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MFE Simulation Data Management. SLAC DMW 2004 March 16, 2004 W. W. Lee and S. Klasky Princeton Plasma Physics Laboratory Princeton, NJ. atomic mfp. electron-ion mfp. system size. skin depth. tearing length. ion gyroradius. Debye length. electron gyroradius. Spatial Scales (m). 10 -6.

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Mfe simulation data management

MFE Simulation Data Management

SLAC DMW 2004

March 16, 2004

W. W. Lee and S. Klasky

Princeton Plasma Physics Laboratory

Princeton, NJ


atomic mfp

electron-ion mfp

system size

skin depth

tearing length

ion gyroradius

Debye length

electron gyroradius

Spatial Scales (m)

10-6

10-2

10-4

100

102

pulse length

current diffusion

Inverse ion plasma frequency

inverse electron plasma frequency

confinement

ion gyroperiod

Ion collision

electron gyroperiod

electron collision

105

10-10

100

10-5

Temporal Scales (s)

Spatial & Temporal Scales Present Major Challenge to Theory & Simulations

  • • Huge range of spatial and temporal scales.

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

  • Different codes/theory for different scales.

  • 5+years: Integration of physics into Fusion Simulation Project




Plasma turbulence simulation
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
Gyrokinetic Approximation

  • Gyromotion

  • Polarization provides quasineutrality

[W. W. Lee, PF ‘83; JCP ‘87]


Earth Simulator

18%

10

(Ethier)


Ion Temperature Gradient Driven Turbulence

Particle Trajectories

Electrostatic Potential


Data management challenges
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
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
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.


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