Data Analysis and Visualization B.K. Muite collaborators S. - - PowerPoint PPT Presentation

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Data Analysis and Visualization B.K. Muite collaborators S. - - PowerPoint PPT Presentation

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Data Analysis and Visualization B.K. Muite collaborators S. Arshad, S. Aseeri, D. Acevedo-Feliz, O. Batra sev, A. Bauer, D. DeMarle, M. Icardi, B. Leu, N. Li, A. Liu, E. M uller, B. Palen, M. Quell, H. Servat, P . Sheth, R. Speck, M. Van

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SLIDE 1

Data Analysis and Visualization

B.K. Muite collaborators

  • S. Arshad, S. Aseeri, D. Acevedo-Feliz, O. Batra˘

sev, A. Bauer, D. DeMarle, M. Icardi, B. Leu, N. Li, A. Liu, E. M¨ uller,

  • B. Palen, M. Quell, H. Servat, P

. Sheth, R. Speck, M. Van Moer, J. Vienne, H. Yi

benson.muite@ut.ee http://kodu.ut.ee/˜benson

16 April 2015

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SLIDE 2
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SLIDE 3

Outline

  • Motivation
  • Data from simulations
  • Challenges
  • Possible solutions
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SLIDE 4

Motivation

  • Visualization examples
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SLIDE 5

Volume rendering

  • https://www.flickr.com/photos/kitware/2864720427/in/pool-paraview
  • Electronic structure of a terpyridine molecule
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SLIDE 6

Volume rendering

  • https://www.flickr.com/photos/kitware/2864720427/in/pool-paraview
  • Cross wind fire simulation
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SLIDE 7

Iso Surfaces

  • http://www.paraview.org/Wiki/ParaView_In_Action#Magnetic_Reconnection_in_Earth.

E2.80.99s_Magnetosphere

  • Magnetic reconnection simulation
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SLIDE 8

Iso surfaces

  • https://www.flickr.com/photos/kitware/2864720427/in/pool-paraview
  • Convection simulation
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SLIDE 9

Domain Partitioning

  • https://www.flickr.com/photos/kitware/2293739197/in/pool-paraview/
  • Surface flow visualization by Renato N. Elias, Rio de

Janeiro, Brazil

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SLIDE 10

Volume Rendering

  • https://www.flickr.com/photos/kitware/2383791290/in/pool-paraview
  • Asteroid colliding with a planet. Simulation done using old

version of Chombo.

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SLIDE 11

Flow Field Visualization

  • https://www.flickr.com/photos/kitware/2293740417/in/pool-paraview/
  • Visualization around Formula 1 Race Car by Renato N.

Elias, Rio de Janeiro, Brazil

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SLIDE 12

Flow Field Visualization

  • https://www.flickr.com/photos/kitware/2294528826/in/pool-paraview/
  • Visualization around Formula 1 Race Car by Renato N.

Elias, Rio de Janeiro, Brazil

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SLIDE 13

Weather and Climate Prediction

  • https://wci.llnl.gov/simulation/computer-codes/visit/gallery/gallery-09
  • High spatial resolution, long time simulations
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SLIDE 14

Computational Fluid Dynamics

  • https://wci.llnl.gov/simulation/computer-codes/visit/gallery/gallery-07
  • Optimize design of cars, trains, airplanes, ships
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SLIDE 15

Computational Fluid Dynamics

  • https://wci.llnl.gov/simulation/computer-codes/visit/gallery/gallery-08
  • Optimize design of cars, trains, airplanes, ships
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SLIDE 16

Computational Fluid Dynamics

  • http://en.wikipidea.org/wiki/File:JRC_N700_series_z28.jpg
  • Optimize design of cars, trains, airplanes, ships
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SLIDE 17

Computational Solid Mechanics

  • https://wci.llnl.gov/simulation/computer-codes/visit/gallery/gallery-24
  • Optimize design of structures, consumer products, roads,

bridges, cars, trains, airplanes, ships

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SLIDE 18

Computational Materials Science

  • https://wci.llnl.gov/simulation/computer-codes/visit/gallery/gallery-39
  • Predict and control micro structural morphology to

determine macroscopic characteristics, such as fracture resistance

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SLIDE 19

Fusion

  • https://wci.llnl.gov/simulation/computer-codes/visit/gallery/gallery-38
  • Predict and control instabilities
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SLIDE 20

Time for IO

10 10

2

10

4

10 10

1

10

2

10

3

Number of Cores Computation Time With output No output Ideal

  • Numerical solution of the Klein Gordon equation on Kraken

(a retired Cray supercomputer formerly at the National Institute for Computational Sciences). A Fourier spectral discretization with 5123 modes is used.

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SLIDE 21

Time for IO

101 102 103 Number of Cores 100 101 102 103 Computation Time (s)

No output Images Output Ideal

  • Numerical solution of the Klein Gordon equation on
  • Beacon. A Fourier spectral discretization with 5123 modes

is used.

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SLIDE 22

Time for IO

10

1

10

2

10

3

10

4

10 10

1

10

2

10

3

Number of cores time in s Scaling on VSC2 COPROCESSING ideal NO OUTPUT ideal REGULAR OUTPUT ideal

  • Numerical solution of the Klein Gordon equation on Vienna

Scientific Cluster 2. A Fourier spectral discretization with 5123 modes is used. Results obtained by M. Quell

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SLIDE 23

Time for IO

  • Visualization workflow on K computer. A. Ogasa, H.

Maesaka, K. Sakamoto, S. Otagiri “Visualization Technology for the K computer” Fujitsu Sci. Tech. J. Vol. 48 No. 3 pp. 348-356 (July 2012)

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SLIDE 24

Time for IO

  • Visualization workflow on K computer. A. Ogasa, H.

Maesaka, K. Sakamoto, S. Otagiri “Visualization Technology for the K computer” Fujitsu Sci. Tech. J. Vol. 48 No. 3 pp. 348-356 (July 2012)

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SLIDE 25

Lattice Boltzmann - Visualization

  • http://optilb.com/openlb/
  • Paraview Demo
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SLIDE 26

Challenges

  • Large data sets, 10, 0003 points on large supercomputer
  • Computation time can be at a premium
  • Do not want to do input and output
  • Want to do insitu visualization
  • Want to be able to find areas of interest automatically -

time for human interaction can be slow.

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SLIDE 27

Challenges

  • Many scientists learn programming because they need to,

not because they want to

  • Scientific data visualization has generally required a factor
  • f 10-100 less computational resources
  • For moderate simulations on a remote cluster, one can

move the data to a local workstation

  • For large data sets, this becomes infeasible and many

centers have a separate visualization cluster

  • For peta and exascale machines, it is not always possible

to have a separate visualization cluster

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SLIDE 28

Challenges

  • Changes in parallel computer architectures
  • Many are now heterogeneous, CPU + accelerator
  • Many accelerators are GPUs or have architecture that

suits GPU programming models

  • A good opportunity for computer graphics programmers
  • For peta and exascale machines, it is not always possible

to have a separate visualization cluster

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SLIDE 29

VTK

  • Visualization Toolkit
  • Open source library of graphics primitives
  • Community involvement
  • Parallel support
  • Aiming to also have GPU support
  • http://www.vtk.org
  • Used on its own and in other visualization tools such as

ParaView and VisIt

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SLIDE 30

DAX toolkit

  • Visualization toolkit to give multithreaded parallelizm a high

level abstraction

  • Main concept is to have worklets that are independent

small codes acting on local data that can be scheduled in multicore environment

  • Open source library of graphics primitives
  • Community involvement
  • CUDA, OpenMP and Intel TBB backends
  • Rendering support through OpenGL
  • Uses finite differences to compute gradients
  • Marching cubes algorithm to calculate isosurfaces is

implemented

  • http://www.daxtoolkit.org/
  • Used on its own and in other visualization tools such as

ParaView and VisIt

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SLIDE 31

PISTON

  • Visualization toolkit to give multithreaded parallelizm a high

level abstraction

  • Open source library of data parallel graphics primitives
  • Community involvement
  • Uses NVIDIA’s thrust library
  • CUDA and OpenMP backends
  • Some OpenCL support
  • https://datascience.lanl.gov/PISTON.html
  • Used on its own and in other visualization tools such as

ParaView and VisIt

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SLIDE 32

CINEMA

  • Insitu analysis tool
  • Open source tool
  • Community involvement
  • Create a reduced size data set at runtime which can then

be explored in post processing

  • Use database of images collected from multiple angles to

allow for interactive exploration at runtime

  • Can one use artificial intelligence methods?
  • https://datascience.lanl.gov/Cinema.html
  • Use other visualization tools such as ParaView, then links

them together with a carefully constructed database

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SLIDE 33

Open Areas

  • Programming models: OpenCL? CUDA? OpenGL? C?

C++? MPI? PGAS?

  • How generate graphics primitive libraries that will run on a

variety of architectures?

  • How show uncertainty in scientific visualization?
  • Is separation of domains still feasible? Will scientists need

to learn about computer graphics?

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SLIDE 34

Acknowledgments

  • KAUST Visualization laboratory
  • XSEDE Extended Collaborative Support Service
  • Kraken at the National Institute for Computational Sciences

through the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation grant number ACI-1053575

  • Vienna Scientific Cluster 2
  • KAUST Supercomputing Laboratory
  • The Beacon Project at the University of Tennessee

supported by the National Science Foundation under Grant Number 1137097;

  • Kitware
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SLIDE 35

References

  • Moreland “A Pervasive Parallel Framework for

Visualization: Final Report for FWP 10-014707.” Tech Report SAND 2014-0047, Sandia National Laboratories, January 2014.

  • https://en.wikibooks.org/wiki/Parallel_

Spectral_Numerical_Methods

  • Bethel, Childs and Hansen “High performance

visualization” CRC Press (2010)

  • Data Science https://datascience.lanl.gov/
  • Dorier, Sisneros, Petreka, Antoniu, Semeraro “A

nonintrusive, adaptable and user-friendly insitu visualization framework” HAL-INRIA pre-print 00831265 http://hal.inria.fr/hal-00831265

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SLIDE 36

References

  • Fabian, Moreland, Thompson, Bauer, Marion, Geveci,

Rasquin, Jansen, “The ParaView coprocessing library: a scalable, general purpose in-situ visualization library.” LDAV 2011 IEEE Symposium on Large-Scale Data Analysis and Visualization, (Oct. 2011), 89-96. DOI=http: //doi.acm.org/10.1109/LDAV.2011.6092322.

  • Ka˘

ceniauskas, Pacevi˘ c, Bugajev, “Efficient visualization by using ParaView software on Balticgrid” Information Technology and Control, 39(2):108-115 (2010)

  • Lo, Sewell, Aherns, “PISTON: A Portable Cross-Platform

Framework for Data-Parallel Visualization Operators” EGPGV pp. 11-20 (2012).

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SLIDE 37

References

  • Ogasa, Maesaka, Sakamoto, Otagiri “Visualization

Technology for the K computer” Fujitsu Sci. Tech. J. Vol. 48

  • No. 3 pp. 348-356 (July 2012) http://www.fujitsu.

com/downloads/MAG/vol48-3/paper14.pdf

  • ParaView http://www.paraview.org/
  • PlanetOS https://planetos.com/
  • Quell, “Performance of a distributed three dimensional

Coprocessing code for the Klein Gordon equation” Poster presentation (2014)

  • Shirley, Marschner “Fundamentals of Computer Graphics”

CRC Press (2010)

  • VisIt https://wci.llnl.gov/simulation/

computer-codes/visit/

  • Weiskopf “GPU-based interactive visualization techniques”

Springer (2007)