J.-N. Leboeuf V.K. Decyk R.E. Waltz J. Candy W. Dorland Z. Lin S. Parker Y. Chen W.M. Nevins B.I. Cohen A.M. Dimits D. Shumaker W.W. Lee S. Ethier J. Lewandowski W. Wang

The Plasma Microturbulence Project Our Goal: Understanding plasma microturbulence through direct numerical simulation Plasma microturbulence is a critical issue to magnetic fusion program –Controls energy confinement  Determines size and cost of a burning plasma experiment Direct numerical Simulation is the right tool to study microturbulence –Far better diagnostics than those available on experiments –Excellent resolution can be achieved on existing computers Our Game plan: Code Development –Enhanced Fidelity –Increased Efficiency –Common data analysis & visualization system Code Validation –Against each other’s codes –Against experiment & theory Expanding our user community –Web-based applications –Collaborations with theory and experimental communities

Plasma Microturbulence is an Interesting Scientific Problem Quasi-2-D turbulence in a 3-D toroidal geometry exhibits inverse cascades and other features of 2-D turbulent systems

Largest runs with ‘GTC’ code required 1 billion particles and 125 million grid points using 1024 processors on the IBM-SP at NERSC Plasma Microturbulence Simulations Require State-of-the-Art Computers Scaling of Plasma Microturbulence with System Size

Enhanced Code Fidelity Physics relevant for plasma confinement: –Ion-scale Physics –Electron Dynamics –Turbulent Electric & Magnetic Field –Realistic Geometry & Plasma Size Accurate implementation demands: –Excellent spatial resolution –High-order time integration –Advanced data-management and visualization dealing with large data sets Simulation Data from GYRO Code

Code Efficiency is a Critical Issue Scaling of Particle-in- Cell Code (‘GTC’) Plasma Microturbulence Project Codes: –Are largest users of computer cycles within the Fusion Energy Sciences Program –PMP codes Scale ~ Linearly with Number of Processors –Production code efficiencies of 10-20% are achieved  Actively working with SciDAC Performance Evaluation Research Center to improve code efficiency

Advanced Visualization and Data Analysis Challenges Terabytes of data are now generated at remote location (Data Management, Data Grid technologies) Advanced visualization techniques needed to help identify key features in the data (Parallel Visualization) Data must be efficiently analyzed to compute derived quantities 121 Million grid points Temperature Time Particle in Cell Turbulence Simulation Heat Potential

Data Management, Data Analysis & Visualization Terabytes/simulation is Data management issue –Interactions with SciDAC Fusion Collaboratory Data must be analyzed –To validate codes –To gain insight Interactive data analysis –New insights from analyzing data in new ways Visualization of analyzed data –Multi-dimensional data sets –Efficient means of communicating between computers to people Turbulent Fluctuation Spectrum