Using Condor An Introduction ICE 2011
description
Transcript of Using Condor An Introduction ICE 2011
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Using Condor An Introduction
ICE 2011
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The Condor Project (Established ‘85)
Distributed High Throughput Computing research performed by a team of ~35 faculty, full time staff and students.
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Condor is a batch computing System
› High Throughput (HTC), Not High Performance (HPC)
› Originated from desktop cycle scavanging
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Cycle Scavanging› Good metaphor even for clusters
which are dedicated
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Cycles Are Cheap!› Amazon.com EC2: 10 cents/hour› Academic computing: 4 cents/hour› Opportunistic computing: even
cheaper
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› 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%
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HTC in a nutshell› Work is divided into “jobs”› Cluster of machines is divided into
“machine”
› HTC runs jobs on machines.
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Condor Tutorial
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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”
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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
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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
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The Magic of Matchmaking
› Jobs and machines state their requirements and preferences
› Condor matches jobs with machinesbased on requirements and
preferences
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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
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1. Choose the “Universe”› Controls how Condor handles jobs› Choices include:
Vanilla Standard Grid Java Parallel VM
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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
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2. Make your job batch-ready
Must be able to run in the background
• No interactive input
• No GUI/window clicks
• No music ;^)
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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
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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.
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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
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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
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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
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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
%
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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
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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
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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
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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)...
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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
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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
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Logged In?source /scratch/setup.shmkdir /scratch/your_namecd /scratch/your_name
› condor_q› condor_status
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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
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And submit it…› condor_submit submit.your_initials
› (wait… remember the HTC bit?)
› Condor_q xx› cat output
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A matlab example› #!/s/std/bin/octave –qf
› printf “Hello World\n”;
› Save as Hello.o› Chmod 0755 Hello.o› ./Hello.o
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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
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“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
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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)
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% 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
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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
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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…………
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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?
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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
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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”
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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
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Now, we submit it…$ condor_submit my_job.submitSubmitting
job(s) ...............................................................................................................................................................................................................................................................
Logging submit event(s) ...............................................................................................................................................................................................................................................................
600 job(s) submitted to cluster 4.
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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
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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
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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
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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
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And submit it…› condor_submit submit
› (wait…)› Condor_q xx› cat log› cat output.yy
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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?
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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
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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
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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.
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Checkpointing:Process Starts
checkpoint: the entire state of a program, saved in a file CPU registers, memory image, I/O
time
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Checkpointing:Process Checkpointed
time
1 2 3
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Checkpointing:Process Killed
time
3
3
Killed!
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Checkpointing:Process Resumed
time
3
3
goodput badput goodput
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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
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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
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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
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Submitting Std uni job› #include <stdio.h>› int main(int argc, char **argv) {› int i;
for(i = 0 ; i < 10000000; i++) {}
› }
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And submit…› condor_compile gcc –o foo foo.c
-- Change "vanilla" to "standard" -- Change "/bin/echo" to "foo" (or
above)
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My jobs have have dependencies…
Can Condor help solve my dependency problems?
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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
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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.”)
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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
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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
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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
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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
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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
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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
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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
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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
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DAGMan
Finishing a DAG› Once the DAG is complete, the DAGMan
job itself is finished, and exits.
CondorJobQueue
C
D
A
B
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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”
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What about Licensed Jobs?
› e.g. matlab Site license? matlab compiler Octave
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Chirp› condor_chirp get_file remote local
› condor_chirp put_file local remote
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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
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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));
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Run the worker alone› (won’t work – why?)› chmod 0755 worker.m› ./worker.m
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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;
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Submit fileuniverse = vanillaexecutable = worker.mshould_transfer_files = truewhen_to_transfer_output = on_exittransfer_input_files = subsetoutput = mean.$(PROCESS)error = foolog = logqueue 10
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And submit the job…› condor_submit submit
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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");
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And run the driver!› ./driver.m
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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!
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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.
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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.
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› 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
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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()
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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.
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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
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Other frameworks› CCTools group at Notre Dame
All Pairs WaveFront Makeflow
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Condor and Big Data› Big Data driving web development› Web developments driving Big Data
› Big Data: Definition
› Like Condor for disks
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In 2003…
http://labs.google.com/papers/gfs.html
http://labs.google.com/papers/mapreduce.html
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Shortly thereafter…
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Two main Hadoop parts
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For more detailCondorWeek 2009 talk
Dhruba Borthakur
http://www.cs.wisc.edu/condor/CondorWeek2009/condor_presentations/borthakur-hadoop_univ_research.ppt
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HDFS overview› Making POSIX distributed file
system go fast is easy…
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HDFS overview› …If you get rid of the POSIX part› Remove
Random access Support for small files authentication In-kernel support
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HDFS Overview› Add in
Data replication • (key for distributed systems)
Command line utilities
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HDFS Architecture
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HDFS Condor Integration
› HDFS Daemons run under master Management/control
› Input/Output files can be in hdfs:› Input = hdfs://some/pathname
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Parallel convergence checking:
Another DAGman example› Evaluating a function at many
points
› Check for convergence -> retry
› Particle Swarm Optimization
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Prepare
Compute Compute Compute Compute
Converge?
Done
Yes!No
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Any Guesses?› Who has thoughts?
› Best to work from “inside out”
109
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The job itself.#!/bin/sh###### random.sh
echo $RANDOMexit 0
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The submit file› Any guesses?
111
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The submit file# submitRandomuniverse = vanillaexecutable = random.shShould_transfer_files = yesWhen_to_transfer_output = on_exitoutput = outlog = logqueue
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Next step: the inner DAG
113
First Last Node
Node0
Node1 Node2 Node3 Node4
Node11
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The DAG file› Any guesses?
114
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The inner DAG fileJob Node0 submitRandomJob Node1 submitRandomJob Node2 submitRandomJob Node3 submitRandomPARENT Node0 CHILD Node1PARENT Node0 CHILD Node2PARENT Node0 CHILD Node3Job Node11 submitRandomPARENT Node1, Node2, Node3 CHILD Node11
115
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Inner DAG› Does this work?
› At least one iteration?
116
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How to iterate› DAGman has simple control
structures (Makes it reliable)
› SUBDAGs!› Remember what happens if post
fails?117
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The Outer Dag› Another Degenerate Dag
(But Useful!)
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Post Script (with exit value)
SubDag (with retry)t
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This one is easy!› Can you do it yourself?
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The outer DAG file####### Outer.dag #############SUBDAG EXTERNAL A inner.dagSCRIPT POST A converge.shRETRY A 10#### converge.sh could look like
#!/bin/sh
echo "Checking convergence" >> convergeexit 1
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Let’s run that…› condor_submit_dag outer.dag
› Does it work? How can you tell?
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DAGman a bit verbose…$ 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.
-----------------------------------------------------------------------
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Debugging helps› Look in the user log file, “log”
› Look in the DAGman debugging log
› “foo”.dagman.out
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What does converge.sh need
› Note the output files?
› How to make them unique?
› Add DAG variables to inner dag And submitRandom file
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The submit file (again)# submitRandomuniverse = vanillaexecutable = random.shoutput = outlog = logqueue
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The submit file# submitRandomuniverse = vanillaexecutable = random.shoutput = out.$(NodeNumber)log = logqueue
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The inner DAG file (again)
Job Node0 submit_preJob Node1 submitRandomJob Node2 submitRandomJob Node3 submitRandomPARENT Node0 CHILD Node1PARENT Node0 CHILD Node2PARENT Node0 CHILD Node3Job Node11 submit_postPARENT Node1 CHILD Node11PARENT Node2 CHILD Node11PARENT Node3 CHILD Node11
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The inner DAG file (again)
Job Node0 submit_preJob Node1 submitRandomJob Node2 submitRandomJob Node3 submitRandom…VARS Node1 NodeNumber=“1”VARS Node2 NodeNumber=“2”VARS Node3 NodeNumber=“3”…
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Then converge.sh sees:$ 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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Thank you!Check us out on the Web:
http://www.condorproject.org
Email:[email protected]