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Work Queue Tutorial

  1. Getting Started
    1. Login to the Future Grid Head Node
    2. Tutorial Setup
    3. Set Environment Variables
  2. Work Queue Example
    1. Setup
    2. Analysis
    3. Running
  3. Exercise
    1. Chaining Tasks

This tutorial will have you install CCTools into your FutureGrid home directory and will take you through some distributed computation examples using Work Queue.

Getting Started

Login to the Future Grid Head Node

For this tutorial, we assume you have an open SSH connection to the Future Grid login nodes. If you do not have an account with Future Grid, then you may register here.

In this tutorial, we will again use the alamo login node:

ssh alamo.futuregrid.org

Tutorial Setup

The setup for this tutorial follows the setup described and installed in the last tutorial session.

NOTE: If you have not already built and installed CCTools on the Future Grid login node, please follow the instructions listed here before continuing further.

Set Environment Variables

For this tutorial, you will need to add your CCTools directory to your $PATH:

export PATH=~/cctools/bin:${PATH}

We will use both Python and Perl in this tutorial. They need to be able to find our installed packages. So set this environment variable for running the Python examples:

export PYTHONPATH=${PYTHONPATH}:~/cctools/lib/python2.4/site-packages

And this environment variable for running the Perl examples:

export PERL5LIB=${PERL5LIB}:~/cctools/lib/perl5/site_perl

Work Queue program running the Simulation Executable

It is simple to write a program in C/Perl/Python (or any language with appropriate Work Queue bindings) which can generate tasks to be queued for Work Queue. In this example, we will create 100 simulation tasks using simulation.py.

Setup

mkdir ~/cctools-tutorial/wq cd ~/cctools-tutorial/wq wget http://www.nd.edu/~ccl/software/tutorials/acic12/wq/simulation.py wget http://www.nd.edu/~ccl/software/tutorials/acic12/wq/input.txt wget http://www.nd.edu/~ccl/software/tutorials/acic12/wq/wq.py

Analysis

We will analyze the code in wq.py to study a workflow implemented using the Python bindings for the Work Queue API.

#!/usr/bin/env python from work_queue import * import sys try: Q = WorkQueue(port = 0) except: print "could not instantiate Work Queue master" sys.exit(1) print "Listening on port %d." % Q.port print "Submitting 100 simulation tasks..." for i in range(0, 100): infile = "input.txt" outfile = "file.%0.2x" % i command = "python simulation.py %d < %s > %s" % (i, infile, outfile) T = Task(command) T.specify_file("simulation.py", "simulation.py", WORK_QUEUE_INPUT, cache = True) T.specify_file(infile, infile, WORK_QUEUE_INPUT, cache = False) T.specify_file(outfile, outfile, WORK_QUEUE_OUTPUT, cache = False) taskid = Q.submit(T) print "done." print "Waiting for tasks to complete..." while not Q.empty(): T = Q.wait(5) if T: print "task (id# %d) complete: %s (return code %d)" % (T.id, T.command, T.return_status) print "done."

Here we load the work_queue Python binding. Python will look in PYTHONPATH which is setup in your environment.

This instantiates a Work Queue master to which you may submit work. Setting the port to 0 instructs Work Queue to pick an arbitrary port to bind on.

We create a task which takes a shell command argument. The first task created in this workflow will have the command:

T = Task("python simulation.py 0 < input.txt > file.00");

Each task usually depends on a number of files to run. These include the executable and any input files. Here we specify the simulation.py executable and its input infile. Notice that we specify both simulation.py and infile twice when calling specify_file. The first argument is the name of the file on the master and the second argument is the name of the file we want created on the worker. Usually these filenames will be the same as in this example.

We specify the output file, outfile, which we want transferred back to the master.

At this point we have finished the description of our task and it is ready to be submitted for execution on the Work Queue workers. Q.submit submits this task.

At this point we wish to wait for all submitted tasks to complete. So long as the queue is not empty, we continue to call Q.wait waiting for the result of a task we submitted.

Here we call Q.wait(5) which takes a timeout argument. The call to wait will return a finished task which allows us to analyze the return_status or output. In this example, we set the timeout to 5 seconds which allows our application to do other things if a task is taking an inordinate amount of time to complete. We could have used the constant WORK_QUEUE_WAITFORTASK to wait indefinitely until a task completes.

Perl equivalent

The Perl code of the above program is in wq.pl and is shown here:

#!/usr/bin/perl use work_queue; my $q = work_queue_create(0); if (not defined($q)) { print "could not instantiate Work Queue master\n"; exit 1; } $port = work_queue_port($q); print "Listening on port $port.\n"; print "Submitting 100 simulation tasks..."; for (my $i = 0; $i < 100; $i++) { my $infile = "input.txt"; my $outfile = sprintf("file.%0.2x", $i); my $command = "python simulation.py $i < $infile > $outfile"; my $t = work_queue_task_create($command); work_queue_task_specify_file($t,"simulation.py","simulation.py",$WORK_QUEUE_INPUT,$WORK_QUEUE_CACHE); work_queue_task_specify_file($t,$infile,$infile,$WORK_QUEUE_INPUT,$WORK_QUEUE_CACHE); work_queue_task_specify_file($t,$outfile,$outfile,$WORK_QUEUE_OUTPUT,$WORK_QUEUE_NOCACHE); my $taskid = work_queue_submit($q, $t); } print "done." print "Waiting for tasks to complete...\n"; while (not work_queue_empty($q)) { my $t = work_queue_wait($q, 5); if (defined($t)) { print "task (id#$t->{taskid}) complete:$t->{command_line} (return code $t->{return_status})\n"; work_queue_task_delete($t); } } print "done.\n"; work_queue_delete($q); exit 0;

You can download this program using:

wget http://www.nd.edu/~ccl/software/tutorials/acic12/wq/wq.pl

Running

To run the above Work Queue Python program, do

python wq.py &

To run the above Work Queue Perl program, do

perl wq.pl &

When a Work Queue program is run, it prints the port on which it is listening for connections from the workers. For example:

$ python wq.py & Listening on port XXXX.

Start 10 workers on the Torque compute nodes in FutureGrid for this master

torque_submit_workers -t 10 alamo.futuregrid.org XXXX 10

replacing XXXX with the port the Work Queue master program is listening on.

You can also start workers on the HPC/HTC cluster at the University of Arizona through the PBS batch submission system using the pbs_submit_workers script. You can download this script as follows:

wget http://www.nd.edu/~ccl/software/tutorials/acic12/wq/pbs_submit_workers

To start 10 workers on the compute nodes in the HPC/HTC cluster at the University of Arizona, run this from the login node:

pbs_submit_workers -t 10 -p "-q windfall -W group_list=$USER" alamo.futuregrid.org XXXX 10

If you have allocations to the higher priority queues (standard, quality, etc) in the UA clusters, you can submit workers to these queues by specifying the appropriate options.

If you encounter an error, be sure you did not forget to setup your environment.

Exercise

Chaining Tasks

The goal of this exercise is to change the workflow to chain the executions of simulation.py so that the output of one simulation is the input of another. For this exercise, the workflow should look like:

chaining simulation

Because our simulation.py is sophisticated and runs on average for 5 seconds, in this example we will only do 5 instances of the simulation (instead of 100) so it takes about 25 seconds.

For this exercise, remember that when you run Q.submit(T), it finalizes the task and allows it to be sent to a worker for execution. You will need to wait for the output from a worker to come back before sending out the next one. As before, you can wait for the completion of a task using Q.wait(5).

Once you have finished implementing your version of the chained simulation, you may compare with this python solution or perl solution.