MFE Simulation Data Management SLAC DMW 2004

March 16, 2004 W. W. Lee and S. Klasky Princeton Plasma Physics Laboratory Princeton, NJ

Spatial & Temporal Scales Present Major Challenge to Theory & Simulations

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Huge range of spatial and temporal scales.

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Overlap in scales often means strong (simplified) ordering not possible

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Different codes/theory for different scales.

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5+years: Integration of physics into Fusion Simulation Project

atomic mfp skin depth electron-ion mfp system size tearing length ion gyroradius Debye length electron gyroradius Spatial Scales (m) 10 -6 10 -4 10 -2 10 0 Inverse ion plasma frequency pulse length current diffusion inverse electron plasma frequency confinement ion gyroperiod electron gyroperiod Ion collision electron collision 10 2 10 -10 10 -5 10 0 Temporal Scales (s) 10 5

Major Fusion Codes

Code

GTC

Gyro GS2 Degas2 Transp Data Rates of Major Fusion Codes (GB) now / 5yr 4,000 / 100,000 Runtime now/5yr (hr) 300/150 Processors Now/5yr 2048 Mbs Now/5yr 80/ 1600 10 / 100 10 / 100 .1

.05

30/30 30/30 1 3 512/2048 512/2048 10 1 .8/ 8 .8 / 8 .2

.04

5/ 50 20/20 128 .6/ 6 Nimrod M3D NSTX

Total (TB)

10 / 100 .25/shot 1/ 4

4.3 / 101

20/20 0.25 * 40 128 1.1/ 11 9, 36

Plasma Turbulence Simulation

• Gyrokinetic Particle-In-Cell Simulation -- Reduced Vlasov-Maxwell Equations • Simulations on MPP Platforms -- Cray T3E & IBM SP (NERSC), Cray-X1 (ORNL), SX6 (Earth Simulator, Japan) • Simulation of Burning Plasmas -- International Tokamak Experimental Reactor (ITER) • Integrated Fusion Simulation Project (MFE) • Visualization -- turbulence evolution & particle orbits

Gyrokinetic Approximation

• Gyromotion • Polarization provides quasineutrality [W. W. Lee, PF ‘83; JCP ‘87]

Earth Simulator 18% 10 (Ethier)

Ion Temperature Gradient Driven Turbulence QuickTime™ and a Video decompressor are needed to see this picture.

QuickTime™ and a Cinepak decompressor are needed to see this picture.

Electrostatic Potential Particle Trajectories

Data Management challenges

• GTC is producing TBs of data – Data rates: 80Mbs now, 1.6Gbs 5 years.

– Need QOS to stream data.

• This data needs to be post-processed – Essential to parallelize the post-processing routines to handle our larger datasets.

– We need a cluster to post process this data.

• M (supercomputer processors) x N (cluster processors) problem.

• QOS becomes more important to sustain this post-processing.

• The post-processed data needs to be shared among collaborators – Different sections of the post-processed data may go to different users .

– Post-processed data, along with other metadata should be archived into a relational database.

Post processing of GTC Data.

• Particle Data – No compression possible.

– Sent to 1 cluster for visualization/analysis.

– Work being done with K. Ma, U.C. Davis: Visualize a million particles.

– Gain new insights into the theory. • Field Data – Geometric/Temporal compression of the data is possible.

– Data needs to be streamed to a local cluster at PPPL.

– Reduced subset needs to be sent to PPPL + collaborators. • Use Logistic Network. [Beck, UT-K] • Data transfer needs to be automatic, and integrated into a dataflow/webflow for use with parallel analysis routines.

– We desire to see post-processed data during the simulation.

After the analysis

• Post-processed data needs to be saved into a relational database – How do we query this abstract data to compare it with experiments?

– 3D correlation functions – Processing of TBs of data/run now, 100’s of TBs of data/run in 5 years.

– Data mining techniques will be necessary to understand this data.