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{toc} h1. Why use the pipeline to run Monte-Carlo jobs?

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The pipeline offers a number of advantages when running large numbers of Monte-Carlo jobs:

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* Provides an easy way to submit a large number of jobs

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* Maintains record of all jobs run, including links to log files and other files produced during the job

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* Makes it easy to rerun any jobs that fail due to quirks in the SLAC batch system.

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* Makes it easy to register output datasets in the data catalog, which in turn makes it easy to keep track of what MC data is available.

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* Provides a web interface to allow the status of jobs to be monitored from anywhere.

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h2. Example Monte-Carlo task

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Normally to run a set of Monte-Carlo jobs it is necessary to define a pipeline "Task". A task consists of an arbitrary graph of batch jobs and "scriptlets" to be run, however a typical Monte-Carlo

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 task consists of just two steps:

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Image Removed

In this example the two steps are:

runMonteCarlo – a batch job that is run to execute the simulation program and generate the output file
register-ds – a scriptlet which is run if the batch job finishes successfully that registers the output file in the data catalog.

Typically when generating large MC event samples it is necessary to run many MC jobs each of which generates some number of events. In the pipeline this is achieved by creating many streams within a given task. In the case of this example Monte-Carlo task each stream will run one batch job followed by one registration scriplet.

Defining a task

To create a pipeline task it is necessary to write an XML configuration file. The key elements of the XML configuration file for the job above (with some details initially left out) are shown here:

Code Block

title	EXOMCTemplate.xml



!TaskImageServlet.gif!

In this example the two steps are:

# runMonteCarlo - a batch job that is run to execute the simulation program and generate the output file
# register-ds - a scriptlet which is run if the batch job finishes successfully that registers the output file in the data catalog.

Typically when generating large MC event samples it is necessary to run many MC jobs each of which generates some number of events. In the pipeline this is achieved by creating many streams within a given task. In the case of this example Monte-Carlo task each stream will run one batch job followed by one registration scriplet.


h2. Defining a task

To create a pipeline task it is necessary to write an XML configuration file. The key elements of the XML configuration file for the task above (with some details initially left out) are shown here:

{code:title=EXOMCTemplate.xml}
<?xml version="1.0" encoding="UTF-8"?>
<pipeline xmlns="http://glast-ground.slac.stanford.edu/pipeline" 
          xmlns:xs="http://www.w3.org/2001/XMLSchema-instance" 
          xs:schemaLocation="http://glast-ground.slac.stanford.edu/pipeline 
          http://srs.slac.stanford.edu/Pipeline-II/schemas/2.0/pipeline.xsd">
  <task name="EXOMCBackground" type="EXO" version="1.3">
    <notation>A generic task for running EXO MC backgrounds</notation> 
    <variables>
      <var name="EXODIR">/nfs/slac/g/exo</var>
      <var name="EXOBASE">${EXODIR}/software/builds/trunk</var>
      <var name="BATCHOPTIONS">-R &quot;select[-rhel30] rusage[scratch=1]&quot;</var>
      <var name="CORE_LIMIT">1024</var>
      <var name="MAXEVENTS">10000</var> 
      <var name="PRINTMODULO">${MAXEVENTS/100}</var>
      <var name="MAXCPU">${MAXEVENTS/10}</var>
      <var name="MAXMEM">1000</var>
      <var name="OUTPUT_DIR">/nfs/slac/g/exo/exo_data/data/MC/backgrounds/test</var>
      <var name="OUTPUT_FORMAT">MC-background-%06d.root</var>
      <var name="OUTPUT_NAME">${format(pipeline.stream,OUTPUT_FORMAT)}</var>
      <var name="OUTPUT_FILE">${OUTPUT_DIR}/${OUTPUT_NAME}</var>
      <var name="DATACAT_DIR">EXO/Test</var>
      <var name="DATACAT_GROUP">MyGroup</var>
    </variables>

      <process name="runMonteCarlo">
         <job batchOptions="${BATCHOPTIONS}" maxCPU="${MAXCPU}" maxMemory="${MAXMEM}">
         ...
         </job>
      </process>


      <process name="register-ds">
         <notation>Register datasets created in this task</notation>
         <script>
         ...
         </script>
         <depends>
              <after process="runMonteCarlo"/>
         </depends>
     </process>
  </task>
</pipeline>
{code}

In this file the *<task>* element defines the name and version # of the task, as well as the task type. *<notation>* just indicates a comment describing the task. The *<variables>* section defines a set of variables which will be used elsewhere in the task. The values given to the variables are defaults which can be overridden for any specific

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The two steps of the task are each defined using a <process> element. The first <process> contains a <job> element indicating it is a batch job (the body of the job is not omitted for the moment). The second <process> contains a <script> element indicating that it is a scriptlet (again the body of the scriptlet is omitted for the time being. The <depends> element indicates that the scriptlet should only run after the batch job successfully completes.

Putting everything together the full XML file for the task is:

Code Block

title	EXOMCBackground.xml


<?xml version="1.0" encoding="UTF-8"?>
<pipeline xmlns="http://glast-ground.slac.stanford.edu/pipeline" 
          xmlns:xs="http://www.w3.org/2001/XMLSchema-instance" 
          xs:schemaLocation="http://glast-ground.slac.stanford.edu/pipeline 
          http://srs.slac.stanford.edu/Pipeline-II/schemas/2.0/pipeline.xsd">
  <task name="EXOMCBackground" type="EXO" version="1.3">
    <notation>A generic task for running EXO MC backgrounds</notation> 
    <variables>
      <var name="EXODIR">/nfs/slac/g/exo</var>
      <var name="EXOBASE">${EXODIR}/software/builds/trunk</var>
      <var name="BATCHOPTIONS">-R &quot;select[-rhel30] rusage[scratch=1]&quot;</var>
      <var name="CORE_LIMIT">1024</var>
      <var name="MAXEVENTS">10000</var> 
      <var name="PRINTMODULO">${MAXEVENTS/100}</var>
      <var name="MAXCPU">${MAXEVENTS/10}</var>
      <var name="MAXMEM">1000</var>
      <var name="OUTPUT_DIR">/nfs/slac/g/exo/exo_data/data/MC/backgrounds/test</var>
      <var name="OUTPUT_FORMAT">MC-background-%06d.root</var>
      <var name="OUTPUT_NAME">${format(pipeline.stream,OUTPUT_FORMAT)}</var>
      <var name="OUTPUT_FILE">${OUTPUT_DIR}/${OUTPUT_NAME}</var>
      <var name="DATACAT_DIR">EXO/Test</var>
      <var name="DATACAT_GROUP">MyGroup</var>
    </variables>

      <process name="runMonteCarlo">
         <job batchOptions="${BATCHOPTIONS}" maxCPU="${MAXCPU}" maxMemory="${MAXMEM}"><![CDATA[
              ulimit -c ${CORE_LIMIT} # Limit core dumps
              set -e # exit on error

              # Create a scratch area to write the output to
              export SCRATCH_DIR=/scratch/exo/${PIPELINE_PROCESSINSTANCE}
              mkdir -p ${SCRATCH_DIR}
              gotEXIT()
              {
                 rm -rf ${SCRATCH_DIR}   
              }
              trap gotEXIT EXIT

              source ${EXOBASE}/setup.sh

              cat > background.exo <<EOF
              use exosim rec toutput
              /exosim/macro background.mac
              /exosim/filter true
              printmodulo ${PRINTMODULO}
              /exosim/initial_seed ${PIPELINE_STREAM}
              maxevents ${MAXEVENTS}
              /toutput/file ${SCRATCH_DIR}/output.root
              begin
              exit
              EOF

              cat > background.mac <<EOF
              /digitizer/wireNoise 800.000000
              /digitizer/APDNoise 2000.000000 
              /digitizer/LXeEnergyRes 0.015000
              /event/LXeEventsOnly true
              /event/digitizeWires true
              /event/digitizeAPDs true
              /gps/pos/type Volume
              /gps/pos/shape Cylinder
              /gps/pos/halfz 72.5 cm
              /gps/pos/radius 75.0 cm
              /gps/pos/centre 0.0 0.0 0.0 cm
              /gps/pos/confine HFE
              /gps/energy 0 keV
              /gps/particle ion
              /gps/ion 19 40 0 0
              /grdm/analogueMC 1
              EOF

              EXOAnalysis background.exo
              
              mkdir -p ${OUTPUT_DIR}
              cp -pv ${SCRATCH_DIR}/output.root ${OUTPUT_FILE}
              ]]>
         </job>
      </process>


       stream when the stream is created. Note that variables can be defined in terms of other variables by using ${expression} syntax.


The two steps of the task are each defined using a *<process>* element. The first *<process>* contains a *<job>* element indicating it is a batch job (the body of the job is not omitted for the moment). Note again how variables defined earlier in the task are used to provide values for some parameters using the ${expression} syntax. The second *<process>* contains a *<script>* element indicating that it is a scriptlet (again the body of the scriptlet is omitted for the time being). The *<depends>* element indicates that the scriptlet should only run after the batch job successfully completes.

Now lets look at the parts which we initially missed out. First the body of the batch job, which by default is written as a bash script:
{code}
ulimit -c ${CORE_LIMIT} # Limit core dumps
set -e # exit on error

# Create a scratch area to write the output to
export SCRATCH_DIR=/scratch/exo/${PIPELINE_PROCESSINSTANCE}
mkdir -p ${SCRATCH_DIR}
gotEXIT()
{
   rm -rf ${SCRATCH_DIR}
}
trap gotEXIT EXIT

source ${EXOBASE}/setup.sh

cat > background.exo <<EOF
use exosim rec toutput
/exosim/macro background.mac
/exosim/filter true
printmodulo ${PRINTMODULO}
/exosim/initial_seed ${PIPELINE_STREAM}
maxevents ${MAXEVENTS}
/toutput/file ${SCRATCH_DIR}/output.root
begin
exit
EOF

cat > background.mac <<EOF
/digitizer/wireNoise 800.000000
/digitizer/APDNoise 2000.000000
/digitizer/LXeEnergyRes 0.015000
/event/LXeEventsOnly true
/event/digitizeWires true
/event/digitizeAPDs true
/gps/pos/type Volume
/gps/pos/shape Cylinder
/gps/pos/halfz 72.5 cm
/gps/pos/radius 75.0 cm
/gps/pos/centre 0.0 0.0 0.0 cm
/gps/pos/confine HFE
/gps/energy 0 keV
/gps/particle ion
/gps/ion 19 40 0 0
/grdm/analogueMC 1
EOF

EXOAnalysis background.exo

mkdir -p ${OUTPUT_DIR}
cp -pv ${SCRATCH_DIR}/output.root ${OUTPUT_FILE}
{code}


There are a few points worth noting
* All of the variables defined earlier in the task are passed to the batch job as environment variables and ca be referred to using the bash ${VARIABLE} syntax.
* The bash file creates a scratch folder at the top of the job, and registers a cleanup trap to delete the scratch area at the end of the job. The output data is written to the scratch area and copied to its final location if the job completes successfully. This is recommended practice since if many batch jobs write simultaneously to the same NFS file server it will likely become overloaded and fail horribly.
* The .mac file and .exo file required for running EXOAnalysis are generated on the fly, substituting in settings from the variables defined earlier. Note also the use of some special pipeline variables:
** *${PIPELINE_STREAM}* \-\- an id assigned when each stream is created. The id will be unique within this task and normally starts at 0 and increments for each subsequent stream
** *${PIPELINE_PROCESSINSTANCE}* \-\- similar to pipeline stream this is a unique id associated with a stream, but this id is unique across all tasks, and is normally a large ugly number

Finally lets look at the body of the scriptlet which is used to register the output dataset. The scriptlet is written in python:
{code}
attributes = {'sCreator':'tonyj','nThings':0.1}
dsNew = datacatalog.newDataset(OUTPUT_NAME, "root", "EXOROOT", DATACAT_DIR, DATACAT_GROUP, "SLAC", OUTPUT_FILE)
datacatalog.registerDataset(dsNew, attributes);
{code}
Again a few things worth noting:
* The datacatalog allows arbitrary meta-data to be associated with datasets. In this case the meta-data is defined as a python dictionary
* The newDataset method has many arguments which need some explanation.

Putting everything together the full XML file for the task is:
{code:title=EXOMCBackground.xml}
xmlns:xs="http://www.w3.org/2001/XMLSchema-instance"
xs:schemaLocation="http://glast-ground.slac.stanford.edu/pipeline
http://srs.slac.stanford.edu/Pipeline-II/schemas/2.0/pipeline.xsd">

A generic task for running EXO MC backgrounds

/nfs/slac/g/exo
${EXODIR}/software/builds/trunk
-R "select[-rhel30] rusage[scratch=1]"
1024
10000
${MAXEVENTS/100}
${MAXEVENTS/10}
1000
/nfs/slac/g/exo/exo_data/data/MC/backgrounds/test
MC-background-%06d.root
${format(pipeline.stream,OUTPUT_FORMAT)}
${OUTPUT_DIR}/${OUTPUT_NAME}
EXO/Test
MyGroup



</job>
</process>


<process name="register-ds">
         <notation>
                  Register datasets created in this task
         </notation>
         <script><!<script><\!\[CDATA\[ 
           from java.util import HashMap
           from org.glast.datacat.client.sql import NewDataset

            NewDataset

attributes = HashMap()
           attributes.put('sCreator', 'tonyj')

           dsNew = NewDataset(OUTPUT_NAME, "root", "EXOROOT", DATACAT_DIR, DATACAT_GROUP, "SLAC", OUTPUT_FILE)
           datacatalog.registerDataset(dsNew, attributes);
           ]]>
         \]\]>
</script>

         <depends>
              <after process="runMonteCarlo"></after>
         </depends>

     </process>
  </task>
</pipeline>
{code}

{code}

{code}

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