Using Condor An Introduction ICE 2011

1http://www.cs.wisc.edu/condor

Using Condor An Introduction

ICE 2011


The Condor Project (Established ‘85)

Distributed High Throughput Computing research performed by a team of ~35 faculty, full time staff and students.


Condor is a batch computing System

› High Throughput (HTC), Not High Performance (HPC)

› Originated from desktop cycle scavanging


Cycle Scavanging› Good metaphor even for clusters

which are dedicated


Cycles Are Cheap!› Amazon.com EC2: 10 cents/hour› Academic computing: 4 cents/hour› Opportunistic computing: even

cheaper


› Total Usage between 2011-07-21 and 2011-07-22 › Group Usage Summary User Hours Pct Demand› -- ------------------------------ ---------- ----- ------ › 1 Physics_Balantekin 6224.8 16.8% 46.4% › 2 ChE_dePablo 5932.3 16.0% 100.0% › 3 Astronomy_Friedman 5764.0 15.5% 0.0% › 4 Economics_Traczynski 4218.4 11.4% 61.1% › 5 Chemistry_Skinner 4186.5 11.3% 45.4% › 6 BMRB 1731.5 4.7% 15.6%› 7 Physics_Petriello 1708.3 4.6% 7.1% › 8 CMS 1494.6 4.0% 31.8% › 9 LMCG 1444.4 3.9% 27.3% › 10 Biochem_Sussman 996.3 2.7% 3.6% › 11 Atlas 847.9 2.3% 79.9% › 12 MSE 812.5 2.2% 2.9%

› --------------------------------- ---------- ------ ------ › TOTAL 37126.7 100.0% 100.0%



HTC in a nutshell› Work is divided into “jobs”› Cluster of machines is divided into

“machine”

› HTC runs jobs on machines.


Condor Tutorial


Definitions› Job

The Condor representation of your work

› Machine The Condor representation of

computers and that can perform the work

› Match Making Matching a job with a machine

“Resource”


JobJobs state their requirements and

preferences:I need a Linux/x86 platformI need the machine at least 500 MbI prefer a machine with more memory


MachineMachines state their requirements

and preferences:Run jobs only when there is no keyboard

activityI prefer to run Frieda’s jobsI am a machine in the econ departmentNever run jobs belonging to Dr. Smith


The Magic of Matchmaking

› Jobs and machines state their requirements and preferences

› Condor matches jobs with machinesbased on requirements and

preferences


Getting Started:Submitting Jobs to

Condor› Overview: Choose a “Universe” for your job Make your job “batch-ready” Create a submit description file Run condor_submit to put your job in

the queue


1. Choose the “Universe”› Controls how Condor handles jobs› Choices include:

Vanilla Standard Grid Java Parallel VM


Using the Vanilla Universe

• The Vanilla Universe:– Allows running

almost any “serial” job

– Provides automatic file transfer, etc.

– Like vanilla ice cream• Can be used in just

about any situation


2. Make your job batch-ready

Must be able to run in the background

• No interactive input

• No GUI/window clicks

• No music ;^)


Make your job batch-ready (continued)…

Job can still use STDIN, STDOUT, and STDERR (the keyboard and the screen), but files are used for these instead of the actual devices

Similar to UNIX (or DOS) shell:•$ ./myprogram <input.txt >output.txt


3. Create a Submit Description File

› A plain ASCII text file› Condor does not care about file extensions› Tells Condor about your job:

Which executable, universe, input, output and error files to use, command-line arguments, environment variables, any special requirements or preferences (more on this later)

› Can describe many jobs at once (a “cluster”), each with different input, arguments, output, etc.


Simple Submit Description File

# Simple condor_submit input file# (Lines beginning with # are comments)# NOTE: the words on the left side are not# case sensitive, but filenames are!Universe = vanillaExecutable = my_jobOutput = output.txt Queue


4. Run condor_submit› You give condor_submit the name

of the submit file you have created: condor_submit my_job.submit

› condor_submit: Parses the submit file, checks for

errors Creates a “ClassAd” that describes

your job(s) Puts job(s) in the Job Queue


The Job Queue› condor_submit sends your job’s

ClassAd(s) to the schedd› The schedd (more details later):

Manages the local job queue Stores the job in the job queue

• Atomic operation, two-phase commit• “Like money in the bank”

› View the queue with condor_q


Examplecondor_submit and

condor_q% condor_submit my_job.submitSubmitting job(s).1 job(s) submitted to cluster 1.

% condor_q

-- Submitter: perdita.cs.wisc.edu : <128.105.165.34:1027> : ID OWNER SUBMITTED RUN_TIME ST PRI SIZE CMD

1.0 frieda 6/16 06:52 0+00:00:00 I 0 0.0 my_job

1 jobs; 1 idle, 0 running, 0 held

%


Input, output & error files› Controlled by submit file settings

› You can define the job’s standard input, standard output and standard error: Read job’s standard input from “input_file”:

• Input = input_file• Shell equivalent: program <input_file

Write job’s standard ouput to “output_file”:• Output = output_file• Shell equivalent: program >output_file

Write job’s standard error to “error_file”:• Error = error_file• Shell equivalent: program 2>error_file


Email about your job• Condor sends email about job

events to the submitting user• Specify “notification” in your

submit file to control which events:

Notification = completeNotification = neverNotification = errorNotification = always

Default


Feedback on your job› Create a log of job events› Add to submit description file:

log = sim.log

› Becomes the Life Story of a Job Shows all events in the life of a job Always have a log file


Sample Condor User Log

000 (0001.000.000) 05/25 19:10:03 Job submitted from host: <128.105.146.14:1816>...001 (0001.000.000) 05/25 19:12:17 Job executing on host: <128.105.146.14:1026>...005 (0001.000.000) 05/25 19:13:06 Job terminated.

(1) Normal termination (return value 0)...


Example Submit Description File With

Logging# Example condor_submit input file# (Lines beginning with # are comments)# NOTE: the words on the left side are not# case sensitive, but filenames are!Universe = vanillaExecutable = /home/frieda/condor/my_job.condorLog = my_job.log ·Job log (from Condor)Input = my_job.in ·Program’s standard inputOutput = my_job.out ·Program’s standard outputError = my_job.err ·Program’s standard errorArguments = -a1 -a2 ·Command line argumentsInitialDir = /home/frieda/condor/runQueue


Let’s run a job› First, need a terminal emulator

http://www.putty.org• (or similar)

› Login to chopin.cs.wisc.edu as cguserXX, and the given password

http://www.putty.org/


Logged In?source /scratch/setup.shmkdir /scratch/your_namecd /scratch/your_name

› condor_q› condor_status


Create submit file› nano submit.your_initials

• universe = vanilla• executable = /bin/echo• Arguments = hello world• Should_transfer_files = yes• When_to_transfer_output = on_exit• Output = out• Log = log• queue


And submit it…› condor_submit submit.your_initials

› (wait… remember the HTC bit?)

› Condor_q xx› cat output


A matlab example› #!/s/std/bin/octave –qf

› printf “Hello World\n”;

› Save as Hello.o› Chmod 0755 Hello.o› ./Hello.o


submit file› nano submit.your_initials

• universe = vanilla• executable = Hello.o• Should_transfer_files = yes• When_to_transfer_output = on_exit• Output = out• Log = log• queue


“Clusters” and “Processes”› If your submit file describes multiple jobs, we call

this a “cluster”› Each cluster has a unique “cluster number”› Each job in a cluster is called a “process”

Process numbers always start at zero› A Condor “Job ID” is the cluster number, a period,

and the process number (i.e. 2.1) A cluster can have a single process

• Job ID = 20.0 ·Cluster 20, process 0 Or, a cluster can have more than one process

• Job ID: 21.0, 21.1, 21.2 ·Cluster 21, process 0, 1, 2


Submit File for a Cluster# Example submit file for a cluster of 2 jobs

# with separate input, output, error and log filesUniverse = vanillaExecutable = my_jobArguments = -x 0log = my_job_0.logInput = my_job_0.inOutput = my_job_0.outError = my_job_0.errQueue ·Job 2.0 (cluster 2, process 0)Arguments = -x 1log = my_job_1.logInput = my_job_1.inOutput = my_job_1.outError = my_job_1.errQueue ·Job 2.1 (cluster 2, process 1)


% condor_submit my_job.submit-file

Submitting job(s).

2 job(s) submitted to cluster 2.% condor_q-- Submitter: perdita.cs.wisc.edu : <128.105.165.34:1027> :

ID OWNER SUBMITTED RUN_TIME ST PRI SIZE CMD 1.0 frieda 4/15 06:52 0+00:02:11 R 0 0.0 my_job –a1 –a2

2.0 frieda 4/15 06:56 0+00:00:00 I 0 0.0 my_job –x 0 2.1 frieda 4/15 06:56 0+00:00:00 I 0 0.0 my_job –x 13 jobs; 2 idle, 1 running, 0 held

%

Submitting The Job


Organize your files and directories for big runs

› Create subdirectories for each “run” run_0, run_1, … run_599

› Create input files in each of these run_0/simulation.in run_1/simulation.in … run_599/simulation.in

› The output, error & log files for each job will be created by Condor from your job’s output


Submit Description File for 600 Jobs

# Cluster of 600 jobs with different directoriesUniverse = vanillaExecutable = simLog = simulation.log...Arguments = -x 0InitialDir = run_0 ·Log, input, output & error files -> run_0Queue ·Job 3.0 (Cluster 3, Process 0)

Arguments = -x 1InitialDir = run_1 ·Log, input, output & error files -> run_1Queue ·Job 3.1 (Cluster 3, Process 1)

·Do this 598 more times…………


Submit File for a Big Cluster of Jobs

› We just submitted 1 cluster with 600 processes

› All the input/output files will be in different directories

› The submit file is pretty unwieldy (over 1200 lines)

› Isn’t there a better way?


Submit File for a Big Cluster of Jobs (the better

way) #1› We can queue all 600 in 1 “Queue” command Queue 600

› Condor provides $(Process) and $(Cluster) $(Process) will be expanded to the

process number for each job in the cluster• 0, 1, … 599

$(Cluster) will be expanded to the cluster number• Will be 4 for all jobs in this cluster


Submit File for a Big Cluster of Jobs (the better

way) #2› The initial directory for each job can

be specified using $(Process) InitialDir = run_$(Process) Condor will expand these to “run_0”,

“run_1”, … “run_599” directories› Similarly, arguments can be variable

Arguments = -x $(Process) Condor will expand these to “-x 0”, “-x 1”, … “-x 599”


Better Submit File for 600 Jobs

# Example condor_submit input file that defines# a cluster of 600 jobs with different directoriesUniverse = vanillaExecutable = my_jobLog = my_job.logInput = my_job.inOutput = my_job.outError = my_job.errArguments = –x $(Process) ·–x 0, -x 1, … -x 599InitialDir = run_$(Process) ·run_0 … run_599Queue 600 ·Jobs 4.0 … 4.599


Now, we submit it…$ condor_submit my_job.submitSubmitting

job(s) ...............................................................................................................................................................................................................................................................

Logging submit event(s) ...............................................................................................................................................................................................................................................................

600 job(s) submitted to cluster 4.


And, Check the queue$ condor_q-- Submitter: x.cs.wisc.edu : <128.105.121.53:510> : x.cs.wisc.eduID OWNER SUBMITTED RUN_TIME ST PRI SIZE CMD4.0 frieda 4/20 12:08 0+00:00:05 R 0 9.8 my_job -arg1 –x 04.1 frieda 4/20 12:08 0+00:00:03 I 0 9.8 my_job -arg1 –x 14.2 frieda 4/20 12:08 0+00:00:01 I 0 9.8 my_job -arg1 –x 24.3 frieda 4/20 12:08 0+00:00:00 I 0 9.8 my_job -arg1 –x 3...4.598 frieda 4/20 12:08 0+00:00:00 I 0 9.8 my_job -arg1 –x 5984.599 frieda 4/20 12:08 0+00:00:00 I 0 9.8 my_job -arg1 –x 599

600 jobs; 599 idle, 1 running, 0 held


Removing jobs› If you want to remove a job from

the Condor queue, you use condor_rm

› You can only remove jobs that you own

› Privileged user can remove any jobs “root” on UNIX “administrator” on Windows


Removing jobs (continued)

› Remove an entire cluster: condor_rm 4 ·Removes the whole

cluster

› Remove a specific job from a cluster: condor_rm 4.0 ·Removes a single job

› Or, remove all of your jobs with “-a” condor_rm -a ·Removes all jobs /

clusters


Submit cluster of 10 jobs

› nano submit• universe = vanilla• executable = /bin/echo• Should_transfer_files = yes• When_to_transfer_output = on_exit• Arguments = hello world $(PROCESS)• Output = out.$(PROCESS)• Log = log• Queue 10


And submit it…› condor_submit submit

› (wait…)› Condor_q xx› cat log› cat output.yy


My new jobs run for 20 days…

› What happens when a job is forced off it’s CPU? Preempted by higher priority

user or job Vacated because of user

activity› How can I add fault

tolerance to my jobs?


Condor’s Standard Universe to the rescue!› Support for transparent process

checkpoint and restart› Remote system calls (remote

I/O)Your job can read / write files as if they were local


Remote System Calls inthe Standard Universe

› I/O system calls are trapped and sent back to the submit machineExamples: open a file, write to a file

› No source code changes typically required

› Programming language independent


Process Checkpointing in the

Standard Universe› Condor’s process checkpointing provides a mechanism to automatically save the state of a job

› The process can then be restarted from right where it was checkpointed After preemption, crash, etc.


Checkpointing:Process Starts

checkpoint: the entire state of a program, saved in a file CPU registers, memory image, I/O

time


Checkpointing:Process Checkpointed

time

1 2 3


Checkpointing:Process Killed

time

3

3

Killed!


Checkpointing:Process Resumed

time

3

3

goodput badput goodput


When will Condor checkpoint your job?

› Periodically, if desired For fault tolerance

› When your job is preempted by a higher priority job

› When your job is vacated because the execution machine becomes busy

› When you explicitly run condor_checkpoint, condor_vacate, condor_off or condor_restart command


Making the Standard Universe Work

› The job must be relinked with Condor’s standard universe support library

› To relink, place condor_compile in front of the command used to link the job:% condor_compile gcc -o myjob myjob.c

- OR -% condor_compile f77 -o myjob filea.f fileb.f

- OR -% condor_compile make –f MyMakefile


Limitations of the Standard Universe

› Condor’s checkpointing is not at the kernel level. Standard Universe the job may not:

• Fork()• Use kernel threads• Use some forms of IPC, such as pipes and shared

memory› Must have access to source code to

relink› Many typical scientific jobs are OK


Submitting Std uni job› #include <stdio.h>› int main(int argc, char **argv) {› int i;

for(i = 0 ; i < 10000000; i++) {}

› }


And submit…› condor_compile gcc –o foo foo.c

-- Change "vanilla" to "standard" -- Change "/bin/echo" to "foo" (or

above)


My jobs have have dependencies…

Can Condor help solve my dependency problems?


Condor Universes:Scheduler and Local

› Scheduler Universe Plug in a meta-scheduler Developed for DAGMan (more later) Similar to Globus’s fork job manager

› Local Very similar to vanilla, but jobs run on

the local host Has more control over jobs than

scheduler universe


DAGMan› Directed Acyclic Graph

Manager› DAGMan allows you to specify the

dependencies between your Condor jobs, so it can manage them automatically for you.

› (e.g., “Don’t run job “B” until job “A” has completed successfully.”)


What is a DAG?› A DAG is the data structure

used by DAGMan to represent these dependencies.

› Each job is a “node” in the DAG.

› Each node can have any number of “parent” or “children” nodes – as long as there are no loops!

Job A

Job B

Job C

Job D


Defining a DAG› A DAG is defined by a .dag file, listing each of

its nodes and their dependencies:# diamond.dagJob A a.subJob B b.subJob C c.subJob D d.subParent A Child B CParent B C Child D

› each node will run the Condor job specified by its accompanying Condor submit file

Job A

Job B Job C

Job D


Submitting a DAG› To start your DAG, just run condor_submit_dag with your .dag file, and Condor will start a personal DAGMan daemon which to begin running your jobs:% condor_submit_dag diamond.dag

› condor_submit_dag is run by the schedd DAGMan daemon itself is “watched” by

Condor, so you don’t have to


DAGMan

Running a DAG› DAGMan acts as a “meta-scheduler”,

managing the submission of your jobs to Condor based on the DAG dependencies.

CondorJobQueue

B C

D

A

A.dagFile


DAGMan

Running a DAG (cont’d)› DAGMan holds & submits jobs to the

Condor queue at the appropriate times.

CondorJobQueue D

B

C

B

A

C


Running a DAG (cont’d)› In case of a job failure, DAGMan continues until

it can no longer make progress, and then creates a “rescue” file with the current state of the DAG.

CondorJobQueue DAGMan

X

D

A

BRescue

File


Recovering a DAG› Once the failed job is ready to be re-run,

the rescue file can be used to restore the prior state of the DAG.

CondorJobQueue

RescueFile

CDAGMan D

A

B C


DAGMan

Recovering a DAG (cont’d)

› Once that job completes, DAGMan will continue the DAG as if the failure never happened.

CondorJobQueue

C

D

A

B

D


DAGMan

Finishing a DAG› Once the DAG is complete, the DAGMan

job itself is finished, and exits.

CondorJobQueue

C

D

A

B


Additional DAGMan Features

› Provides other handy features for job management… nodes can have PRE & POST scripts failed nodes can be automatically re-

tried a configurable number of times job submission can be “throttled”


What about Licensed Jobs?

› e.g. matlab Site license? matlab compiler Octave


Chirp› condor_chirp get_file remote local

› condor_chirp put_file local remote


General User Commands› condor_status View Pool Status

› condor_q View Job Queue› condor_submit Submit new Jobs› condor_rm Remove Jobs› condor_prio Intra-User Prios› condor_history Completed Job Info› condor_submit_dag Submit new DAG› condor_checkpoint Force a checkpoint› condor_compile Link Condor library


Statistical Bootstrap› Build up from the worker side out› The matlab/octave worker:› worker.m:

#!/s/octave/bin/octave -qload "subset" subset;subset = subset(floor(rand(10,1) .* 1000));

printf("%f ", mean(subset));


Run the worker alone› (won’t work – why?)› chmod 0755 worker.m› ./worker.m


Create the initial datadriver.m

#!/s/octave/bin/octave –qfdist_size = 100000;d = rand(dist_size, 1) .* 500;subset = d(floor(rand(1000,1) .*

100000));save "subset" subset;


Submit fileuniverse = vanillaexecutable = worker.mshould_transfer_files = truewhen_to_transfer_output = on_exittransfer_input_files = subsetoutput = mean.$(PROCESS)error = foolog = logqueue 10


And submit the job…› condor_submit submit


Add the submission tothe driver script…

#!/s/octave/bin/octave –qfdist_size = 100000;d = rand(dist_size, 1) .* 500;subset = d(floor(rand(1000,1) .* 100000));save "subset" subset;system("condor_submit submit");system("condor_wait log");


And run the driver!› ./driver.m


Master – Worker:Many very short jobs

› Condor doesn’t run short jobs well.› time needed to transmit the executable/data/results.› Condor doesn’t deal directly with parallel algorithms.› Can have the process on the user’s workstation generating› waves of “worker” jobs to run in parallel, but› each worker job must be scheduled anew in the Condor› pool, and› the master application has to handle all the details of› scheduling, rescheduling after faults, managing input and› outputs to workers, etc.› Master-Worker (MW) addresses these issues!


Master – Worker:› Master assigns tasks to the workers› Workers perform tasks, and report

results back to master› Workers do not communicate

(except through the master)› Simple!› Fault-tolerant› Dynamic› Programming model reusable

across many applications.


Master – Worker:

› Data common to all tasks is sent to workers only once

› (Try to) Retain workers until the whole computation is

› complete—don’t release them after a single task is done.

› These features make for much higher parallel efficiency.

› We now need to transmit much less data between master

› and workers.› We avoid the overhead of putting each task on the

condor› queue and waiting for it to be allocated to a

processor.


› Three abstractions in the master-worker paradigm: Master,Worker, and Task.

› The MW package encapsulates these abstractions› C++ abstract classes› User writes 10 functions (Templates and skeletons supplied› in distribution)› The MWized code will adapt transparently to the dynamic› and heterogeneous environment› The back side of MW interfaces to resource management


MW FunctionsMWMaster

get_userinfo()setup_initial_tasks()

› pack_worker_init_data()› act_on_completed_task()MWTask› (un)pack_work› (un)pack_result]MWWorker› unpack_worker_init_data()› execute_task()


But Wait, there’s more..› User-defined checkpointing of master. (Don’t lose the whole run

if the master crashes.)› (Rudimentary) Task Scheduling› MW assigns first task to first idle worker› Lists of tasks and workers can be arbitrarily ordered and

reordered› User can set task rescheduling policies› User-defined benchmarking› A (user-defined) task is sent to each worker upon initialization› By accumulating normalized task CPU time, MW computes› a performance statistic that is comparable between runs,› though the properties of the pool may differ between runs.


There’s an App for that..› MWFATCOP (Chen, Ferris, Linderoth) – A branch and cut code

› for linear integer programming› MWQAP (Anstreicher, Brixius, Goux, Linderoth) – A› branch-and-bound code for solving the quadratic assignment

problem› MWATR (Linderoth, Shapiro, Wright) – A trust-region-enhanced› cutting plane code for two-stage linear stochastic programming› and statistical verification of solution quality.› MWKNAP (Glankwamdee, Linderoth) – A simple branch-and-

bound knapsack solver› MWAND (Linderoth, Shen) – A nested decomposition-based› solver for multistage stochastic linear programming› MWSYMCOP (Linderoth, Margot, Thain) – An LP-based› branch-and-bound solver for symmetric integer programs


Other frameworks› CCTools group at Notre Dame

All Pairs WaveFront Makeflow


Condor and Big Data› Big Data driving web development› Web developments driving Big Data

› Big Data: Definition

› Like Condor for disks


In 2003…

http://labs.google.com/papers/gfs.html

http://labs.google.com/papers/mapreduce.html

http://labs.google.com/papers/gfs.html

http://labs.google.com/papers/mapreduce.html




Shortly thereafter…


Two main Hadoop parts


For more detailCondorWeek 2009 talk

Dhruba Borthakur

http://www.cs.wisc.edu/condor/CondorWeek2009/condor_presentations/borthakur-hadoop_univ_research.ppt



HDFS overview› Making POSIX distributed file

system go fast is easy…


HDFS overview› …If you get rid of the POSIX part› Remove

Random access Support for small files authentication In-kernel support


HDFS Overview› Add in

Data replication • (key for distributed systems)

Command line utilities


HDFS Architecture


HDFS Condor Integration

› HDFS Daemons run under master Management/control

› Input/Output files can be in hdfs:› Input = hdfs://some/pathname


Parallel convergence checking:

Another DAGman example› Evaluating a function at many

points

› Check for convergence -> retry

› Particle Swarm Optimization


Prepare

Compute Compute Compute Compute

Converge?

Done

Yes!No


Any Guesses?› Who has thoughts?

› Best to work from “inside out”

109


The job itself.#!/bin/sh###### random.sh

echo $RANDOMexit 0

110


The submit file› Any guesses?

111


The submit file# submitRandomuniverse = vanillaexecutable = random.shShould_transfer_files = yesWhen_to_transfer_output = on_exitoutput = outlog = logqueue

112


Next step: the inner DAG

113

First Last Node

Node0

Node1 Node2 Node3 Node4

Node11


The DAG file› Any guesses?

114


The inner DAG fileJob Node0 submitRandomJob Node1 submitRandomJob Node2 submitRandomJob Node3 submitRandomPARENT Node0 CHILD Node1PARENT Node0 CHILD Node2PARENT Node0 CHILD Node3Job Node11 submitRandomPARENT Node1, Node2, Node3 CHILD Node11

115


Inner DAG› Does this work?

› At least one iteration?

116


How to iterate› DAGman has simple control

structures (Makes it reliable)

› SUBDAGs!› Remember what happens if post

fails?117


The Outer Dag› Another Degenerate Dag

(But Useful!)

118

Post Script (with exit value)

SubDag (with retry)t


This one is easy!› Can you do it yourself?

119


The outer DAG file####### Outer.dag #############SUBDAG EXTERNAL A inner.dagSCRIPT POST A converge.shRETRY A 10#### converge.sh could look like

#!/bin/sh

echo "Checking convergence" >> convergeexit 1

120


Let’s run that…› condor_submit_dag outer.dag

› Does it work? How can you tell?

121


DAGman a bit verbose…$ condor_submit_dag outer.dag

-----------------------------------------------------------------------

File for submitting this DAG to Condor : submit.dag.condor.sub

Log of DAGMan debugging messages : submit.dag.dagman.out

Log of Condor library output : submit.dag.lib.out

Log of Condor library error messages : submit.dag.lib.err

Log of the life of condor_dagman itself : submit.dag.dagman.log

-no_submit given, not submitting DAG to Condor. You can do this with:

"condor_submit submit.dag.condor.sub"

-----------------------------------------------------------------------

-----------------------------------------------------------------------

File for submitting this DAG to Condor : outer.dag.condor.sub

Log of DAGMan debugging messages : outer.dag.dagman.out

Log of Condor library output : outer.dag.lib.out

Log of Condor library error messages : outer.dag.lib.err

Log of the life of condor_dagman itself : outer.dag.dagman.log

Submitting job(s).

Logging submit event(s).

1 job(s) submitted to cluster 721.

-----------------------------------------------------------------------

122


Debugging helps› Look in the user log file, “log”

› Look in the DAGman debugging log

› “foo”.dagman.out

123


What does converge.sh need

› Note the output files?

› How to make them unique?

› Add DAG variables to inner dag And submitRandom file

124


The submit file (again)# submitRandomuniverse = vanillaexecutable = random.shoutput = outlog = logqueue

125


The submit file# submitRandomuniverse = vanillaexecutable = random.shoutput = out.$(NodeNumber)log = logqueue

126


The inner DAG file (again)

Job Node0 submit_preJob Node1 submitRandomJob Node2 submitRandomJob Node3 submitRandomPARENT Node0 CHILD Node1PARENT Node0 CHILD Node2PARENT Node0 CHILD Node3Job Node11 submit_postPARENT Node1 CHILD Node11PARENT Node2 CHILD Node11PARENT Node3 CHILD Node11

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The inner DAG file (again)

Job Node0 submit_preJob Node1 submitRandomJob Node2 submitRandomJob Node3 submitRandom…VARS Node1 NodeNumber=“1”VARS Node2 NodeNumber=“2”VARS Node3 NodeNumber=“3”…

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Then converge.sh sees:$ ls out.*out.1 out.10 out.2 out.3 out.4 out.5 out.6 out.7 out.8 out.9

$

› And can act accordingly…

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Documents

Using Condor An Introduction ICE 2011