Desktop techniques as long as the exploration of terascale size, time-varying data sets

Desktop techniques as long as the exploration of terascale size, time-varying data sets

Desktop techniques as long as the exploration of terascale size, time-varying data sets

Ganzer, Tony, Morning Edition Producer;Reporter has reference to this Academic Journal, PHwiki organized this Journal Desktop techniques as long as the exploration of terascale size, time-varying data sets John Clyne & Alan Norton Scientific Computing Division National Center as long as Atmospheric Research Boulder, CO USA National Center as long as Atmospheric Research Space Weather Turbulence Atmospheric Chemistry Climate Weather The Sun More than just the atmosphere from the earth’s oceans to the solar interior Goals Improve scientist’s ability to investigate in addition to underst in addition to complex phenomena found in high-resolution fluid flow simulations Accelerate analysis process in addition to improve scientific productivity Enable exploration of data sets hereto as long as e impractical due to unwieldy size Gain insight into physical processes governing fluid dynamics widely found in the natural world Demonstrate visualization’s ability to aid in day-to-day scientific discovery process

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Problem motivation: Analysis of high resolution numerical turbulence simulations Simulations are huge!! May require months of supercomputer time Multi-variate (typically 5 to 8 variables) Time-varying data A single experiment may yield terabytes of numerical data Analysis requirements are as long as midable Numerical outputs simulate phenomena not easily observed!!! Interesting domain regions (ROIs) may not be known apriori Additionally Historical focus of computing centers on batch processing Dichotomy of batch in addition to interactive processing needs Currently available analysis tools inadequate as long as large data needs Single threaded, 32bit, in-core algorithms Lack advanced visualization capabilities Currently available visualization tools ill-suited as long as analysis And furthermore [Numerical] models that can currently be run on typical supercomputing plat as long as ms produce data in amounts that make storage expensive, movement cumbersome, visualization difficult, in addition to detailed analysis impossible. The result is a significantly reduced scientific return from the nation’s largest computational ef as long as ts. A sampling of various technology per as long as mance curves Not all technologies advance at same rate!!!

Example: Compressible plume dynamics 504x504x2048 5 variables (u,v,w,rho,temp) ~500 time steps saved 9TBs storage Six months compute time required on 112 IBM SP RS/6000 processors Three months as long as post-processing Data may be analyzed as long as several years M. Rast, 2004. Image courtesy of Joseph Mendoza, NCAR/SCD Visualization in addition to Analysis Plat as long as m as long as oceanic, atmospheric, in addition to solar Research (VAPoR) Key components Domain specific numerically simulated turbulence in the natural sciences Data processing language Data post processing in addition to quantitative analysis Advanced visualization Identify spatial/temporal ROIs Multiresolution Enable speed/quality tradeoffs This work is funded in part through a U.S. National Science Foundation, In as long as mation Technology Research program grant Combination of visualization with multiresolution data representation that provide sufficient data reduction to enable interactive work on time-varying data

Multiresolution Data Representation Geometry Reduction (Schroeder et al, 1992; Lindrstrom & Silva, 2001;Shaffer in addition to Garl in addition to , 2001) Wavelet based progressive data access Mathematical trans as long as ms similar to Fourier trans as long as mations Invertible in addition to lossless Numerically efficient as long as ward in addition to inverse trans as long as m No additional storage costs Permit hierarchical representations of functions See Clyne, VIIP2003 Render geometry data Pixels Visualization Pipeline Data reduction (Cignoni, et al 1994; Wilhelms & Van Gelder, 1994; Pascucci & Frank, 2001; Clyne 2003) Putting it all together Visual data browsing permits rapid identification of features of interest, reducing data domain Multiresolution data representation af as long as ds a second level of data reduction by permitting speed/quality trade offs enabling rapid hypothesis testing Quantitative operators in addition to data processing enable data analysis Result: Integrated environment as long as large-data exploration in addition to discovery Goal: Avoid unnecessary in addition to expensive full-domain calculations Execute on human time scales!!! Compressible Convection 1283 5123 M. Rast, 2002

Compressible plume 504x504x2048 Full 252x252x1024 1/8 126x126x512 1/64 63x63x256 1/512 Compressible plume data set shown at native in addition to progressively coarser resolutions Resolution: Problem size: Rendering timings 5123 Compressible Convection 5042×2048 Compressible Plume Reduced resolution af as long as ds responsive interaction while preserving all but finest features SGI Octane2, 1x600MHz R14k SGI Origin, 10x600MHz R14k Derived quantities p: pressure : density T: temperature : ionization potential : Avogadro’s number me: electron mass k: Boltzmann’s constant h: Planck’s constant Derived quantities produced from the simulation’s field variables as a post-process

Calculation timings as long as derived quantities Note: 1/2th resolution is 1/8th problem size, etc Deriving new quantities on interactive time scales only possible with data reduction SGI Origin, 10x600MHz R14k Error in approximations Error is highly dependent on operation per as long as med Algebraic operations tested introduced low error even after substantial coarsening Error grows rapidly as long as gradient calculation Point-wise error gives no indication of global (average) error Point-wise, normalized, maximum, absolute error Integrated visualization in addition to analysis on interactively selected subdomains: Vertical vorticity of the flow Mach number of the vertical velocity Efficient analysis requires rapid calculation in addition to visualization of unanticipated derived quantities. This can be facilitated by a combination of subdomain selection in addition to resolution reduction.

A test of multiresolution analysis: Force balance in supersonic downflows 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 as long as ces are maximum on the tube periphery due to mass flux convergence. The same interpretation results from analysis at half resolution. Full Half Resolution Subdomain selection in addition to reduced resolution together yield data reduction by a factor of 128 Summary Presented prototype, integrated analysis environment aimed at aid investigation of high-resolution numerical fluid flow simulations Orders of magnitude data reduction achieved through: Visualization: Reduce full domain to ROI Multiresolution: Enable speed/quality trade-offs Coarsened data frequently suitable as long as rapid hypothesis testing that may later be verified at full resolution Future work Quantify in addition to predict error in results obtained with various mathematical operations applied to coarsened data Investigate lossy in addition to lossless data compression Add support as long as less regular meshes Explore other scientific domains Climate, weather, atmospheric chemistry,

Future Original 20:1 Lossy Compression Acknowledgements 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 Liya Li, Ohio State Systems Support Joey Mendoza, NCAR, SCD Questions

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