Using Condor An Introduction Condor Week 2007

Tutorial Outline The story of Frieda, the scientist Using Condor to manage jobs Using Condor to manage resources Condor architecture and mechanisms Condor on the grid Flocking Condor and other grid technologies Stop me if you have any questions!

She is a scientist with a big problem. Meet Frieda. She is a scientist with a big problem.

Frieda’s Application … Run a Parameter Sweep of F(x,y,z) for 20 values of x, 10 values of y and 3 values of z 20×10×3 = 600 combinations F takes on the average 6 hours to compute on a “typical” workstation (total = 600 × 6 = 3600 hours) F requires a “moderate” (256MB) amount of memory F performs “moderate” I/O - (x,y,z) is 5 MB and F(x,y,z) is 50 MB

I have 600 simulations to run. Where can I get help?

While sharing a beverage with some colleagues, she shares her problem While sharing a beverage with some colleagues, she shares her problem. Somebody asks “Have you tried Condor? It’s free.”

Getting Condor Available as a free download from http://www.cs.wisc.edu/condor Download Condor for your operating system Available for most UNIX (including Linux and Apple’s OS/X) platforms Also for Windows NT / XP

Condor Releases Stable / Developer Releases Version numbering scheme similar to that of the (pre 2.6) Linux kernels … Major.minor.release Minor is even (a.b.c): Stable Examples: 6.6.3, 6.8.4, 6.8.5 Very stable, mostly bug fixes Minor is odd (a.b.c): Developer New features, may have some bugs Examples: 6.7.11, 6.9.1, 6.9.2

Frieda Installs a “Personal Condor” on her machine… What do we mean by a “Personal” Condor? Condor on your own workstation No root / administrator access required No system administrator intervention needed After installation, Frieda submits her jobs to her Personal Condor…

Frieda’s Condor Pool F(3,4,5) personal Condor Frieda's workstation jobs personal Condor Frieda's workstation

Personal Condor?! What’s the benefit of a Condor “Pool” with just one user and one machine?

Your Personal Condor will ... Keep an eye on your jobs and will keep you posted on their progress Implement your policy on the execution order of the jobs Keep a log of your job activities Add fault tolerance to your jobs Implement your policy on when the jobs can run on your workstation

Definitions Job Machine Match Making 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”

Job Jobs state their requirements and preferences: I need a Linux/x86 platform I want the machine with the most memory I prefer a machine in the chemistry department

Machine Machines state their requirements and preferences: Run jobs only when there is no keyboard activity I prefer to run Frieda’s jobs I am a machine in the physics department Never run jobs belonging to Dr. Smith

The Magic of Matchmaking Jobs and machines state their requirements and preferences Condor matches jobs with machines based 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

Using 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 windows No GUI

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 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 = vanilla Executable = my_job Output = 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

ClassAd ? Condor’s internal data representation Similar to classified ads (as the name implies) Represent an object & its attributes Usually many attributes Can also describe what an object matches with

ClassAd Details ClassAds can contain a lot of details The job’s executable is analysis.exe The machine’s load average is 5.6 ClassAds can specify requirements I require a machine with Linux ClassAds can specify preferences This machine prefers to run jobs from the physics group

ClassAd Details (continued) ClassAds are: semi-structured user-extensible schema-free Attribute = Expression

ClassAd Example Example: MyType = "Job" String TargetType = "Machine" ClusterId = 1377 Owner = "roy" Cmd = "sim.exe" Requirements = (Arch == "INTEL") && (OpSys == "LINUX") && (Disk >= DiskUsage) && ((Memory * 1024)>=ImageSize) … String Number Boolean

The Dog ClassAd Type = “Dog” Color = “Brown” Price = 12 ClassAd for the “Job” . . . Requirements = (type == “Dog”) && (color == “Brown”) && (price <= 15) ClassAd Type = “Dog” Color = “Brown” Price = 12

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

Example condor_submit and condor_q % condor_submit my_job.submit Submitting 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 = complete Notification = never Notification = error Notification = 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 = vanilla Executable = /home/frieda/condor/my_job.condor Log = my_job.log ·Job log (from Condor) Input = my_job.in ·Program’s standard input Output = my_job.out ·Program’s standard output Error = my_job.err ·Program’s standard error Arguments = -a1 -a2 ·Command line arguments InitialDir = /home/frieda/condor/run 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 files Universe = vanilla Executable = my_job Arguments = -x 0 log = my_job_0.log Input = my_job_0.in Output = my_job_0.out Error = my_job_0.err Queue ·Job 2.0 (cluster 2, process 0) Arguments = -x 1 log = my_job_1.log Input = my_job_1.in Output = my_job_1.out Error = my_job_1.err Queue ·Job 2.1 (cluster 2, process 1)

Submitting The Job % 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 1 3 jobs; 2 idle, 1 running, 0 held %

Back to our 600 jobs… We could put all input, output, error & log files in the one directory One of each type for each job That’d be 2400 files (4 files × 600 jobs) Difficult to sort through Better: Create a subdirectory for each run

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

Frieda’s simulation directory sim.exe sim.sub run_0 simulation.in simulation.out simulation.err simulation.log simulation.in simulation.out simulation.err simulation.log run_599

Submit Description File for 600 Jobs # Cluster of 600 jobs with different directories Universe = vanilla Executable = sim Log = simulation.log ... Arguments = -x 0 InitialDir = run_0 ·Log, input, output & error files -> run_0 Queue ·Job 3.0 (Cluster 3, Process 0) Arguments = -x 1 InitialDir = run_1 ·Log, input, output & error files -> run_1 Queue ·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 directories Universe = vanilla Executable = my_job Log = my_job.log Input = my_job.in Output = my_job.out Error = my_job.err Arguments = –x $(Process) ·–x 0, -x 1, … -x 599 InitialDir = run_$(Process) ·run_0 … run_599 Queue 600 ·Jobs 4.0 … 4.599

Now, we submit it… $ condor_submit my_job.submit Submitting 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.edu ID OWNER SUBMITTED RUN_TIME ST PRI SIZE CMD 4.0 frieda 4/20 12:08 0+00:00:05 R 0 9.8 my_job -arg1 –x 0 4.1 frieda 4/20 12:08 0+00:00:03 I 0 9.8 my_job -arg1 –x 1 4.2 frieda 4/20 12:08 0+00:00:01 I 0 9.8 my_job -arg1 –x 2 4.3 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 598 4.599 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

Another Universe

More about Condor Universes Multiple Condor Universes Different feature sets We’ve been using the “Vanilla” universe Can be used to run any serial job And, introducing: Scheduler Local

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

Frieda can still only run one job at a time, however. 600 Condor jobs Frieda’s Condor Pool F(3,4,5) Frieda can still only run one job at a time, however. personal Condor Frieda's workstation

Good News The Boss says Frieda can add her co-workers’ desktop machines into her Condor pool as well… but only if they can also submit jobs. (Boss Fat Cat)

Adding nodes Frieda installs Condor on the desktop machines, and configures them with her machine as the central manager The central manager: Central repository for the whole pool Performs job / machine matching, etc. These are “non-dedicated” nodes, meaning that they can't always run Condor jobs

Frieda’s Condor Pool 600 Condor jobs Now, Frieda and her co-workers can run multiple jobs at a time so their work completes sooner. Condor Pool

condor_status % condor_status Name OpSys Arch State Activ LoadAv Mem ActvtyTime antipholus.cs LINUX INTEL Unclaimed Idle 0.020 511 0+02:28:42 coral.cs.wisc LINUX INTEL Claimed Busy 0.990 511 0+01:27:21 doc.cs.wisc.e LINUX INTEL Unclaimed Idle 0.260 511 0+00:20:04 dsonokwa.cs.w LINUX INTEL Claimed Busy 0.810 511 0+00:01:45 ferdinand.cs. LINUX INTEL Claimed Suspe 1.130 511 0+00:00:55 vm1@pinguino. LINUX INTEL Unclaimed Idle 0.000 255 0+01:03:28 vm2@pinguino. LINUX INTEL Unclaimed Idle 0.190 255 0+01:03:29

How can my jobs access their data files?

Access to Data in Condor Use shared filesystem if available No shared filesystem? Condor can transfer files Can automatically send back changed files Atomic transfer of multiple files Can be encrypted over the wire Remote I/O Socket Standard Universe can use remote system calls (more on this later)

Condor File Transfer ShouldTransferFiles = YES Always transfer files to execution site ShouldTransferFiles = NO Rely on a shared filesystem ShouldTransferFiles = IF_NEEDED Will automatically transfer the files if the submit and execute machine are not in the same FileSystemDomain Universe = vanilla Executable = my_job Log = my_job.log ShouldTransferFiles = IF_NEEDED Transfer_input_files = dataset.$(Process), common.data Transfer_output_files = TheAnswer.dat Queue 600

We Always Want More Condor is managing and running our jobs, but Our CPU requirements are greater than our resources Jobs are preempted more often than we like

Happy Day! Frieda’s organization purchased a Dedicated Cluster! Frieda Installs Condor on all the dedicated Cluster nodes Frieda also adds a dedicated central manager She configures her entire pool with this new host as the central manager…

Frieda’s Condor Pool 600 Condor jobs With the additional resources, Frieda and her co-workers can get their jobs completed even faster. Condor Pool Dedicated Cluster

What Condor Daemons are running on my machine, and what do they do?

condor_master Starts up all other Condor daemons If there are any problems and a daemon exits, it restarts the daemon and sends email to the administrator Acts as the server for many Condor remote administration commands: condor_reconfig, condor_restart, condor_off, condor_on, condor_config_val, etc.

Personal Condor / Central Manager Condor Daemon Layout Personal Condor / Central Manager Master negotiator startd schedd collector = Process Spawned

Central Manager: condor_collector Central manager: central repository and match maker for whole pool Collects information from all other Condor daemons in the pool “Directory Service” / Database for a Condor pool Each daemon sends a periodic update called a “ClassAd” to the collector Services queries for information: Queries from other Condor daemons Queries from users (condor_status) Only on the Central Manager At least one collector per pool

Condor Pool Layout: Collector = Process Spawned Central Manager = ClassAd Communication Pathway Master Collector

Central Manager: condor_negotiator Performs “matchmaking” in Condor Each “Negotiation Cycle” (typically 5 minutes): Gets information from the collector about all available machines and all idle jobs Tries to match jobs with machines that will serve them Both the job and the machine must satisfy each other’s requirements Only one negotiator per pool Only on the Central Manager

Condor Pool Layout: Negotiator = Process Spawned Central Manager = ClassAd Communication Pathway Master negotiator Collector

Execute Hosts: condor_startd Execute host: machines that run user jobs Represents a machine to the Condor system Responsible for starting, suspending, and stopping jobs Enforces the wishes of the machine owner (the owner’s “policy”… more on this in the administrator’s tutorial) Creates a “starter” for each running job One startd runs on each execute node

Condor Pool Layout: startd Cluster Node Master startd = Process Spawned Central Manager = ClassAd Communication Pathway Master negotiator Cluster Node Master startd Collector

Submit Hosts: condor_schedd Submit hosts: machines that users can submit jobs on Maintains the persistent queue of jobs Responsible for contacting available machines and sending them jobs Services user commands which manipulate the job queue: condor_submit,condor_rm, condor_q, condor_hold, condor_release, condor_prio, … Creates a “shadow” for each running job One schedd runs on each submit host

Condor Pool Layout: schedd Cluster Node Master startd = Process Spawned Central Manager = ClassAd Communication Pathway Master negotiator negotiator schedd Cluster Node Master startd Collector Desktop Desktop Master Master startd startd schedd schedd

Condor Pool Layout: master Cluster Node Master startd = Process Spawned Central Manager = ClassAd Communication Pathway schedd Master Master schedd negotiator negotiator Cluster Node Master startd Collector Desktop Desktop Master Master startd startd schedd schedd

Now what? Some of the machines in the pool can’t run my jobs Not enough RAM Not enough scratch disk space Required software not installed Etc.

Specify Requirements An expression (syntax similar to C or Java) Must evaluate to True for a match to be made Universe = vanilla Executable = my_job Log = my_job.log InitialDir = run_$(Process) Requirements = Memory >= 256 && Disk > 10000 Queue 600

Advanced Requirements Requirements can match custom attributes in your Machine Ad Can be added by hand to each machine Or, automatically using the “Hawkeye” mechanism Universe = vanilla Executable = my_job Log = my_job.log InitialDir = run_$(Process) Requirements = Memory >= 256 && Disk > 10000 \ && ( HaveProg =!= UNDEFINED && HaveProg) ) Queue 600

And, Specify Rank All matches which meet the requirements can be sorted by preference with a Rank expression. Higher the Rank, the better the match Universe = vanilla Executable = my_job Log = my_job.log Arguments = -arg1 –arg2 InitialDir = run_$(Process) Requirements = Memory >= 256 && Disk > 10000 Rank = (KFLOPS*10000) + Memory Queue 600

My jobs aren’t running!!

Check the queue Check the queue with condor_q: bash-2.05a$ condor_q -- Submitter: x.cs.wisc.edu : <128.105.121.53:510> :x.cs.wisc.edu ID OWNER SUBMITTED RUN_TIME ST PRI SIZE CMD 5.0 frieda 4/20 12:23 0+00:00:00 I 0 9.8 my_job -arg1 –n 0 5.1 frieda 4/20 12:23 0+00:00:00 I 0 9.8 my_job -arg1 –n 1 5.2 frieda 4/20 12:23 0+00:00:00 I 0 9.8 my_job -arg1 –n 2 5.3 frieda 4/20 12:23 0+00:00:00 I 0 9.8 my_job -arg1 –n 3 5.4 frieda 4/20 12:23 0+00:00:00 I 0 9.8 my_job -arg1 –n 4 5.5 frieda 4/20 12:23 0+00:00:00 I 0 9.8 my_job -arg1 –n 5 5.6 frieda 4/20 12:23 0+00:00:00 I 0 9.8 my_job -arg1 –n 6 5.7 frieda 4/20 12:23 0+00:00:00 I 0 9.8 my_job -arg1 –n 7 6.0 frieda 4/20 13:22 0+00:00:00 H 0 9.8 my_job -arg1 –arg2 8 jobs; 8 idle, 0 running, 1 held

Look at jobs on hold Or, See full details for a job % condor_q –l 6.0 % condor_q –hold -- Submiter: x.cs.wisc.edu : <128.105.121.53:510> :x.cs.wisc.edu ID OWNER HELD_SINCE HOLD_REASON 6.0 frieda 4/20 13:23 Error from starter on vm1@skywalker.cs.wisc 9 jobs; 8 idle, 0 running, 1 held Or, See full details for a job % condor_q –l 6.0

Check machine status Verify that there are idle machines with condor_status: bash-2.05a$ condor_status Name OpSys Arch State Activity LoadAv Mem ActvtyTime vm1@tonic.c LINUX INTEL Claimed Busy 0.000 501 0+00:00:20 vm2@tonic.c LINUX INTEL Claimed Busy 0.000 501 0+00:00:19 vm3@tonic.c LINUX INTEL Claimed Busy 0.040 501 0+00:00:17 vm4@tonic.c LINUX INTEL Claimed Busy 0.000 501 0+00:00:05 Total Owner Claimed Unclaimed Matched Preempting INTEL/LINUX 4 0 4 0 0 0 Total 4 0 4 0 0 0

Look in Job Log Look in your job log for clues: bash-2.05a$ cat my_job.log 000 (031.000.000) 04/20 14:47:31 Job submitted from host: <128.105.121.53:48740> ... 007 (031.000.000) 04/20 15:02:00 Shadow exception! Error from starter on gig06.stat.wisc.edu: Failed to open '/scratch.1/frieda/workspace/v67/condor- test/test3/run_0/my_job.in' as standard input: No such file or directory (errno 2) 0 - Run Bytes Sent By Job 0 - Run Bytes Received By Job

Still not running? Exercise a little patience On a busy pool, it can take a while to match and start your jobs Wait at least a negotiation cycle or two (typically 5 minutes)

Look to condor_q for help: condor_q -analyze bash-2.05a$ condor_q -ana 29 --- 029.000: Run analysis summary. Of 1243 machines, 1243 are rejected by your job's requirements 0 are available to run your job WARNING: Be advised: No resources matched request's constraints Check the Requirements expression below: Requirements = ((Memory > 8192)) && (Arch == "INTEL") && (OpSys == "LINUX") && (Disk >= DiskUsage) && (TARGET.FileSystemDomain == MY.FileSystemDomain)

Better analysis (Linux only): condor_q –better-analyze bash-2.05a$ condor_q -better-ana 29 The Requirements expression for your job is: ( ( target.Memory > 8192 ) ) && ( target.Arch == "INTEL" ) && ( target.OpSys == "LINUX" ) && ( target.Disk >= DiskUsage ) && ( TARGET.FileSystemDomain == MY.FileSystemDomain ) Condition Machines Matched Suggestion --------- ---------------- ---------- 1 ( ( target.Memory > 8192 ) ) 0 MODIFY TO 4000 2 ( TARGET.FileSystemDomain == "cs.wisc.edu" )584 3 ( target.Arch == "INTEL" ) 1078 4 ( target.OpSys == "LINUX" ) 1100 5 ( target.Disk >= 13 ) 1243

Learn about available resources: bash-2.05a$ condor_status –const 'Memory > 8192' (no output means no matches) bash-2.05a$ condor_status -const 'Memory > 4096' Name OpSys Arch State Activ LoadAv Mem ActvtyTime vm1@s0-03.cs. LINUX X86_64 Unclaimed Idle 0.000 5980 1+05:35:05 vm2@s0-03.cs. LINUX X86_64 Unclaimed Idle 0.000 5980 13+05:37:03 vm1@s0-04.cs. LINUX X86_64 Unclaimed Idle 0.000 7988 1+06:00:05 vm2@s0-04.cs. LINUX X86_64 Unclaimed Idle 0.000 7988 13+06:03:47 Total Owner Claimed Unclaimed Matched Preempting X86_64/LINUX 4 0 0 4 0 0 Total 4 0 0 4 0 0

Job Policy Expressions User can supply job policy expressions in the submit file. Can be used to describe a successful run. on_exit_remove = <expression> on_exit_hold = <expression> periodic_remove = <expression> periodic_hold = <expression>

Job Policy Examples Do not remove if exits with a signal: on_exit_remove = ExitBySignal == False Place on hold if exits with nonzero status or ran for less than an hour: on_exit_hold = ( (ExitBySignal==False) && (ExitSignal != 0) ) || ( (ServerStartTime - JobStartDate) < 3600) Place on hold if job has spent more than 50% of its time suspended: periodic_hold = CumulativeSuspensionTime > (RemoteWallClockTime / 2.0)

Insert ClassAd attributes Special purpose usage In the submit description file, introduce an attribute for the job +Department = biochemistry causes the ClassAd to contain Department = ”biochemistry”

We’ve seen how Condor can: Keep an eye on your jobs and will keep you posted on their progress Implement your policy on the execution order of the jobs Keep a log of your job activities

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?

Run them in Todd’s Private Universe?

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 in the Standard Universe I/O system calls are trapped and sent back to the submit machine Examples: 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 Killed! 3 3

Checkpointing: Process Resumed goodput goodput badput time 3 3

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 % 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

Connecting Condors Frieda knows people with their own Condor pools, and gets permission to use their computing resources… How can Condor help her do this?

Connect Condors with Flocking Frieda configures her Condor pool to “flock” to her friend’s pool. Flocking is a Condor-specific technology.

Frieda’s Condor Pool 600 Condor jobs Condor Pool Friendly Condor Pool

Frieda meets The Grid Frieda also has access to grid resources she wants to use She has certificates and access to Globus or other resources at remote institutions But Frieda wants Condor’s queue management features for her jobs! She installs Condor so she can submit “Grid Universe” jobs to Condor

“Grid” Universe All handled in your submit file Supports a number of “back end” types: Globus: GT2, GT3, GT4 NorduGrid UNICORE Condor PBS LSF

Grid Universe & Globus 2/3 Used for a Globus GT2 / GT3 back-end “Condor-G” Format: Grid_Resource = (gt2|gt3) Head-Node Globus_rsl = <RSL-String> Example: Universe = grid Grid_Resource = gt2 beak.cs.wisc.edu/jobmanager Globus_rsl = (queue=long)(project=atom-smasher)

Grid Universe & Globus 4 Used for a Globus GT4 back-end Format: Grid_Resource = gt4 <Head-Node> <Scheduler-Type> Globus_XML = <XML-String> Example: Universe = grid Grid_Resource = gt4 beak.cs.wisc.edu Condor Globus_xml = <queue>long</queue><project>atom- smasher</project>

Grid Universe & Condor Used for a Condor back-end Format: Example: “Condor-C” Format: Grid_Resource = condor <Schedd-Name> <Collector-Name> Remote_<param> = <value> “Remote_” part is stripped off Example: Universe = grid Grid_Resource = condor beak condor.cs.wisc.edu Remote_Universe = standard

Grid Universe & NorduGrid Used for a NorduGrid back-end Grid_Resource = nordugrid <Host-Name> Example: Universe = grid Grid_Resource = nordugrid ngrid.cs.wisc.edu

Grid Universe & UNICORE Used for a UNICORE back-end Format: Grid_Resource = unicore <USite> <VSite> Example: Universe = grid Grid_Resource = unicore uhost.cs.wisc.edu vhost

Grid Universe & PBS Used for a PBS back-end Format: Example: Grid_Resource = pbs Example: Universe = grid

Grid Universe & LSF Used for a LSF back-end Format: Example: Grid_Resource = lsf Example: Universe = grid

Credential Management Condor will do The Right Thing™ with your X509 certificate and proxy Override default proxy: X509UserProxy = /home/frieda/other/proxy Proxy may expire before jobs finish executing Condor can use MyProxy to renew your proxy When a new proxy is available, Condor will forward the renewed proxy to the job This works for non-grid jobs, too

My jobs have have dependencies… Can Condor help solve my dependency problems?

Frieda learns 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.”) In the simplest case…

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 a DAG is the best data structure to represent a workflow of jobs with dependencies children may not run until their parents have finished – this is why the graph is a directed graph … there’s a direction to the flow of work In this example, called a “diamond” dag, job A must run first; when it finishes, jobs B and C can run together; when they are both finished, D can run; when D is finished the DAG is finished Loops, where two jobs are both descended from one another, are prohibited because they would lead to deadlock – in a loop, neither node could run until the other finished, and so neither would start – this restriction is what makes the graph acyclic

Defining a DAG A DAG is defined by a .dag file, listing each of its nodes and their dependencies: # diamond.dag Job A a.sub Job B b.sub Job C c.sub Job D d.sub Parent A Child B C Parent 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 This is all it takes to specify the example “diamond” dag

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 Just like any other Condor job, you get fault tolerance in case the machine crashes or reboots, or if there’s a network outage And you’re notified when it’s done, and whether it succeeded or failed % condor_q -- Submitter: foo.bar.edu : <128.105.175.133:1027> : foo.bar.edu ID OWNER SUBMITTED RUN_TIME ST PRI SIZE CMD 2456.0 user 3/8 19:22 0+00:00:02 R 0 3.2 condor_dagman -f -

Running a DAG DAGMan acts as a “meta-scheduler”, managing the submission of your jobs to Condor based on the DAG dependencies. DAGMan A Condor Job Queue .dag File A B C First, job A will be submitted alone… D

Running a DAG (cont’d) DAGMan holds & submits jobs to the Condor queue at the appropriate times. DAGMan A Condor Job Queue B B C Once job A completes successfully, jobs B and C will be submitted at the same time… C D

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. DAGMan X D A B Condor Job Queue Rescue File If job C fails, DAGMan will wait until job B completes, and then will exit, creating a rescue file. Job D will not run. In its log, DAGMan will provide additional details of which node failed and why.

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. DAGMan A Condor Job Queue Rescue File B C Since jobs A and B have already completed, DAGMan will start by re- submitting job C C D

Recovering a DAG (cont’d) Once that job completes, DAGMan will continue the DAG as if the failure never happened. DAGMan A Condor Job Queue B C D D

Finishing a DAG Once the DAG is complete, the DAGMan job itself is finished, and exits. DAGMan A Condor Job Queue B C If job C fails, DAGMan will wait until job B completes, and then will exit, creating a rescue file. D

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”

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

Condor Job Universes Serial Jobs Parallel Jobs Vanilla Universe Standard Universe Grid Universe Scheduler Local Universe Java Universe Parallel Jobs MPI Universe PVM Universe Parallel Universe

Why have a special Universe for Java jobs? Java Universe provides more than just inserting “java” at the start of the execute line of a vanilla job: Knows which machines have a JVM installed Knows the location, version, and performance of JVM on each machine Knows about jar files, etc. Provides more information about Java job completion than just JVM exit code Program runs in a Java wrapper, allowing Condor to report Java exceptions, etc.

Universe Java Job Example Java Universe Submit file: Universe = java Executable = Main.class jar_files = MyLibrary.jar Input = infile Output = outfile Arguments = Main 1 2 3 Queue

Java support, cont. bash-2.05a$ condor_status –java Name JavaVendor Ver State Actv LoadAv Mem abulafia.cs Sun Microsy 1.5.0_ Claimed Busy 0.180 503 acme.cs.wis Sun Microsy 1.5.0_ Unclaimed Idle 0.000 503 adelie01.cs Sun Microsy 1.5.0_ Claimed Busy 0.000 1002 adelie02.cs Sun Microsy 1.5.0_ Claimed Busy 0.000 1002 … Total Owner Claimed Unclaimed Matched Preempting INTEL/LINUX 965 179 516 250 20 0 INTEL/WINNT50 102 6 65 31 0 0 SUN4u/SOLARIS28 1 0 0 1 0 0 X86_64/LINUX 128 2 106 20 0 0 Total 1196 187 687 302 20 0

Frieda wants Condor features on remote resources She wants to run standard universe jobs on Grid-managed resources For matchmaking and dynamic scheduling of jobs For job checkpointing and migration For remote system calls

Condor GlideIn Frieda can use the Grid Universe to run Condor daemons on Grid resources When the resources run these GlideIn jobs, they will temporarily join her Condor Pool She can then submit Standard, Vanilla, PVM, or MPI Universe jobs and they will be matched and run on the remote resources Currently only supports Globus GT2 We hope to fix this limitation

LSF PBS Remote Grid Condor Condor Pool 600 Condor jobs glide-in jobs Friendly Condor Pool

Collector & Negotiator How It Works Condor jobs Personal Condor Remote Resource Master Manager GlideIn jobs LSF Collector & Negotiator Schedd Startd Starter Grid Manager Shadow User Job

GlideIn Concerns What if the remote resource kills my GlideIn job? That resource will disappear from your pool and your jobs will be rescheduled on other machines Standard universe jobs will resume from their last checkpoint like usual What if all my jobs are completed before a GlideIn job runs? If a GlideIn Condor daemon is not matched with a job in 10 minutes, it terminates, freeing the resource

In Review With Condor’s help, Frieda can: Manage her compute job workload Access local machines Access remote Condor Pools via flocking Access remote compute resources on the Grid via “Grid Universe” jobs Carve out her own personal Condor Pool from the Grid with GlideIn technology

Advanced Topics

Administrator Commands condor_vacate Leave a machine now condor_on Start Condor condor_off Stop Condor condor_reconfig Reconfig on-the-fly condor_config_val View/set config condor_userprio User Priorities condor_stats View detailed usage accounting stats

My boss wants to watch what Condor is doing

Use CondorView! Provides visual graphs of current and past utilization Data is derived from Condor's own accounting statistics Interactive Java applet Quickly and easily view: How much Condor is being used How many cycles are being delivered Who is using them Utilization by machine platform or by user

CondorView Usage Graph

A Common Question My Personal Condor is flocking with a bunch of Solaris and Linux machines, and also doing a GlideIn to a SGI O2K. I do not want to statically partition my jobs. Solution: In your submit file, specify: Executable = myjob.$$(OpSys).$$(Arch) Requirements = (Arch==“INTEL” && OpSys==“LINUX”)\ ||(Arch==“SUN4u” && OpSys==“SOLARIS8” )\ ||(Arch==“SGI” && OpSys==“IRIX65”) The “$$(xxx)” notation is replaced with attributes from the machine ClassAd which was matched with your job.

Thank you! Check us out on the Web: http://www.condorproject.org Email: condor-admin@cs.wisc.edu