large-scale accelerator simulations: synergia on the grid turn 1 turn 27 turn 19 turn 16 c++...
DESCRIPTION
Running Synergia (1)Few-person collaboration (2)Simulations require complex input parameters (3)Output consists of many files (4)Need to take advantage of computing resources wherever they are available Grid computing is the answer for (4), but the increase in complexity arising from (2) and (3) has to be mitigated. The tools provided by Synergia allow the scientist to do science without getting bogged down by bookkeeping.TRANSCRIPT
Large-scale accelerator simulations: Synergia on the Grid
turn 1
turn 27turn 19
turn 16
C++
C++
Synergia
Field solver(FFT,
multigrid)
single particleoptics/utilities
wrapper/job control
glue
input &lattice(MAD)
analysistools
results
beamstudies
Python
Fortran 90
C++Octave,
C++
softw
are
simulations
data
Synergia
● Simulate multi-particle physics in accelerators
● Computationally intensive– 1-10's of millions of macro
particles– 10's of thousands (or more) of
PDE solves● Massively parallel
– Clusters and supercomputers● 64-node Linux cluster typical● 512 processors at NERSC
C++
C++
Synergia
Field solver(FFT, multigrid)
single particleoptics/utilities
wrapper/job control
glue
input &lattice(MAD)
analysistools
results
beamstudies
Python
Fortran 90
C++
Octave,
C++
Running Synergia
(1) Few-person collaboration
(2) Simulations require complex input parameters
(3) Output consists of many files
(4) Need to take advantage of computing resources wherever they are available
Grid computing is the answer for (4), but the increase in complexity arising from (2) and (3) has to be
mitigated.The tools provided by Synergia allow the
scientist to do science without getting bogged down by bookkeeping.
Computing on the Grid
● Scientist uses local resources for most tasks
● Remote systems used for computationally-intensive tasks only
● In our case, the computationally intensive tasks are running the simulations and some analysis
job exportjob creationjob DBanalysis tools
importresults
importresults
Job creation
● Python-based system– Python not required on target
site● Job contains
– Batch input● created from template
– Input files● user-defined
– Utilities● clean output, pack output
– Description● human and machine readable
Job directory
batch file
input files
utility scripts
description
Goal is reproducibility
Managing job options
● Python module for command-line options
● Groups of options can be composed– General Synergia options– Batch options– Application-specific
options– etc.
● Command-line is stored for cut-and-paste modification
● Automatic command-line help generation
● Automatic human-readable summary
● Options for created jobs can be added to database
Job database
Job information is automatically entered in spreadsheet.
Results
● The measure of a scientific computing project is the science it produces
● The Synergia infrastructure has allowed us to produce more science with less time wasted on tedious tasks– Better utilization of resources– Less time spent bookkeeping– Fewer redundant simulation runs
The measure of a scientific computing project is the science it produces
Fermilab Booster Accelerator