View source for Documentation:Running ALPS Simulations

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= Overview =

In the ALPS library simulations are based on the scheduler library which allows you to specify parameters for your simulations, including multiple definitions of parameters (e.g. if you want to simulate a physical system at a couple of temperatures). The scheduler library will then start jobs for every single parameter set, either on a serial or parallel machine, and uses checkpoints to prevent data loss when exceeding machine walltimes.
The scheduler library asks for a job file which specifies task files for every set of parameters for which a Monte Carlo simulation shall be run. The job and task files are given in XML format, following the schema at [http://xml.comp-phys.org http://xml.comp-phys.org]. The scheduler will read in these files and write observables into the task file.
An example job file could look like this:

 <JOB>
  <OUTPUT file="parm.xml"/>
  <TASK status="new">
    <INPUT file="parm.task1.in.xml"/>
    <OUTPUT file="parm.task1.xml"/>
  </TASK>
  <TASK status="new">
    <INPUT file="parm.task2.in.xml"/>
    <OUTPUT file="parm.task2.xml"/>
  </TASK>
  <TASK status="new">
    <INPUT file="parm.task3.in.xml"/>
    <OUTPUT file="parm.task3.xml"/>
  </TASK> 
 </JOB>

and an example task file like:

 <SIMULATION>
  <PARAMETERS>
    <PARAMETER name="L">100</PARAMETER>
    <PARAMETER name="SWEEPS">10000</PARAMETER>
    <PARAMETER name="T">0.5</PARAMETER>
    <PARAMETER name="THERMALIZATION">100</PARAMETER>
  </PARAMETERS> 
 </SIMULATION>

Here we will discuss how to prepare, run, and evaluate ALPS simulations. ALPS 2 supports three ways of performing simulations:
# [[ALPS_using_the_command_line | Using the command line ]] (with limited evaluation tools)
# [[ALPS_using_Python | Using Python]]
# [[ALPS_using_Vistrails| Using  the provenance-enabled Vistrails workflow system]]

All three ways produce the same output files. Command line, Python, and Vistrails can be mixed and matched as you desire. The common features are the three phases of a simulation:

# Preparing the input files
# Running the simulation
# Evaluating the results

= Comment on random number generators =

Whenever you use Monte-Carlo simulations, you need to remember that you work with
[http://en.wikipedia.org/wiki/Pseudorandom_number_generator pseudo-random numbers].
There is always a small chance that your application is just by chance the one that shows that
a so-far good pseudo random number generator is not ideal. Hence, as is standard practice for all
high-accuracy Monte Carlo simuations, you should run a simulation with more than one random number generator
if you strive for high accuracy.
The [[Tutorials:Parameters#Additional parameters for Monte Carlo simulations|RNG parameter]]
of the simulation allows you to change the
random number generator in order to validate your results.




&copy; 2003-2010 by Simon Trebst, Matthias Troyer, and Synge Todo