green osg slac applications meeting 06 01 05

GRASE VO Science and Engineering Applicationson Open Science Grid: 

Mark L. Green, Ph.D. Grid Computational Scientist, CCR GRASE VO Science and Engineering Applications on Open Science Grid

Outline: 

Outline Center for Computational Research Advanced Computational Data Center Grid (ACDC-Grid) System Architecture Molecular Structure Determination Quantum Chemistry Earthquake Engineering Princeton Ocean Model and Biohazards Geophysical Mass Flows Numerical Methods ACDC Grid Dashboards Outline

Major Compute/Storage Resources : 

SGI Altix3700 (0.4TF peak) 64 Processors (1.3GHz ITF2) 256 GB RAM 2.5 TB Disk Apex Bioinformatics System Sun V880 (3), Sun 6800 Sun 280R (2) Intel PIIIs Sun 3960: 7 TB Disk Storage HP/Compaq SAN 75 TB Disk; 190 TB Tape 64 Alpha Processors (400 MHz) 32 GB RAM; 400 GB Disk Major Compute/Storage Resources Dell Linux Cluster (10TF peak) 1600 Xeon EM64T Processors (3.2 GHz) 2 TB RAM; 65 TB Disk Myrinet / Force10 30 TB EMC SAN Dell Linux Cluster (2.9TF peak) 600 P4 Processors (2.4 GHz) 600 GB RAM; 40 TB Disk; Myrinet Dell Linux Cluster (6TF peak) 4036 Processors (PIII 1.2 GHz) 2TB RAM; 160TB Disk; 16TB SAN IBM BladeCenter Cluster (3TF peak) 532 P4 Processors (2.8 GHz) 5TB SAN SGI Intel Linux Cluster (0.1TF peak) 150 PIII Processors (1 GHz) Myrinet

ACDC-Grid System Architecture: 

ACDC-Grid System Architecture Campus Grid Infrastructure Data Grid Infrastructure Grid Portal Applications and Administration Grid Collaborations

ACDC-Grid System Architecture: 

ACDC-Grid System Architecture Grid Collaborations

Grid-enabled Data Mining Infrastructure: 

Grid Portal Workflow Job Manager ACDC-Grid Computational Resources Molecular Structure Database Grid-enabled Data Mining Infrastructure

Grid-enabling Application Template (GAT): 

Grid-enabling Application Template (GAT)

Grid-enabling Application Template (GAT): 

Grid-enabling Application Template (GAT)

ACDC Data Grid Database Schema: 

ACDC Data Grid Database Schema ACDC-Grid Data Grid

SnB GAT: 

SnB GAT SnB is a computer program based on Shake-and-Bake where: A dual-space direct-methods procedure for determining molecular crystal structures from X-ray diffraction data is used. As many as 2000 unique non-H atom difficult molecular structures have been solved in a routine fashion. SnB has been routinely applied to jump-start the solution of large proteins, increasing the number of selenium atoms determined in Se-Met molecules from dozens to several hundred. SnB is expected to play a vital role in the study of ribosomes and large macromolecular assemblies containing many different protein molecules and hundreds of heavy-atom sites.

SnB GAT: 

SnB GAT

SnB GAT: 

SnB GAT

SnB GAT: 

SnB GAT

SnB GAT: 

SnB GAT

Slide15: 

Quantum Chemistry Software (Q-Chem) ab initio electronic structure program capable of performing first principles calculations on both the ground and excited states of molecules the Q-Chem program exploits the latest developments in computer science, having adopted an Object Oriented approach to program design allows developers to rapidly implement new methodologies with ease and reduce program code redundancy Q-Chem GAT

Slide16: 

Features Ground State Self-Consistent Field Methods Hartree-Fock Methods | Density Functional Theory | Linear Scaling Methods | AOINTS Package for Two Electron Integrals | SCF Improvement Features Wave Function Based Treatments of Electron Correlation Møller-Plesset Perturbation Theory | Local MP2 Methods | Coupled Cluster Methods | Optimized Orbital Coupled-Cluster Methods Excited State Methods Supported Calculation Types | CIS Methods | Time-Dependent DFT | Coupled-Cluster Excited State Methods | Attachment-Detachment Analysis Properties Analysis Automated Geometry and Transition Structure Optimization | Vibrational Spectroscopy | NMR Shielding Tensors | Natural Bond Orbital Analysis | Stewart Atoms | Momentum Densities | Intracules | Atoms in Molecules | Solvation Modeling | Relativistic Energy Corrections | Diagonal Adiabatic Correction Basis Sets Gaussian Basis Sets | Pseudopotential Basis Sets Correction for Basis Set Superposition Error Q-Chem GAT

Slide17: 

Q-Chem GAT

Slide18: 

Q-Chem GAT

Slide19: 

Q-Chem GAT

Earthquake Engineering: 

Earthquake Engineering Evolutionary Aseismic Design andamp; Retrofit (EADR) In our effort to develop disaster-resilient communities, there is a need to model, understand, and ultimately direct the behavior of a wide variety of complex multi-scale systems, including the many engineering systems that shape our physical environment. Two aspects of the structural system needs: a multi-scale evaluation tools of progressive collapse of structures and use of such evaluation tools in a new general framework for aseismic design and retrofit, based upon evolutionary methodologies. EADR GAT

Slide21: 

Multidisciplinary Center for Earthquake Engineering (MCEER) MCEER is a national center of excellence that applies knowledge and advanced technologies to reduce earthquake losses. Network for Earthquake Engineering Simulation (NEES) NEES is a national, networked, simulation resource that includes geographically-distributed, shared-use, next-generation experimental equipment sites. Department of Civil, Structural, and Environmental Engineering (CSEE) CSEE faculty support eight research centers, programs, institutes, and laboratories providing synergistic collaborative activities. EADR GAT

Earthquake Engineering: 

Earthquake Engineering EADR GAT

Slide23: 

EADR GAT

Slide24: 

EADR GAT

POM GAT: 

POM GAT Monitoring and Event Response for Harmful Algal Blooms Development of a transport model capable of predicting the movement of harmful algal blooms in a lake is currently underway. Developed for each of the three main lakes in the Monitoring and Event Response for Harmful Algal Blooms (MERHAB) study: Ontario, Erie and Champlain. To accomplish the goal of predicting bloom movement, it is necessary to maintain a near real-time database for water velocity fields in the lakes and to provide short-term predictions of lake circulations. This requires a combination of hydrodynamic and transport modeling, along with linkages to various data sources, including regional weather stations, water monitoring stations, and satellite data.

POM GAT: 

POM GAT

POM GAT: 

Assessment of the Regional Great Lakes Watershed POM GAT

POM GAT: 

POM GAT

POM GAT: 

POM GAT

Geophysical Mass Flows: 

Geophysical Mass Flows Titan2d Mass-Flow Model The risk of potential volcanic eruptions and associated mass flows is a problem that public safety authorities throughout the world face several times a year. Flow models are useful to forecast the movement of volcanic materials on or above the surface. Applications of such models include: pre-crisis understanding of hazards and developing risk maps, real-time crisis assistance and management and post-crisis reconstruction and distribution of aid. Titan GAT

Geophysical Mass Flows: 

Geophysical Mass Flows Titan GAT

Geophysical Mass Flows: 

Geophysical Mass Flows Titan GAT

Geophysical Mass Flows: 

Geophysical Mass Flows Titan GAT

Slide34: 

Geophysical Mass Flows

OSTRICH GAT: 

OSTRICH GAT Ostrich: Optimization Software Tool OSTRICH implements numerous model-independent optimization and calibration (parameter estimation) algorithms including: Particle Swarm Optimization, Genetic Algorithm, Simulated Annealing, Levenberg-Marquardt least-squares regression and several zero- and 1st-order numerical techniques. OSTRICH can be used for optimizing many of the existing ACDC-Grid grid-enabled applications.

OSTRICH GAT: 

OSTRICH GAT

OSTRICH GAT: 

OSTRICH GAT

OSTRICH GAT: 

OSTRICH GAT

OSTRICH GAT: 

OSTRICH GAT

ACDC Grid Dashboards: 

ACDC Grid Dashboards

Acknowledgments: 

Acknowledgments GAT Team Leaders Russ Miller (SnB) Tom Furlani (Q-Chem) Gary Dargush (EADR) Joseph Atkinson (POM) Abani Patra (Titan) Alan Rabideau (OSTRICH) ACDC-Grid Portal Steve Gallo Jon Bednasz ACDC-Grid Research Naimesh Shah Cathy Ruby Amin Ghadersohi Center Personnel Jason Rappleye Sam Guercio Tony Kew Dori Macchioni Martins Innus Adam Koniak Collaborators Charles Weeks Dave Pape Bill Furey Steve Potter NSF, NIH, NYS, DOE