and Visualization using HDF July 19th 2016 Durham, NC
  • 2. What We Do at Kitware? 0pen Source and Open Data is strongly encouraged and practised at Kitware
  • 4. Parallel Processing and Rendering - Paraview
  • 5. Computer Vision Images, Video, Point Clouds Recognition by Function Content- based Retrieval Event & Activity Recognition Anomaly Detection 3D Extraction and Compression Detection & Tracking
  • 6. Medical Computing Quantitative imaging Electronic health records Vascular analysis Surgical guidance And simulation Digital pathology Orthopedic analysis Longitudinal and population shape analysis Interactive medical applications and visualizations
  • 8. HDF at Kitware Climate Community High Performance Computing Extensible Data Model and Format - Developed to exchange scientific data between HPC codes and tools - Heavy data is stored using HDF5 Network Common Data Form (NetCDF) - Most projects use NetCDF4 Medical Community Vision Community Leading-edge algorithms for registering and segmenting multidimensional data
  • 9. ACME The Accelerated Climate Modeling for Energy (ACME) project is sponsored by the Earth System Modeling (ESM) program (Biological and Environmental Research) with eight national laboratories and six partner institutions to develop and apply the most complete, leading-edge climate and Earth system models to challenging and demanding climate-change research imperatives. Most commonly used data format - NetCDF4 Data streaming using OpenDAP Python Interface for most of the tools
  • 10. OpenNEX NEX is a platform for scientific collaboration, knowledge sharing and research for the Earth science com Global Daily Downscaled Projections (NEX- GDDP, NetCDF4) MODIS-Land and Atmosphere (HDF)
  • 16. Possible Improvements Streaming and Big Data analytics - Any useful ingestion of HDF data into cluster requires ETL pipeline - For some tools, computation cannot move close to the data, streaming support is necessary in such cases - Optimal read/write on cloud storage Web-Support - More tools and projects are moving to support web-enabled data analysis and visualization - Pure JS implementation if possible
  • 17. Summary ● HDF is widely data format for scientific computing, climate/geospatial visualization, and in other domains at Kitware ● Recently we have started using HDF for information visualization ● We are looking forward to HDF usage on cloud and web-environment ● Kitware is always looking for strong open source collaborations and is committed to push open-source scientific computing to its next level
  • 18. Information Aashish Chaudhary: aashish.chaudhary@kitware.com LinkedIn: www.linkedin.com/in/aachaudhary Kitware: http://www.kitware.com NASA-NEX: https://nex.nasa.gov/nex Kitware-AIST: https://github.com/OpenGeoscience/nex HPC Cloud : http://www.kitware.com/publications/item/view/1784 HPCloud Github: https://github.com/Kitware/HPCCloud