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High Performance Computing in GG Strengths, Challenges, Future Directions • Processing/inverting increasingly large datasets for -imaging crustal & mantle structure (Dunn, Moore, Wolfe) -locating/characterizing acoustic sources (Frazer, Duennebier ) -analyzing global altimetry/topography (Wessel) •Simulating dynamical processes in expanding volumes, at finer resolution, & with stronger heterogeneity: -dynamics of mantle & lithosphere (Ito, Conrad- probably) -crustal deformation & faulting (Martel) -watershed processes & contaminant transport (El- Kadi)

High Performance Computing in GG Strengths, Challenges, Future Directions Processing/inverting increasingly large datasets for -imaging crustal & mantle

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Page 1: High Performance Computing in GG Strengths, Challenges, Future Directions Processing/inverting increasingly large datasets for -imaging crustal & mantle

High Performance Computing in GGStrengths, Challenges, Future Directions

High Performance Computing in GGStrengths, Challenges, Future Directions

• Processing/inverting increasingly large datasets for -imaging crustal & mantle structure (Dunn, Moore, Wolfe)-locating/characterizing acoustic sources (Frazer, Duennebier ) -analyzing global altimetry/topography (Wessel)

• Processing/inverting increasingly large datasets for -imaging crustal & mantle structure (Dunn, Moore, Wolfe)-locating/characterizing acoustic sources (Frazer, Duennebier ) -analyzing global altimetry/topography (Wessel)

• Simulating dynamical processes in expanding volumes, at finer resolution, & with stronger heterogeneity:-dynamics of mantle & lithosphere (Ito, Conrad-probably)-crustal deformation & faulting (Martel)-watershed processes & contaminant transport (El-Kadi)

• Simulating dynamical processes in expanding volumes, at finer resolution, & with stronger heterogeneity:-dynamics of mantle & lithosphere (Ito, Conrad-probably)-crustal deformation & faulting (Martel)-watershed processes & contaminant transport (El-Kadi)

Page 2: High Performance Computing in GG Strengths, Challenges, Future Directions Processing/inverting increasingly large datasets for -imaging crustal & mantle

Crustal structure beneath mid-ocean ridges: Dunn & Conley Crustal structure beneath mid-ocean ridges: Dunn & Conley

Page 3: High Performance Computing in GG Strengths, Challenges, Future Directions Processing/inverting increasingly large datasets for -imaging crustal & mantle

Finding seamounts with global satellite altimetry data: Wessel & KimFinding seamounts with global satellite altimetry data: Wessel & Kim

Page 4: High Performance Computing in GG Strengths, Challenges, Future Directions Processing/inverting increasingly large datasets for -imaging crustal & mantle

Mechanics of fissures on Big IslandMartel & Langley, Kaven Mechanics of fissures on Big IslandMartel & Langley, Kaven

Page 5: High Performance Computing in GG Strengths, Challenges, Future Directions Processing/inverting increasingly large datasets for -imaging crustal & mantle

Dynamics of Mantle & LithosphereIto & Bianco, Mittelstaedt Dynamics of Mantle & LithosphereIto & Bianco, Mittelstaedt

60million tracers of composition,3 million elements,60million tracers of composition,3 million elements,

adequate thermodynamic model of melting adequate thermodynamic model of melting Visco-elastic/elastic-plastic rheology.Visco-elastic/elastic-plastic rheology.

30-40 million timesteps for 1.3 Myr30-40 million timesteps for 1.3 Myr

Page 6: High Performance Computing in GG Strengths, Challenges, Future Directions Processing/inverting increasingly large datasets for -imaging crustal & mantle

Dynamics of Mantle & LithosphereIto & Bianco, Mittelstaedt Dynamics of Mantle & LithosphereIto & Bianco, Mittelstaedt

60million tracers of composition,3 million elements,60million tracers of composition,3 million elements,

adequate thermodynamic model of meltingadequate thermodynamic model of melting

Visco-elastic/elastic-plastic rheology.Visco-elastic/elastic-plastic rheology.

30-40 million timesteps for 1.3 Myr30-40 million timesteps for 1.3 Myr

Page 7: High Performance Computing in GG Strengths, Challenges, Future Directions Processing/inverting increasingly large datasets for -imaging crustal & mantle

Watershed processes & contaminant transport El-Kadi & Cutler, Corley, Hughes, Lamptoc, Perrault, Rotzoll, Whittier

Watershed processes & contaminant transport El-Kadi & Cutler, Corley, Hughes, Lamptoc, Perrault, Rotzoll, Whittier

Manoa Food Area Manoa Food Area Hydrology Problems Involve:-Strong heterogeneity

-Non-linearity of coupled equations (e.g., contamination by petroleum products)

-Extreme conditions (rainfall events, topography, etc)

-Large range of scales (point-measurements to watershed)

-Numerical solutions with fine (<meter) spatial & time resolution

Hydrology Problems Involve:-Strong heterogeneity

-Non-linearity of coupled equations (e.g., contamination by petroleum products)

-Extreme conditions (rainfall events, topography, etc)

-Large range of scales (point-measurements to watershed)

-Numerical solutions with fine (<meter) spatial & time resolution

Oahu water sources Oahu water sources

Page 8: High Performance Computing in GG Strengths, Challenges, Future Directions Processing/inverting increasingly large datasets for -imaging crustal & mantle

Community Code Development in Geodynamics

SOEST is a “member institution” of:

Community Code Development in Geodynamics

SOEST is a “member institution” of:

Seismic WavePropagation

Lithosphere dynamics

Core dynamics & geodynamo

Mantle Convection

Page 9: High Performance Computing in GG Strengths, Challenges, Future Directions Processing/inverting increasingly large datasets for -imaging crustal & mantle

Use of Resources of the Maui High Performance Computer Center (MHPCC) Use of Resources of the Maui High Performance Computer Center (MHPCC)

UH High Performance Computing Student Engagement Grants ($10-20K)

GG AwardeesE. Nosal 2004 (Frazer)T. Fedenczuk 2006 (Fryer)T. Bianco 2006 (Ito)S.-S. Kim 2007 (Wessel)

Page 10: High Performance Computing in GG Strengths, Challenges, Future Directions Processing/inverting increasingly large datasets for -imaging crustal & mantle

AKUA: 32-nodes, 64 Xeon 2.2-2.8 GHz processors, ~100 GB RAM,4.5 TB disk space, Gigabit-interconnect

AKUA: 32-nodes, 64 Xeon 2.2-2.8 GHz processors, ~100 GB RAM,4.5 TB disk space, Gigabit-interconnect

Purchased with cost-match SOEST & NSF OCE-0136793 ($109K)

Purchased with cost-match SOEST & NSF OCE-0136793 ($109K)

Maintenance, upgrades, consulting, software installation:

Maintenance, upgrades, consulting, software installation:

GT High-Performance Computing Facilities GT High-Performance Computing Facilities