worker quick start#


The worker framework has been developed to meet a specific use case. Often you want to run an application on many input files, or with a variety of parameters. Since this requires considerable bookkeeping and is error prone, it is much more convenient to use the worker framework for this type of workload.

The framework will keep track of the computations that have been performed, those that succeeded or failed, and enable you to resume computations easily based on that information. Additionally, It helps you to aggregate information produced by individual computations.

In short, it has been designed for any scenario that can be reduced to a MapReduce approach.

However, in some cases, atools is a better alternative than worker, especially for MPI applications. The worker framework can not handle such applications since it uses MPI for its own process communication. atools offers similar functionality to worker. We provide some guidance for choosing between worker and atools.

This quick start shows you how to use the basics of the worker framework. For more information, please consult the worker documentation.


A (sequential) job you have to run many times for various parameter values. We will use a non-existent program cfd-test by way of running example.

Step by step#

We will consider the following use cases already mentioned above:

Parameter variations#

Suppose the program the user wishes to run is cfd-test (this program does not exist, it is just an example) that takes three parameters, a temperature, a pressure and a volume. A typical call of the program looks like:

cfd-test -t 20 -p 1.05 -v 4.3

The program will write its results to standard output. A PBS script (say run.pbs) that would run this as a job would then look like:

#!/bin/bash -l
#PBS -l nodes=1:ppn=1
#PBS -l walltime=00:15:00

cfd-test -t 20  -p 1.05  -v 4.3

When submitting this job, the calculation is performed or this particular instance of the parameters, i.e., temperature = 20, pressure = 1.05, and volume = 4.3. To submit the job, the user would use:

$ qsub run.pbs

However, the user wants to run this program for many parameter instances, e.g., he wants to run the program on 100 instances of temperature, pressure and volume. To this end, the PBS file can be modified as follows:

#!/bin/bash -l
#PBS -l nodes=1:ppn=8
#PBS -l walltime=04:00:00

cfd-test -t $temperature  -p $pressure  -v $volume


  1. the parameter values 20, 1.05, 4.3 have been replaced by variables $temperature, $pressure and $volume respectively;

  2. the number of processors per node has been increased to 8 (i.e., ppn=1 is replaced by ppn=8); and

  3. the walltime has been increased to 4 hours (i.e., walltime=00:15:00 is replaced by walltime=04:00:00).

The walltime is calculated as follows: one calculation takes 15 minutes, so 100 calculations take 1,500 minutes on one core. However, this job will use 7 cores (1 is reserved for delegating work), so the 100 calculations will be done in 1,500/7 = 215 minutes, i.e., 4 hours to be on the safe side. Note that starting from version 1.3, a dedicated core is no longer required for delegating work when using the -master flag. This is however not the default behavior since it is implemented using features that are not standard. This implies that in the previous example, the 100 calculations would be completed in 1,500/8 = 188 minutes.

The 100 parameter instances can be stored in a comma separated value file (CSV) that can be generated using a spreadsheet program such as Microsoft Excel, or just by hand using any text editor (do not use a word processor such as Microsoft Word). The first few lines of the file data.txt would look like:


It has to contain the names of the variables on the first line, followed by 100 parameter instances in the current example. Items on a line are separated by commas.

The job can now be submitted as follows:

$ module load worker/1.6.10-intel-2018a
$ wsub  -batch run.pbs  -data data.txt

Note that the PBS file is the value of the -batch option . The cfd-test program will now be run for all 100 parameter instances—7 concurrently—until all computations are done. A computation for such a parameter instance is called a work item in worker parlance.

Job array-like scenario#

worker also supports job array-like usage pattern since it offers a convenient workflow.

A typical PBS script run.pbs for use with job arrays would look like this:

#!/bin/bash -l
#PBS -l nodes=1:ppn=1
#PBS -l walltime=00:15:00

word-count  -input "${INPUT_FILE}"  -output "${OUTPUT_FILE}"

As in the previous section, the word-count program does not exist. Input for this fictitious program is stored in files with names such as input_1.dat, input_2.dat, …, input_100.dat that the user produced by whatever means, and the corresponding output computed by word-count is written to output_1.dat, output_2.dat, …, output_100.dat. (Here we assume that the non-existent word-count program takes options -input and -output.)

The job would be submitted using:

$ qsub  -t 1-100  run.pbs

The effect is that rather than 1 job, the user would actually submit 100 jobs to the queue system. There are some potential disadvantages to this.

Using worker, a feature akin to job arrays can be used with minimal modifications to the PBS script:

#!/bin/bash -l
#PBS -l nodes=1:ppn=8
#PBS -l walltime=04:00:00

word-count  -input "${INPUT_FILE}"  -output "${OUTPUT_FILE}"


  1. the number of cores is increased to 8 (ppn=1 is replaced by ppn=8); and

  2. the walltime has been modified (walltime=00:15:00 is replaced by walltime=04:00:00).

The walltime is calculated as follows: one calculation takes 15 minutes, so 100 calculation take 1,500 minutes on one core. However, this job will use 7 cores (1 is reserved for delegating work), so the 100 calculations will be done in 1,500/7 = 215 minutes, i.e., 4 hours to be on the safe side. Note that starting from version 1.3 when using the -master flag, a dedicated core for delegating work is no longer required. This is however not the default behavior since it is implemented using features that are not standard. So in the previous example, the 100 calculations would be done in 1,500/8 = 188 minutes.

The job is now submitted as follows:

$ module load worker/1.6.10-intel-2018a
$ wsub  -t 1-100  -batch run.pbs

The word-count program will now be run for all 100 input files---7 concurrently—--until all computations are done. Again, a computation for an individual input file, or, equivalently, an array ID, is called a work item in worker speak. Note that in contrast to Torque job arrays, a worker job array submits a single job.

MapReduce: prologues and epilogue#

Often, an embarrassingly parallel computation can be abstracted to three simple steps:

  1. a preprocessing phase in which the data is split up into smaller, more manageable chunks;

  2. on these chunks, the same algorithm is applied independently (these are the work items); and

  3. the results of the computations on those chunks are aggregated into, e.g., a statistical description of some sort.

The worker framework directly supports this scenario by using a prologue and an epilogue. The former is executed just once before work is started on the work items, the latter is executed just once after the work on all work items has finished. Technically, the prologue and epilogue are executed by the master, i.e., the process that is responsible for dispatching work and logging progress.

Suppose that is a script that prepares the data by splitting it into 100 chunks, and aggregates the data, then one can submit a MapReduce style job as follows:

$ wsub  -prolog  -batch run.pbs  -epilog  -t 1-100


The time taken for executing the prologue and the epilogue should be added to the job’s total walltime.

Some notes on using worker efficiently#

worker is implemented using MPI, so it is not restricted to a single compute node, it scales well to many nodes. However, remember that jobs requesting a large number of nodes typically spend quite some time in the queue.

Typically, worker will be effective when

  • work items, i.e., individual computations, are neither too short, nor too long (i.e., from a few minutes to a few hours); and,

  • when the number of work items is larger than the number of cores involved in the job (e.g., more than 30 for 8 cores).

Too few work items per core will lead to bad load balance, and hence inefficient use of the compute resources. To analyse the load balance of a finished job, use wload (see the worker documentation for details).

Monitoring a worker job#

Since a worker job will typically run for several hours, it may be reassuring to monitor its progress. worker keeps a log of its activity in the directory where the job was submitted. The log’s name is derived from the job’s name and the job’s ID, i.e., it has the form <jobname>.log<jobid>. For the running example, this could be run.pbs.log445948, assuming the job’s ID is 445948. To keep an eye on the progress, one can use:

$ tail  -f  run.pbs.log445948

Alternatively, a worker command that summarizes a log file can be used:

$ watch  -n 60  wsummarize run.pbs.log445948

This will summarize the log file every 60 seconds.

The efficiency of a worker job is in part determined by the load balance. The load balanced is consider good if each worker slave is busy as much as possible. You can analyse this when the job is done using wload, see the worker documentation for details.

Time limits for work items#

Sometimes, the execution of a work item takes long than expected, or worse, some work items get stuck in an infinite loop. This situation is unfortunate, since it implies that work items that could successfully are not even started. Again, a simple and yet versatile solution is offered by the worker framework. If we want to limit the execution of each work item to at most 20 minutes, this can be accomplished by modifying the script of the running example.

#!/bin/bash -l
#PBS -l nodes=1:ppn=8
#PBS -l walltime=04:00:00

module load timedrun/1.0.1
timedrun  -t 00:20:00  cfd-test -t $temperature  -p $pressure  -v $volume


It is trivial to set individual time constraints for work items by introducing a parameter, and including the values of the latter in the CSV file, along with those for the temperature, pressure and volume.


timedrun is in fact offered in a module of its own, so it can be used outside the worker framework as well.

Resuming a worker job#

Unfortunately, it is not always easy to estimate the walltime for a job, and consequently, sometimes the latter is underestimated. When using the worker framework, this implies that not all work items will have been processed. worker makes it very easy to resume such a job without having to figure out which work items did complete successfully, and which remain to be computed. Suppose the job that did not complete all its work items had ID 445948.

$ wresume -jobid 445948

This will submit a new job that will start to work on the work items that were not done yet. Note that it is possible to change almost all job parameters when resuming, specifically the requested resources such as the number of cores and the walltime.

$ wresume  -l walltime=1:30:00  -jobid 445948

Work items may fail to complete successfully for a variety of reasons, e.g., a data file that is missing, a (minor) programming error, etc. Upon resuming a job, the work items that failed are considered to be done, so resuming a job will only execute work items that did not terminate either successfully, or reporting a failure. It is also possible to retry work items that failed (preferably after the glitch why they failed was fixed).

$ wresume  -jobid 445948  -retry

By default, a job’s prologue is not executed when it is resumed, while its epilogue is. wresume has options to modify this default behavior.

Aggregating result data#

In some settings, each work item produces a file as output, but the final result should be an aggregation of those files. Although this is not necessarily hard, it is tedious, but worker can help you achieve this easily since, typically, the file name produced by a work item is based on the parameters of that work item.

Consider the following data file data.csv:

a,   b
1.3, 5.7
2.7, 1.4
3.4, 2.1
4.1, 3.8

Processing it would produce 4 files, i.e., output-1.3-5.7.txt, output-2.7-1.4.txt, output-3.4-2.1.txt, output-4.1-3.8.txt. To obtain the final data, these files should be concatenated into a single file output.txt. This can be done easily using wcat:

$ wcat  -data data.csv  -pattern output-[%a%]-[%b%].txt \
        -output output.txt

The pattern describes the file names as generated by each work item in terms of the parameter names and values defined in the data file data.csv.

wcat optionally skips headers of all of the first file when the -skip_first <n> option is used (<n> is the number of lines to skip). By default, blank lines are omitted, but by using the -keep_blank options, they will be written to the output file. Help is available using the -help flag.

For more sophisticated aggregation tasks, the worker framework provides the wreduce command, see the worker documentation for details.

Multi-threaded work items#

The worker framework can be used to run multi-threaded work items, please see the worker documentation for details.

Further information#

For the information about the most recent version and new features please check the official worker documentation web page.

For information on how to MPI programs as work items, please contact your friendly system administrator.

This how-to introduces only worker’s basic features. The wsub command and all other worker commands have some usage information that is printed when the -help option is specified:

### usage: wsub  -batch <batch-file>          \\
#                [-data <data-files>]         \\
#                [-prolog <prolog-file>]      \\
#                [-epilog <epilog-file>]      \\
#                [-log <log-file>]            \\
#                [-mpiverbose]                \\
#                [-master]                    \\
#                [-threaded]                  \\
#                [-dryrun] [-verbose]         \\
#                [-quiet] [-help]             \\
#                [-t <array-req>]             \\
#                [<pbs-qsub-options>]
#   -batch <batch-file>   : batch file template, containing variables to be
#                           replaced with data from the data file(s) or the
#                           PBS array request option
#   -data <data-files>    : comma-separated list of data files (default CSV
#                           files) used to provide the data for the work
#                           items
#   -prolog <prolog-file> : prolog script to be executed before any of the
#                           work items are executed
#   -epilog <epilog-file> : epilog script to be executed after all the work
#                           items are executed
#   -mpiverbose           : pass verbose flag to the underlying MPI program
#   -verbose              : feedback information is written to standard error
#   -dryrun               : run without actually submitting the job, useful
#   -quiet                : don't show information
#   -help                 : print this help message
#   -master               : start an extra master process, i.e.,
#                           the number of slaves will be nodes*ppn
#   -threaded             : indicates that work items are multi-threaded,
#                           ensures that CPU sets will have all cores,
#                           regardless of ppn, hence each work item will
#                           have <total node cores>/ppn cores for its
#                           threads
#   -t <array-req>        : qsub's PBS array request options, e.g., 1-10
#   <pbs-qsub-options>    : options passed on to the queue submission
#                           command


The most common problem with the worker framework is that it doesn’t seem to work at all, showing messages in the error file about module failing to work. The cause is trivial, and easy to remedy.

Like any PBS script, a worker PBS file has to be in UNIX format!

If you edited a PBS script on your desktop, or something went wrong during sftp/scp, the PBS file may end up in DOS/Windows format, i.e., it has the wrong line endings. The PBS/Torque queue system can not deal with that, so you will have to convert the file, e.g., for file run.pbs:

$ dos2unix run.pbs