This tutorial is viewable at: http://swift-lang.org/tutorials/cray/swift-cray-tutorial.html

Introduction: Why Parallel Scripting?

Swift is a simple scripting language for executing many instances of ordinary application programs on distributed parallel resources. Swift scripts run many copies of ordinary programs concurrently, using statements like this:

foreach protein in proteinList {
  runBLAST(protein);
}

Swift acts like a structured "shell" language. It runs programs concurrently as soon as their inputs are available, reducing the need for complex parallel programming. Swift expresses your workflow in a portable fashion: The same script runs on multicore computers, clusters, clouds, grids, and supercomputers.

In this tutorial, you’ll be able to first try a few Swift examples (parts 1-3) on the Cray login host, to get a sense of the language. Then in parts 4-6 you’ll run similar workflows on Cray compute nodes, and see how more complex workflows can be expressed with Swift scripts.

Swift tutorial setup

To install the tutorial scripts on Cray "Raven" XE6-XK7 test system, do:

$ cd $HOME
$ tar xzf /home/users/p01537/swift-cray-tutorial.tgz
$ cd swift-cray-tutorial
$ source setup.sh   # You must run this with "source" !

Verify your environment

To verify that Swift (and the Java environment it requires) are working, do:

$ java -version   # verify that you have Java (ideally Oracle JAVA 1.6 or later)
$ swift -version  # verify that you have Swift 0.94.1
Note If you re-login or open new ssh sessions, you must re-run source setup.sh in each ssh shell/window.

To check out the tutorial scripts from SVN

If you later want to get the most recent version of this tutorial from the Swift Subversion repository, do:

$ svn co https://svn.ci.uchicago.edu/svn/vdl2/SwiftTutorials/swift-cray-tutorial Cray-Swift

This will create a directory called "Cray-Swift" which contains all of the files used in this tutorial.

Simple "science applications" for the workflow tutorial

This tutorial is based on two intentionally trivial example programs, simulation.sh and stats.sh, (implemented as bash shell scripts) that serve as easy-to-understand proxies for real science applications. These "programs" behave as follows.

simulate.sh

The simulation.sh script serves as a trivial proxy for any more complex scientific simulation application. It generates and prints a set of one or more random integers in the range [0-2^62) as controlled by its command line arguments, which are:

$ ./app/simulate.sh --help
./app/simulate.sh: usage:
    -b|--bias       offset bias: add this integer to all results [0]
    -B|--biasfile   file of integer biases to add to results [none]
    -l|--log        generate a log in stderr if not null [y]
    -n|--nvalues    print this many values per simulation [1]
    -r|--range      range (limit) of generated results [100]
    -s|--seed       use this integer [0..32767] as a seed [none]
    -S|--seedfile   use this file (containing integer seeds [0..32767]) one per line [none]
    -t|--timesteps  number of simulated "timesteps" in seconds (determines runtime) [1]
    -x|--scale      scale the results by this integer [1]
    -h|-?|?|--help  print this help
$

All of thess arguments are optional, with default values indicated above as [n].

With no arguments, simulate.sh prints 1 number in the range of 1-100. Otherwise it generates n numbers of the form (R*scale)+bias where R is a random integer. By default it logs information about its execution environment to stderr. Here’s some examples of its usage:

$ simulate.sh 2>log
       5
$ head -4 log

Called as: /home/wilde/swift/tut/CIC_2013-08-09/app/simulate.sh:
Start time: Thu Aug 22 12:40:24 CDT 2013
Running on node: login01.osgconnect.net

$ simulate.sh -n 4 -r 1000000 2>log
  239454
  386702
   13849
  873526

$ simulate.sh -n 3 -r 1000000 -x 100 2>log
 6643700
62182300
 5230600

$ simulate.sh -n 2 -r 1000 -x 1000 2>log
  565000
  636000

$ time simulate.sh -n 2 -r 1000 -x 1000 -t 3 2>log
  336000
  320000
real    0m3.012s
user    0m0.005s
sys     0m0.006s

stats.sh

The stats.sh script serves as a trivial model of an "analysis" program. It reads N files each containing M integers and simply prints the\ average of all those numbers to stdout. Similarly to simulate.sh it logs environmental information to the stderr.

$ ls f*
f1  f2  f3  f4

$ cat f*
25
60
40
75

$ stats.sh f* 2>log
50

Basic of the Swift language with local execution

A Summary of Swift in a nutshell

  • Swift scripts are text files ending in .swift The swift command runs on any host, and executes these scripts. swift is a Java application, which you can install almost anywhere. On Linux, just unpack the distribution tar file and add its bin/ directory to your PATH.

  • Swift scripts run ordinary applications, just like shell scripts do. Swift makes it easy to run these applications on parallel and remote computers (from laptops to supercomputers). If you can ssh to the system, Swift can likely run applications there.

  • The details of where to run applications and how to get files back and forth are described in configuration files separate from your program. Swift speaks ssh, PBS, Condor, SLURM, LSF, SGE, Cobalt, and Globus to run applications, and scp, http, ftp, and GridFTP to move data.

  • The Swift language has 5 main data types: boolean, int, string, float, and file. Collections of these are dynamic, sparse arrays of arbitrary dimension and structures of scalars and/or arrays defined by the type declaration.

  • Swift file variables are "mapped" to external files. Swift sends files to and from remote systems for you automatically.

  • Swift variables are "single assignment": once you set them you can’t change them (in a given block of code). This makes Swift a natural, "parallel data flow" language. This programming model keeps your workflow scripts simple and easy to write and understand.

  • Swift lets you define functions to "wrap" application programs, and to cleanly structure more complex scripts. Swift app functions take files and parameters as inputs and return files as outputs.

  • A compact set of built-in functions for string and file manipulation, type conversions, high level IO, etc. is provided. Swift’s equivalent of printf() is tracef(), with limited and slightly different format codes.

  • Swift’s foreach {} statement is the main parallel workhorse of the language, and executes all iterations of the loop concurrently. The actual number of parallel tasks executed is based on available resources and settable "throttles".

  • In fact, Swift conceptually executes all the statements, expressions and function calls in your program in parallel, based on data flow. These are similarly throttled based on available resources and settings.

  • Swift also has if and switch statements for conditional execution. These are seldom needed in simple workflows but they enable very dynamic workflow patterns to be specified.

We’ll see many of these points in action in the examples below. Lets get started!

Part 1: Run a single application under Swift

The first swift script, p1.swift, runs simulate.sh to generate a single random number. It writes the number to a file.

p1 workflow
p1.swift
type file;

app (file o) simulation ()
{
  simulate stdout=@filename(o);
}

file f <"sim.out">;
f = simulation();

To run this script, run the following command:

$ cd part01
$ swift p1.swift
Swift 0.94.1 RC2 swift-r6895 cog-r3765

RunID: 20130827-1413-oa6fdib2
Progress:  time: Tue, 27 Aug 2013 14:13:33 -0500
Final status: Tue, 27 Aug 2013 14:13:33 -0500  Finished successfully:1
$ cat sim.out
      84
$ swift p1.swift
$ cat sim.out
      36

To cleanup the directory and remove all outputs (including the log files and directories that Swift generates), run the cleanup script which is located in the tutorial PATH:

$ cleanup
Note You’ll also find two Swift configuration files in each partNN directory of this tutorial. These specify the environment-specific details of where to find application programs (file apps) and where to run them (file sites.xml). These files will be explained in more detail in parts 4-6, and can be ignored for now.

Part 2: Running an ensemble of many apps in parallel with a "foreach" loop

The p2.swift script introduces the foreach parallel iteration construct to run many concurrent simulations.

part02.png
p2.swift
type file;

app (file o) simulation ()
{
  simulate stdout=@filename(o);
}

foreach i in [0:9] {
  file f <single_file_mapper; file=@strcat("output/sim_",i,".out")>;
  f = simulation();
}

In part 2, we also update the apps file. Instead of using shell script (simulate.sh), we use the equivalent python version (simulate.py). The new apps file now looks like this:

localhost simulate simulate.py

Swift does not need to know anything about the language an application is written in. The application can be written in Perl, Python, Java, Fortran, or any other language.

The script also shows an example of naming the output files of an ensemble run. In this case, the output files will be named output/sim_N.out.

To run the script and view the output:

$ cd ../part02
$ swift p2.swift
$ ls output
sim_0.out  sim_1.out  sim_2.out  sim_3.out  sim_4.out  sim_5.out  sim_6.out  sim_7.out  sim_8.out  sim_9.out
$ more output/*
::::::::::::::
output/sim_0.out
::::::::::::::
      44
::::::::::::::
output/sim_1.out
::::::::::::::
      55
...
::::::::::::::
output/sim_9.out
::::::::::::::
      82

Part 3: Analyzing results of a parallel ensemble

After all the parallel simulations in an ensemble run have completed, its typically necessary to gather and analyze their results with some kind of post-processing analysis program or script. p3.swift introduces such a postprocessing step. In this case, the files created by all of the parallel runs of simulation.sh will be averaged by by the trivial "analysis application" stats.sh:

part03.png
p3.swift
type file;

app (file o) simulation (int sim_steps, int sim_range, int sim_values)
{
  simulate "--timesteps" sim_steps "--range" sim_range "--nvalues" sim_values stdout=@filename(o);
}

app (file o) analyze (file s[])
{
  stats @filenames(s) stdout=@filename(o);
}

int nsim   = @toInt(@arg("nsim","10"));
int steps  = @toInt(@arg("steps","1"));
int range  = @toInt(@arg("range","100"));
int values = @toInt(@arg("values","5"));

file sims[];

foreach i in [0:nsim-1] {
  file simout <single_file_mapper; file=@strcat("output/sim_",i,".out")>;
  simout = simulation(steps,range,values);
  sims[i] = simout;
}

file stats<"output/average.out">;
stats = analyze(sims);

To run:

$ cd part03
$ swift p3.swift

Note that in p3.swift we expose more of the capabilities of the simulate.sh application to the simulation() app function:

app (file o) simulation (int sim_steps, int sim_range, int sim_values)
{
  simulate "--timesteps" sim_steps "--range" sim_range "--nvalues" sim_values stdout=@filename(o);
}

p3.swift also shows how to fetch application-specific values from the swift command line in a Swift script using @arg() which accepts a keyword-style argument and its default value:

int nsim   = @toInt(@arg("nsim","10"));
int steps  = @toInt(@arg("steps","1"));
int range  = @toInt(@arg("range","100"));
int values = @toInt(@arg("values","5"));

Now we can specify that more runs should be performed and that each should run for more timesteps, and produce more that one value each, within a specified range, using command line arguments placed after the Swift script name in the form -parameterName=value:

$ swift p3.swift -nsim=3 -steps=10 -values=4 -range=1000000

Swift 0.94.1 RC2 swift-r6895 cog-r3765

RunID: 20130827-1439-s3vvo809
Progress:  time: Tue, 27 Aug 2013 14:39:42 -0500
Progress:  time: Tue, 27 Aug 2013 14:39:53 -0500  Active:2  Stage out:1
Final status: Tue, 27 Aug 2013 14:39:53 -0500  Finished successfully:4

$ ls output/
average.out  sim_0.out  sim_1.out  sim_2.out
$ more output/*
::::::::::::::
output/average.out
::::::::::::::
651368
::::::::::::::
output/sim_0.out
::::::::::::::
  735700
  886206
  997391
  982970
::::::::::::::
output/sim_1.out
::::::::::::::
  260071
  264195
  869198
  933537
::::::::::::::
output/sim_2.out
::::::::::::::
  201806
  213540
  527576
  944233

Now try running (-nsim=) 100 simulations of (-steps=) 1 second each:

$ swift p3.swift -nsim=100 -steps=1
Swift 0.94.1 RC2 swift-r6895 cog-r3765

RunID: 20130827-1444-rq809ts6
Progress:  time: Tue, 27 Aug 2013 14:44:55 -0500
Progress:  time: Tue, 27 Aug 2013 14:44:56 -0500  Selecting site:79  Active:20  Stage out:1
Progress:  time: Tue, 27 Aug 2013 14:44:58 -0500  Selecting site:58  Active:20  Stage out:1  Finished successfully:21
Progress:  time: Tue, 27 Aug 2013 14:44:59 -0500  Selecting site:37  Active:20  Stage out:1  Finished successfully:42
Progress:  time: Tue, 27 Aug 2013 14:45:00 -0500  Selecting site:16  Active:20  Stage out:1  Finished successfully:63
Progress:  time: Tue, 27 Aug 2013 14:45:02 -0500  Active:15  Stage out:1  Finished successfully:84
Progress:  time: Tue, 27 Aug 2013 14:45:03 -0500  Finished successfully:101
Final status: Tue, 27 Aug 2013 14:45:03 -0500  Finished successfully:101

We can see from Swift’s "progress" status that the tutorial’s default sites.xml parameters for local execution allow Swift to run up to 20 application invocations concurrently on the login node. We’ll look at this in more detail in the next sections where we execute applications on the site’s compute nodes.

Running applications on Cray compute nodes with Swift

Part 4: Running a parallel ensemble on Cray compute nodes

p4.swift will run our mock "simulation" applications on Cray compute nodes. The script is similar to as p3.swift, but specifies that each simulation app invocation should additionally return the log file which the application writes to stderr.

Now when you run swift p4.swift you’ll see that two types output files will placed in the output/ directory: sim_N.out and sim_N.log. The log files provide data on the runtime environment of each app invocation. For example:

$ cat output/sim_0.log
Called as: /home/users/p01532/swift-cray-tutorial/app/simulate.sh: --timesteps 1 --range 100 --nvalues 5

Start time: Tue Aug 27 12:17:43 CDT 2013
Running on node: nid00018
Running as user: uid=61532(p01532) gid=61532 groups=61532

Simulation parameters:

bias=0
biasfile=none
initseed=none
log=yes
paramfile=none
range=100
scale=1
seedfile=none
timesteps=1
output width=8

Environment:

ALPS_APP_DEPTH=32
ASSEMBLER_X86_64=/opt/cray/cce/8.2.0.173/cray-binutils/x86_64-unknown-linux-gnu/bin/as
ASYNCPE_DIR=/opt/cray/xt-asyncpe/5.23.02
ASYNCPE_VERSION=5.23.02
...

To tell Swift to run the apps on compute nodes, we specify in the apps file that the apps should be executed on the raven site (instead of the localhost site). We can specify the location of each app in the third field of the apps file, with either an absolute pathname or the name of an executable to be located in PATH). Here we use the latter form:

$ cat apps
raven simulate simulate.sh
raven stats    stats.sh

You can experiment, for example, with an alternate version of stats.sh by specfying that app’s location explicitly:

$ cat apps
raven simulate simulate.sh
raven stats    /home/users/p01532/bin/my-alt-stats.sh

We can see that when we run many apps requesting a larger set of nodes (6), we are indeed running on the compute nodes:

$ swift p4.swift -nsim=1000 -steps=1
Swift 0.94.1 RC2 swift-r6895 cog-r3765

RunID: 20130827-1638-t23ax37a
Progress:  time: Tue, 27 Aug 2013 16:38:11 -0500
Progress:  time: Tue, 27 Aug 2013 16:38:12 -0500  Initializing:966
Progress:  time: Tue, 27 Aug 2013 16:38:13 -0500  Selecting site:499  Submitting:500  Submitted:1
Progress:  time: Tue, 27 Aug 2013 16:38:14 -0500  Selecting site:499  Stage in:1  Submitted:500
Progress:  time: Tue, 27 Aug 2013 16:38:16 -0500  Selecting site:499  Submitted:405  Active:95  Stage out:1
Progress:  time: Tue, 27 Aug 2013 16:38:17 -0500  Selecting site:430  Submitted:434  Active:66  Stage out:1  Finished successfully:69
Progress:  time: Tue, 27 Aug 2013 16:38:18 -0500  Selecting site:388  Submitted:405  Active:95  Stage out:1  Finished successfully:111
...
Progress:  time: Tue, 27 Aug 2013 16:38:30 -0500  Stage in:1  Submitted:93  Active:94  Finished successfully:812
Progress:  time: Tue, 27 Aug 2013 16:38:31 -0500  Submitted:55  Active:95  Stage out:1  Finished successfully:849
Progress:  time: Tue, 27 Aug 2013 16:38:32 -0500  Active:78  Stage out:1  Finished successfully:921
Progress:  time: Tue, 27 Aug 2013 16:38:34 -0500  Active:70  Stage out:1  Finished successfully:929
Progress:  time: Tue, 27 Aug 2013 16:38:37 -0500  Stage in:1  Finished successfully:1000
Progress:  time: Tue, 27 Aug 2013 16:38:38 -0500  Stage out:1  Finished successfully:1000
Final status: Tue, 27 Aug 2013 16:38:38 -0500  Finished successfully:1001

$ grep "on node:" output/*log | head
output/sim_0.log:Running on node: nid00063
output/sim_100.log:Running on node: nid00060
output/sim_101.log:Running on node: nid00061
output/sim_102.log:Running on node: nid00032
output/sim_103.log:Running on node: nid00060
output/sim_104.log:Running on node: nid00061
output/sim_105.log:Running on node: nid00032
output/sim_106.log:Running on node: nid00060
output/sim_107.log:Running on node: nid00061
output/sim_108.log:Running on node: nid00062

$ grep "on node:" output/*log | awk '{print $4}' | sort | uniq -c
    158 nid00032
    156 nid00033
    171 nid00060
    178 nid00061
    166 nid00062
    171 nid00063
$ hostname
raven
$ hostname -f
nid00008

Swift’s sites.xml configuration file allows many parameters to specify how jobs should be run on a given cluster. Consider for example that Raven has several queues, each with limitiations on the size of jobs that can be run in them. All Raven queues will only run 2 jobs per user at one. The Raven queue "small" will only allow up to 4 nodes per job and 1 hours of walltime per job. The following site.xml parameters will allow us to match this:

  <profile namespace="globus" key="queue">small</profile>
  <profile namespace="globus" key="slots">2</profile>
  <profile namespace="globus" key="maxNodes">4</profile>
  <profile namespace="globus" key="nodeGranularity">4</profile>

To run large jobs, we can specify:

  <profile namespace="globus" key="queue">medium</profile>
  <profile namespace="globus" key="slots">2</profile>
  <profile namespace="globus" key="maxNodes">8</profile>
  <profile namespace="globus" key="nodeGranularity">8</profile>
  <profile namespace="karajan" key="jobThrottle">50.0</profile>
  <profile namespace="globus" key="maxTime">21600</profile>
  <profile namespace="globus" key="lowOverAllocation">10000</profile>
  <profile namespace="globus" key="highOverAllocation">10000</profile>

This will enable 512 Swift apps (2 x 8 x 32) to run concurrently within 2 8-node jobs on Raven’s 32-core nodes. It results in the following two PBS jobs submitted by Swift to "provision" compute nodes to run thousands of apps, 512 at a time:

$ qstat -u $USER

Job ID          Username Queue    Jobname    SessID NDS TSK Memory Time  S Time
--------------- -------- -------- ---------- ------ --- --- ------ ----- - -----
288637.sdb      p01532   medium   B0827-2703    --    8 256    --  05:59 Q   --
288638.sdb      p01532   medium   B0827-2703    --    8 256    --  05:59 Q   --

The following section is a summary of the important sites.xml attributes for running apps on Cray systems. Many of these attributes can be set the same for all Swift users of a given system; only a few of the attributes need be overridden by users. We explain these attributes in detail here to show the degree of control afforded by Swift over application execution. Most users will use templates for a given Cray system, only changing a few parameters to meet any unique needs of their application workflows.

The additional attributes in the sites.xml file (described here without their XML formatting) specify that Swift should run applications on Raven in the following manner:

execution provider coaster, jobmanager local:pbs specifies that Swift should run apps using its "coaster" provider, which submits "pilot jobs" using qsub. These pilot jobs hold on to compute nodes and allow Swift to run many app invocations within a single job. This mechanism is described in this paper from UCC-2011.

profile tags specify additional attributes for the execution provider. (A "provider" is like a driver which knows how to handle site-specific aspects of app execution). The attributes are grouped into various "namespaces", but we can ignore this for now).

The env key PATHPREFIX specifies that our tutorial app directory (../app) will be placed at the front of PATH to locate the app on the compute node.

queue small specifies that pilot (coaster) jobs to run apps will be submitted to Raven’s small queue.

providerAttributes pbs.aprun;pbs.mpp;depth=32 specifies some Cray-specific attributes: that jobs should use Cray-specific PBS "mpp" resource attributes (eg mppwidth and mppnppn) and an mppdepth of 32 (because we will be running one coaster process per node, and Raven’s XE6 dual IL-16 nodes have a depth of 32 processing elements (PEs).

jobsPerNode 32 tells Swift that each coaster should run up to 32 concurrent apps. This can be reduced to place fewer apps per node, eg if each app needs more memory (or, rarely, greater than 32, e.g. if the apps are IO-bound or for benchmark experiments, etc).

slots 2 specifies that Swift will run up to 2 concurrent PBS jobs, and maxNodes 1 specifies that each of these jobs will request only 1 compute node.

maxWallTime 00:01:00 specifies that Swift should allow each app to run for up to one minute of walltime within the larger pilot job. In this example Swift will dynamically determine the total PBS walltime needed for the pilot job, but this can be specified manually using attributes maxtime along with highOverAllocation and lowOverAllocation.

jobThrottle 3.20 specifies that Swift should allow up to 320 apps to run on the raven site at once. This is typically set to a number greater than or equal to the number of slots x compute nodes x apps per node (jobsPerNode attribute).

initialscore 10000 is specified to override Swift’s automatic throttling, and forces an actual throttle value of approximately (specifically 1 over) jobThrottle * 100 to be used.

The last two attributes specify where and how Swift should perform data management. workdirectory /lus/scratch/{env.USER}/swiftwork specifies where the Swift "application execution sanbox directory" used for each app will be located. In some situations this can be a directory local to the compute node (eg, for Cray systems, /dev/shm or /tmp, if those are writable by user jobs and the nodes have sufficient space in these RAM-based filesystems).

Finally, stagingMethod sfs specifies that Swift will copy data to and from the shared file system to the application sandbox directories.

Plotting run activity

The tutorial bin directory in your PATH provides a script plot.sh to plot the progress of a Swift script. It generates two image files: activeplot.png, which shows the number of active jobs over time, and cumulativeplot.png, which shows the total number of app calls completed as the Swift script progresses.

After each swift run, a log file will be created called partNN-<YYYYmmdd>-<hhmm>-<random>.log. Once you have identified the log file name, run the command ./plot.sh <logfile>` (where logfile is the most recent Swift run log) to generate the plots for that specific run. For example:

$ ls -lt *.log | head
-rw-r--r-- 1 p01532 61532 2237693 Aug 26 12:45 p4-20130826-1244-kmos0d87.log
-rw-r--r-- 1 p01532 61532    1008 Aug 26 12:44 swift.log
-rw-r--r-- 1 p01532 61532 5345345 Aug 26 12:44 p4-20130826-1243-10u2qdbd.log
-rw-r--r-- 1 p01532 61532  357687 Aug 26 12:00 p4-20130826-1159-j01p4lu0.log
...
$ plot.sh p4-20130826-1244-kmos0d87.log

This yields plots like:

width=700
width=700
Note Because systems like Raven are often firewalled, you may need to use scp to pull these image files back to a system on which you can view them with a browser or preview tool.

Part 5: Controlling the compute-node pools where applications run

In this section we’ll use the script p5.swift, very similar to p4.swift of the prior section, to show how we can route apps to specific sites, and also how we can make multiple pools of resources (on the same or on different computer systems) available to run a single Swift script.

First, lets specify that the analysis app stats.sh should be run on the local login node instead of on the cluster. This is done simply by change the site name field of the analyze app in the apps file:

$ cat apps
raven     simulate  simulate.sh
localhost stats     stats.sh

Running this with swift p5.swift we see:

$ grep "on node:" output/*.log
output/average.log:Running on node: raven
output/sim_0.log:Running on node: nid00029
output/sim_1.log:Running on node: nid00029
output/sim_2.log:Running on node: nid00029
output/sim_3.log:Running on node: nid00029
output/sim_4.log:Running on node: nid00029
output/sim_5.log:Running on node: nid00029
output/sim_6.log:Running on node: nid00029
output/sim_7.log:Running on node: nid00029
output/sim_8.log:Running on node: nid00029
output/sim_9.log:Running on node: nid00029

Now lets make further use of Swift’s ability to route specific apps to specific pools of resources. The Cray Raven system has two node types, XE6 32-core 2 x IL-16, and XK7 16-core 1 x IL-16 plus one GPU. Each "pool" of nodes has different queue characteristics. We can define these differences to Swift as two separate pools, and then spread the load of executing a large ensemble of simulations across all the pools. (And we’ll continue to run the analysis script on a third pool, comprising the single login host.)

We use the following apps file:

$ cat multipools
raven     simulate simulate.sh
ravenGPU  simulate simulate.sh
localhost stats    stats.sh

and we adjust the sites file to specify 4-node jobs in the Raven pool:

    <profile namespace="globus" key="maxNodes">4</profile>
    <profile namespace="globus" key="nodeGranularity">4</profile>

This results in these PBS jobs:

p01532@raven:~> qstat -u $USER

Job ID          Username Queue    Jobname    SessID NDS TSK Memory Time  S Time
--------------- -------- -------- ---------- ------ --- --- ------ ----- - -----
288687.sdb      p01532   small    B0827-3406   9919   4 128    --  00:59 R 00:00
288688.sdb      p01532   gpu_node B0827-3406   9931   6  96    --  00:59 R 00:00

  1. and achieves the following parallelism (of about 224 concurrent app tasks): 

$ swift -tc.file multipools p5.swift -nsim=1000 -steps=3
Swift 0.94.1 RC2 swift-r6895 cog-r3765

RunID: 20130827-1829-o3h6mht5
Progress:  time: Tue, 27 Aug 2013 18:29:31 -0500
Progress:  time: Tue, 27 Aug 2013 18:29:32 -0500  Initializing:997
Progress:  time: Tue, 27 Aug 2013 18:29:34 -0500  Selecting site:774  Submitting:225  Submitted:1
Progress:  time: Tue, 27 Aug 2013 18:29:35 -0500  Selecting site:774  Stage in:1  Submitted:225
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Progress:  time: Tue, 27 Aug 2013 18:29:39 -0500  Selecting site:774  Submitted:2  Active:223  Stage out:1
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Progress:  time: Tue, 27 Aug 2013 18:29:53 -0500  Selecting site:54  Submitted:2  Active:223  Stage out:1  Finished successfully:720
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Progress:  time: Tue, 27 Aug 2013 18:29:55 -0500  Submitted:3  Active:216  Stage out:1  Finished successfully:780
Progress:  time: Tue, 27 Aug 2013 18:29:56 -0500  Stage in:1  Active:143  Finished successfully:856
Progress:  time: Tue, 27 Aug 2013 18:29:57 -0500  Active:38  Stage out:1  Finished successfully:961
Progress:  time: Tue, 27 Aug 2013 18:29:58 -0500  Active:8  Stage out:1  Finished successfully:991
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Progress:  time: Tue, 27 Aug 2013 18:30:06 -0500  Stage out:1  Finished successfully:1000
Final status: Tue, 27 Aug 2013 18:30:07 -0500  Finished successfully:1001

Part 6: Specifying more complex workflow patterns

p6.swift expands the workflow pattern of p4.swift to add additional stages to the workflow. Here, we generate a dynamic seed value that will be used by all of the simulations, and for each simulation, we run an pre-processing application to generate a unique "bias file". This pattern is shown below, followed by the Swift script.

part06.png
p6.swift
type file;

# app() functions for application programs to be called:

app (file out) genseed (int nseeds)
{
  genseed "-r" 2000000 "-n" nseeds stdout=@out;
}

app (file out) genbias (int bias_range, int nvalues)
{
  genbias "-r" bias_range "-n" nvalues stdout=@out;
}

app (file out, file log) simulation (int timesteps, int sim_range,
                                     file bias_file, int scale, int sim_count)
{
  simulate "-t" timesteps "-r" sim_range "-B" @bias_file "-x" scale
           "-n" sim_count stdout=@out stderr=@log;
}

app (file out, file log) analyze (file s[])
{
  stats @filenames(s) stdout=@out stderr=@log;
}

# Command line arguments

int  nsim  = @toInt(@arg("nsim",   "10"));  # number of simulation programs to run
int  steps = @toInt(@arg("steps",  "1"));   # number of timesteps (seconds) per simulation
int  range = @toInt(@arg("range",  "100")); # range of the generated random numbers
int  values = @toInt(@arg("values", "10"));  # number of values generated per simulation

# Main script and data

tracef("\n*** Script parameters: nsim=%i range=%i num values=%i\n\n", nsim, range, values);

file seedfile<"output/seed.dat">;        # Dynamically generated bias for simulation ensemble
seedfile = genseed(1);

int seedval = readData(seedfile);
tracef("Generated seed=%i\n", seedval);

file sims[];                      # Array of files to hold each simulation output

foreach i in [0:nsim-1] {
  file biasfile <single_file_mapper; file=@strcat("output/bias_",i,".dat")>;
  file simout   <single_file_mapper; file=@strcat("output/sim_",i,".out")>;
  file simlog   <single_file_mapper; file=@strcat("output/sim_",i,".log")>;
  biasfile = genbias(1000, 20);
  (simout,simlog) = simulation(steps, range, biasfile, 1000000, values);
  sims[i] = simout;
}

file stats_out<"output/average.out">;
file stats_log<"output/average.log">;
(stats_out,stats_log) = analyze(sims);

Note that the workflow is based on data flow dependencies: each simulation depends on the seed value, calculated in these two dependent statements:

seedfile = genseed(1);
int seedval = readData(seedfile);

and on the bias file, computed and then consumed in these two dependent statements:

  biasfile = genbias(1000, 20);
  (simout,simlog) = simulation(steps, range, biasfile, 1000000, values);

To run:

$ cd ../part06
$ swift p6.swift

The default parameters result in the following execution log:

$ swift p6.swift
Swift 0.94.1 RC2 swift-r6895 cog-r3765

RunID: 20130827-1917-jvs4gqm5
Progress:  time: Tue, 27 Aug 2013 19:17:56 -0500

*** Script parameters: nsim=10 range=100 num values=10

Progress:  time: Tue, 27 Aug 2013 19:17:57 -0500  Stage in:1  Submitted:10
Generated seed=382537
Progress:  time: Tue, 27 Aug 2013 19:17:59 -0500  Active:9  Stage out:1  Finished successfully:11
Final status: Tue, 27 Aug 2013 19:18:00 -0500  Finished successfully:22

which produces the following output:

$ ls -lrt output
total 264
-rw-r--r-- 1 p01532 61532     9 Aug 27 19:17 seed.dat
-rw-r--r-- 1 p01532 61532   180 Aug 27 19:17 bias_9.dat
-rw-r--r-- 1 p01532 61532   180 Aug 27 19:17 bias_8.dat
-rw-r--r-- 1 p01532 61532   180 Aug 27 19:17 bias_7.dat
-rw-r--r-- 1 p01532 61532   180 Aug 27 19:17 bias_6.dat
-rw-r--r-- 1 p01532 61532   180 Aug 27 19:17 bias_5.dat
-rw-r--r-- 1 p01532 61532   180 Aug 27 19:17 bias_4.dat
-rw-r--r-- 1 p01532 61532   180 Aug 27 19:17 bias_3.dat
-rw-r--r-- 1 p01532 61532   180 Aug 27 19:17 bias_2.dat
-rw-r--r-- 1 p01532 61532   180 Aug 27 19:17 bias_1.dat
-rw-r--r-- 1 p01532 61532   180 Aug 27 19:17 bias_0.dat
-rw-r--r-- 1 p01532 61532    90 Aug 27 19:17 sim_9.out
-rw-r--r-- 1 p01532 61532 14897 Aug 27 19:17 sim_9.log
-rw-r--r-- 1 p01532 61532 14897 Aug 27 19:17 sim_8.log
-rw-r--r-- 1 p01532 61532    90 Aug 27 19:17 sim_7.out
-rw-r--r-- 1 p01532 61532    90 Aug 27 19:17 sim_6.out
-rw-r--r-- 1 p01532 61532 14897 Aug 27 19:17 sim_6.log
-rw-r--r-- 1 p01532 61532    90 Aug 27 19:17 sim_5.out
-rw-r--r-- 1 p01532 61532 14897 Aug 27 19:17 sim_5.log
-rw-r--r-- 1 p01532 61532    90 Aug 27 19:17 sim_4.out
-rw-r--r-- 1 p01532 61532 14897 Aug 27 19:17 sim_4.log
-rw-r--r-- 1 p01532 61532 14897 Aug 27 19:17 sim_1.log
-rw-r--r-- 1 p01532 61532    90 Aug 27 19:18 sim_8.out
-rw-r--r-- 1 p01532 61532 14897 Aug 27 19:18 sim_7.log
-rw-r--r-- 1 p01532 61532    90 Aug 27 19:18 sim_3.out
-rw-r--r-- 1 p01532 61532 14897 Aug 27 19:18 sim_3.log
-rw-r--r-- 1 p01532 61532    90 Aug 27 19:18 sim_2.out
-rw-r--r-- 1 p01532 61532 14898 Aug 27 19:18 sim_2.log
-rw-r--r-- 1 p01532 61532    90 Aug 27 19:18 sim_1.out
-rw-r--r-- 1 p01532 61532    90 Aug 27 19:18 sim_0.out
-rw-r--r-- 1 p01532 61532 14897 Aug 27 19:18 sim_0.log
-rw-r--r-- 1 p01532 61532     9 Aug 27 19:18 average.out
-rw-r--r-- 1 p01532 61532 14675 Aug 27 19:18 average.log

Each sim_N.out file is the sum of its bias file plus newly "simulated" random output scaled by 1,000,000:

$ cat output/bias_0.dat
     302
     489
      81
     582
     664
     290
     839
     258
     506
     310
     293
     508
      88
     261
     453
     187
      26
     198
     402
     555

$ cat output/sim_0.out
64000302
38000489
32000081
12000582
46000664
36000290
35000839
22000258
49000506
75000310

We produce 20 values in each bias file. Simulations of less than that number of values ignore the unneeded number, while simualtions of more than 20 will use the last bias number for all remoaining values past 20. As an exercise, adjust the code to produce the same number of bias values as is needed for each simulation. As a further exercise, modify the script to generate a unique seed value for each simulation, which is a common practice in ensemble computations.