VAPOR: A desktop environment for interac7ve explora7on of large scale CFD simula7on data John Clyne , Alan Norton Na7onal Center for Atmospheric Research Boulder, CO USA This work is funded in part through U.S. Na7onal Science Founda7on grants 0325934 and 0906379, and through a TeraGrid GIG award. Computa7onal and Informa7on Systems Laboratory Na7onal Center for Atmospheric Research 9/23/2011 FRHPCS Sept. 23, 2011
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VAPOR: A desktop environment for interac7ve explora7on of large scale CFD simula7on data
John Clyne, Alan Norton Na7onal Center for Atmospheric Research
Boulder, CO USA
This work is funded in part through U.S. Na7onal Science Founda7on grants 03-‐25934 and 09-‐06379, and through a TeraGrid GIG award.
Computa7onal and Informa7on Systems Laboratory Na7onal Center for Atmospheric Research
9/23/2011
FRHPCS Sept. 23, 2011
Computational and Information Systems Laboratory National Center for Atmospheric Research
9/23/2011
Outline
• Problem mo7va7on • VAPOR overview – what makes VAPOR unique?
– Data model – Earth and space sciences focus – Analysis capabili7es
• Laptop demonstra7on • Future direc7ons
Computational and Information Systems Laboratory National Center for Atmospheric Research
9/23/2011
Solar thermal star7ng plume Computed at the dawn of terascale compu7ng
– Proper7es • Mul7resolu7on representa7on • Efficient: Linear 7me complexity • Adaptable: Can represent func7ons with discon7nui7es, bounded domains,
and arbitrary topology • Time frequency localiza7on: Many coefficients are zero or close to zero
€
f t( ) =1N
atej 2πnt N
n= 0
N−1
∑ 0 ≤ t ≤ N −1( )
€
f t( ) = c k( )k∑ φk t( ) + d j k( )
j= 0
log2 N
∑k∑ ψ j,k t( )
€
φ t( ) = hφ k( )k∑ 2φ 2t − k( ), k ∈ Z scaling function
€
ψ t( ) = hψ k( )k∑ 2φ 2t − k( ), k ∈ Z wavelet function
Scaling term (coarse representa7on of signal)
Detail term (high frequency components of signal)
Computa7onal and Informa7on Systems Laboratory Na7onal Center for Atmospheric Research
9/23/2011
Wavelet compression and progressive access (VDC1) Frequency trunca3on method
• Truncate “j” parameter of expansion: • Provides coarsened approxima7on at power-‐of-‐two
increments • Good
– Simple – Fast – Maintains structure of original grid
• Bad: – Limited to power-‐of-‐two reduc7ons – Compression quality
€
f t( ) = c k( )k∑ φk t( ) + d j k( )
j= 0
log2 N
∑k∑ ψ j,k t( )
Computa7onal and Informa7on Systems Laboratory Na7onal Center for Atmospheric Research
9/23/2011
• Goal: priori7ze coefficients used in linear expansion
€
f t( ) = anu t( )n= 0
N−1
∑ , original f (t)
€
ˆ f t( ) = amu t( )m= 0
M −1
∑ , M < N( ), compressed f (t)
€
L2 = f (t) − ˆ f (t)2
2
€
L2 = aπ i( )( )2
=2
2
f t( )− ˆ f t( ) , where aπ i( )i= M
N−1
∑ are discarded coefficients
If u(t) (φ(t) and ψ(t) in case of wavelet expansion func7ons) are orthonormal, then
• The error is the sum of the squares of the coefficients we leave out! • So to minimize the L2 error, we simply discard (or delay transfer) the smallest coefficients! • If discarded coefficients are zero, there is no informa7on loss!
6x6x1(deep) constant heat flux into the bolom, constant temperature on top
Computa7onal and Informa7on Systems Laboratory Na7onal Center for Atmospheric Research
9/23/2011
Computa7onal and Informa7on Systems Laboratory Na7onal Center for Atmospheric Research
9/23/2011
Future direc7ons
• Broadening scien7fic end user community – E.g. Weather researchers, ocean modelers
• Geo-‐referenced 2D & 3D data • Stretched grids • Missing & undefined data
• VAPOR progressive access data model – VAPOR data importers for VisIt and ParaView – Fortran-‐callable, distributed memory (MPI) API
• Extensible architecture – Facilitate 3rd party development
Computa7onal and Informa7on Systems Laboratory Na7onal Center for Atmospheric Research
9/23/2011
VAPOR Summary • Progressive data access
– Enables interac7ve explora7on of massive datasets – Hypothesis may be interac3vely explored with coarsened
data and later validated (perhaps non-‐interac3vely) with na3ve data
• Visualiza7on aided data analysis – Intended to be used by scien7sts, not visualiza7on specialist – Requirements defined by a steering commilee of scien7sts
• Narrow focus: Earth & space CFD simula7ons – Algorithms – Data types
• Emphasis on desktop/laptop pla}orms, not on visualiza7on supercomputers
Computational and Information Systems Laboratory National Center for Atmospheric Research
9/23/2011
Acknowledgements • Steering Commilee
– Benjamin Brown – U. of Wisconsin – Nic Brummell – CU – Gerry Creager – Texas A&M – Yuhong Fan -‐ NCAR, HAO – Aimé Fournier – NCAR, IMAGe – Pablo Mininni -‐ NCAR, IMAGe – Aake Nordlund -‐ University of
Copenhagen – Leigh Orf -‐ Central Michigan U. – Yannick Ponty -‐ Observatoire de la
Cote d'Azur – Thara Prabhakaran -‐ U. of Georgia – Annick Pouquet -‐ NCAR, ESSL – Mark Rast -‐ CU – Duane Rosenberg -‐ NCAR, IMAGe – Malhias Rempel -‐ NCAR, HAO – Geoff Vasil, CU
• Developers – John Clyne – NCAR, CISL – Dan Lagreca – NCAR, CISL – Alan Norton – NCAR, CISL – Kenny Gruchalla – NREL – Victor Snyder – CSM – Kendal Southwick – NCAR, CISL
• 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, CISL – Pam Gilman -‐ NCAR, CISL
Computational and Information Systems Laboratory National Center for Atmospheric Research