1. MFE Simulation Data Management
SLAC DMW 2004
March 16, 2004
W. W. Lee and S. Klasky
Princeton Plasma Physics Laboratory
Princeton, NJ

2. • Huge range of spatial and temporal scales.
Spatial & Temporal Scales Present Major Challenge to Theory & Simulations
10-6
10-4
10-2
100
102
Spatial Scales (m)
electron gyroradius
Debye length
ion gyroradius
tearing length
skin depth
system size
atomic mfp
electron-ion mfp
10-10
10-5
105
Temporal Scales (s)
electron gyroperiod
electron collision
ion gyroperiod
Ion collision
inverse electron plasma frequency
confinement
Inverse ion plasma frequency
current diffusion
pulse length
• 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

3. Major Fusion Codes

4. Data Rates of Major Fusion Codes
(GB)
now / 5yr
Runtime
now/5yr (hr)
Processors
Now/5yr
Mbs
GTC
4,000 / 100,000
300/150
2048
80/ 1600
Gyro
10 / 100
30/30
512/2048
.8/ 8
GS2
.8 / 8
Degas2 .1 1 10 .2
Transp
.05 3
.04
Nimrod
5/ 50
20/20
128
.6/ 6
M3D
1.1/ 11
NSTX
.25/shot
1/ 4
0.25 * 40
9, 36
Total (TB)
4.3 / 101

5. 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

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

8. 18% 10
(Ethier)
Earth Simulator

9. Ion Temperature Gradient Driven Turbulence
Particle Trajectories
Electrostatic Potential

10. 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.

11. 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.

12. 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.