PEER 2003 Meeting 03/08/031 Interdisciplinary Framework Major focus areas Structural Representation Fault Systems Earthquake Source Physics Ground Motions Seismic Hazard Analysis Implementation interface Risk Assessment & Mitigation (e.g. PEER)

When Producing Broad Impact Computational Data Products, Forecast Testing Should Increase Along with Forecast Impact Public and Governmental Forecasts Engineering and Interdisciplinary Research Collaborative Research Project Individual Research Project Computational codes, structural models, and simulation results versioned with associated tests. Development of new computational, data, and physical models. Automated retrospective testing of forecast models using community defined validation problems. Automated prospective testing of forecast models over time within collaborative forecast testing center. SCEC Computational Forecast Users Scientific and Engineering Requirements for Forecast Modeling Systems

Productive Open-Science Research SCEC bridges research (NSF) and operational (USGS,DOE) organizations. –SCEC Mission is to translate latest research results into use with public impact. Apply best techniques to established seismic hazard data calculations. The CME bridges the scientific and computer scientists within SCEC. –CME provides an organization that integrates existing SCEC scientific codes for research purposes. Best and most qualified codes within SCEC are adopted and highly optimized.CME simulations at scales beyond individual investigtors. Develops techniques for improving accuracy and precision of existing widely used, high impact calculations including PSHA, siesmograms etc

Computational Science Broader Impact Seismology has several existing information interfaces to broad impact users. Each interface represents a transfer of specific computational data products between groups. Each type of computation requires specialized techniques. –Probabilistic Seismic Hazard Curves : Forecast of peak ground motion at site over years : Building code development –Scenario Earthquake Seismograms : Forecast ground motions up to 10Hz at any site for arbitrary earthquake : Building engineers. –Earthquake Early Warning : Ground Motion Alert : Public and Work Place –Earthquake ShakeMaps : Geographic Distribution and levels of Peak Shaking : Public and Press

Need for Balanced Software System HPC systems seek a balance between compute, communications, and input/output to maximize performance and effectiveness. Performance improvements in one area move performance bottlenecks around system. Similarly, research results are obtained using simulation software systems (computational platforms), well integrated suites of code. As simulations scale up, all simulation software system elements must advance together.

Translating Performance into Production At milestone scale, every simulation is custom hand crafted. This is unavoidable to start, but problematic in the long run. Once milestone simulation is achieved, we drive performance improvements into routine, automated, production computing.

Productive Open-Science Research In our experience, when working with highly-talented and highly- motivated basic research groups, computational grand challenges can be highly productive open-science basic research. Challenges designed to produce: –Significant new scientific result –Significant new cyber-infrastructure that enables you to repeat the calculation. Key is this approach is to select right grand challenge, one that drives both broad impact science and the computational capabilities most needed. This talk will describe why M8 was the right grand challenge. And offer suggestions on what the next right grand challenge will be.

SCEC/CME Annual Allocation Targets 1.USC HPCC –Linux Cluster (some large memory nodes) and 6 GPU Nodes 2.NSF XSEDE –Linux Clusters, Shared Memory Computers, GPU nodes 3.DOE INCITE –Leadership-class Cray Supercomputers some with GPU nodes 4.Blue Waters –NSF Track 1 Cray cluster with GPU nodes accessible in Jan 2013

Typical Elements in Allocation Request 1.PI and co-PIs: –Add users, transfer units etc. 2.Research Objectives and Potential Impact: –Goals of research for non specialists and potential impact 3.Supporting Grants –List of grants funding proposed research 4.Status of current allocation and current results –Hours used and current results from current allocation? 5.Selection of Appropriate Computer Resources –Specify computing resources you wish to use 6.List of Software To Be Used with Performance Information –Describe software implementation and performance benchmarks 7.List of Simulations with CPU usage and Data Storage –Total of all simulations to determine total request on each machine 8.Project Milestones and Schedule –Progress reports must describe status of milestones

Allocation Responsibilities 1.Register PI, co-Pi, and Users: –Some systems like DOE have stricter registration process 2.Must learn and abide by Supercomputer Terms of Use: –Don’t share accts, don’t run jobs on the head-node etc 3.Receive and use physical crypto-key –Larger computing systems require physical password devices 4.Monitor allocation usage and burn rate –Monitoring reports and tools differ by systems 5.Keep project data storage within quotas –Large data requires close coordination with Resource Providers 6.Submit quarterly progress reports –Most Programs require quarterly reports tracking progress 7.Science results for non-scientists with images –Resource Providers want to show results from their systems

Conclusions Numerical simulations of large earthquakes have now advanced to the point where they can usefully predict the strong ground motions from anticipated earthquake sources –M8 simulation demonstrates the capability for full-scale simulations –Computational tools now facilitate verification, validation, and data assimilation Simulations can enhance the technologies for time-dependent seismic hazard analysis –Long-term probabilistic seismic hazard analysis –Operational earthquake forecasting –Earthquake and tsunami early warning –Post-earthquake information These applications offer new (and urgent) computational challenges –Exascale problems –Data-intensive computing –Rapid access to very large data sets –Robust on-demand computing