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John Clyne 1 , Alan Norton 1 and Mark Rast 2 National Center for Atmospheric Research

Experiences with multiresolution methods for qualitative and quantitative analysis of terascale turbulence data sets. John Clyne 1 , Alan Norton 1 and Mark Rast 2 National Center for Atmospheric Research SCD Users Forum.

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John Clyne 1 , Alan Norton 1 and Mark Rast 2 National Center for Atmospheric Research

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  1. Experiences with multiresolution methods for qualitative and quantitative analysis of terascale turbulence data sets John Clyne1, Alan Norton1 and Mark Rast2 National Center for Atmospheric Research SCD Users Forum This work is funded in part through a U.S. National Science Foundation, Information Technology Research program grant 1 Scientific Computing Division 2 High Altitude Observatory

  2. Goal: Facilitate analysis of terascale size earth sciences data sets • Our ability to numerically model earth sciences phenomena has surpassed our ability to analyze and gain insight from the resultant simulation outputs • Historical focus of computing centers on batch processing • See tomorrow’s 10:40am talk: Towards a Comprehensive Environment for Data Analysis and Visualization • Dichotomy of batch and interactive processing needs • Analysis is an inherently interactive task • Numerical simulation is well suited to batch processing • Lack of scalable tools • 32bit • single threaded • in-core algorithms • Moore’s law doesn’t apply to all technology curves • E.g. File access speeds

  3. Visualization and Analysis Platform for ocean, atmosphere, and sun Researchers (VAPoR) • Key components • Domain specific application focus: numerically simulated turbulence • Integrate visualization into analysis process, and employ as a first order data reduction technique • Employ multiresolution data representation as a second order data reduction technique • Assumptions • High resolution 3D grids • Desktop PC with commodity, hardware accelerated graphics • Familiarity with existing analysis tools (e.g. IDL, Matlab) • For *some* analysis operations, results from coarsened data can yield same interpretation as results from native data

  4. 1/8 1/4 1/2 • Multiple copies of data at varying power of two resolutions • Storage costs: Enabling speed/quality tradeoffs with multiresolution data representation • 2D Example: Texture MIP Mapping 1

  5. Permit hierarchical data representation Invertible and lossless (subject to floating point round off errors) Numerically efficient – forward and inverse transform No additional storage costs!!! Wavelets Transforms for 3D Multiresolution data representation

  6. VAPoR Project Status • Alpha release of software currently under evaluation by steering committee • Scalar data • Cartesian grids • Expect first production release later this year • Vector data • More general computation grids (e.g. AMR, spherical grids)

  7. Steering Committee Nic Brummell - CU, JILA Aimé Fournier – NCAR, IMAGe Helene Politano - Observatoire de la Cote d'Azur Pablo Mininni, NCAR, IMAGe Yannick Ponty - Observatoire de la Cote d'Azur Annick Pouquet - NCAR, ESSL Mark Rast - NCAR, HAO Duane Rosenberg - NCAR, IMAGe Matthias Rempel - NCAR, HAO Yuhong Fan - NCAR, HAO Developers Alan Norton – NCAR, SCD John Clyne – NCAR, SCD Research Collaborators Kwan-Liu Ma, U.C. Davis Hiroshi Akiba, U.C. Davis Han-Wei Shen, Ohio State Systems Support Joey Mendoza, NCAR, SCD People

  8. Preliminary experiences

  9. 504x504x2048 5 variables (u,v,w,rho,temp) ~500 time steps saved 9TBs storage (4GBs/variable/timestep) Six months compute time required on 112 IBM SP RS/6000 processors Three months for post-processing Data may be analyzed for several years Example: Compressible plume dynamics M. Rast, 2004. Image courtesy of Joseph Mendoza, NCAR/SCD

  10. Full domain seen from above Full domain seen from above Subdomain from side Subdomain from side Integrated visualization and analysis on interactively selected subdomains: Mach number of the vertical velocity Efficient analysis requires rapid calculation and visualization of unanticipated derived quantities. This can be facilitated by a combination of subdomain selection and resolution reduction. Vertical vorticity of the flow

  11. A test of multiresolution analysis: Force balance in supersonic downflows Resolution Full Half Subdomain selection and reduced resolution together yield data reduction by a factor of 128 Sites of supersonic downflow are also those of very high vertical vorticity. The core of the vortex tubes are evacuated, with centripetal acceleration balancing that due to the inward directed pressure gradient. Buoyancy forces are maximum on the tube periphery due to mass flux convergence. The same interpretation results from analysis at half resolution.

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