Condor Tutorial
First EuroGlobus Workshop
June 2001

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
Case Study: DTF

Tutorial Outline
Overview: What is Condor
What does Condor do?
What is Condor good for?
What kind of results can I expect?

The Condor Project (Established ‘85)
Distributed High Throughput Computing research performed by a team of ~25 faculty, full time staff and students who:
face software engineering challenges in a distributed UNIX/Linux/NT environment,
are involved in national and international collaborations,
actively interact with academic and commercial users,
maintain and support a large distributed production environment,
and educate and train students.
Funding – US Govt. (DoD, DoE, NASA, NSF),
AT&T, IBM, INTEL, Microsoft UW-Madison

What is High-Throughput Computing?
High-performance: CPU cycles/second under ideal circumstances.
“How fast can I run simulation X on this machine?”
High-throughput: CPU cycles/day (week, month, year?) under non-ideal circumstances.
“How many times can I run simulation X in the next month using all available machines?”

What is Condor?
Condor converts collections of distributively owned workstations and dedicated clusters into a distributed high-throughput computing facility.
Condor uses ClassAd Matchmaking to make sure that everyone is happy.

The Condor System
Unix and NT
Operational since 1986
Manages more than 1300 CPUs at UW-Madison
Software available free on the web
More than 150 Condor installations worldwide in academia and industry

Some HTC Challenges
Condor does whatever it takes to run your jobs, even if some machines…
Crash (or are disconnected)
Run out of disk space
Don’t have your software installed
Are frequently needed by others
Are far away & managed by someone else

What is ClassAd Matchmaking?
Condor uses ClassAd Matchmaking to make sure that work gets done within the constraints of both users and owners.
Users (jobs) have constraints:
“I need an Alpha with 256 MB RAM”
Owners (machines) have constraints:
“Only run jobs when I am away from my desk and never run jobs owned by Bob.”

Upgrade to Condor-G
A Grid-enabled version of Condor that provides robust job management for Globus.
Robust replacement for globusrun
Provides extensive fault-tolerance
Brings Condor’s job management features to Globus jobs

What Have We Done on the Grid Already?
Example: NUG30
quadratic assignment problem
30 facilities, 30 locations
minimize cost of transferring materials between them
posed in 1968 as challenge, long unsolved
but with a good pruning algorithm & high-throughput computing...

NUG30 Solved on the Grid with Condor + Globus
Resource simultaneously utilized:
the Origin 2000 (through LSF ) at NCSA.
the Chiba City Linux cluster at Argonne
the SGI Origin 2000 at Argonne.
the main Condor pool at Wisconsin (600 processors)
the Condor pool at Georgia Tech (190 Linux boxes)
the Condor pool at UNM  (40 processors)
the Condor pool at Columbia (16 processors)
the Condor pool at Northwestern (12 processors)
the Condor pool at NCSA (65 processors)
the Condor pool at INFN (200 processors)

NUG30 - Solved!!!
Subject: Re: Let the festivities begin.
Hi dear Condor Team,
you all have been amazing. NUG30 required 10.9 years of Condor Time.  In just seven days !
More stats tomorrow !!! We are off celebrating !
condor rules !

The Idea
Computing power
is everywhere,
we try to make it usable by anyone.

Meet Frieda.

Frieda’s Application …
Simulate the behavior 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 3 hours to compute on a “typical” workstation (total = 1800 hours)
F requires a “moderate” (128MB) 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?

Slide 18

Installing Condor
Download Condor for your operating system
Available as a free download from
Stable –vs- Developer Releases
Naming scheme similar to the Linux Kernel…
Available for most Unix platforms and Windows NT

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

Slide 21

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

Getting Started: Submitting Jobs to Condor
Choosing a “Universe” for your job
Just use VANILLA for now
Make your job “batch-ready”
Creating a submit description file
Run condor_submit on your submit description file

Making your job batch-ready
Must be able to run in the background: no interactive input, windows, GUI, etc.
Can still use STDIN, STDOUT, and STDERR (the keyboard and the screen), but files are used for these instead of the actual devices
Organize data files

Creating a Submit Description File
A plain ASCII text file
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

Running condor_submit
You give condor_submit the name of the submit file you have created
condor_submit parses the file, checks for errors, and creates a “ClassAd” that describes your job(s)
Sends your job’s ClassAd(s) and executable to the condor_schedd, which stores the job in its queue
Atomic operation, two-phase commit
View the queue with condor_q

Running condor_submit
% condor_submit my_job.submit-file
Submitting job(s).
1 job(s) submitted to cluster 1.
% condor_q
-- Submitter: : <> :
  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

Another Submit Description File

“Clusters” and “Processes”
If your submit file describes multiple jobs, we call this a “cluster”
Each job within a cluster is called a “process” or “proc”
If you only specify one job, you still get a cluster, but it has only one process
A Condor “Job ID” is the cluster number, a period, and the process number (“23.5”)
Process numbers always start at 0

Example Submit Description File for a Cluster

Slide 33

Submit Description File for a BIG Cluster of Jobs
Specify initial directory for each job is specified with the $(Process) macro, and instead of submitting a single job, we use “Queue 600” to submit 600 jobs at once
$(Process) will be expanded to the process number for each job in the cluster (from 0 up to 599 in this case), so we’ll have “run_0”, “run_1”, … “run_599” directories
All the input/output files will be in different directories!

Submit Description File for a BIG Cluster of Jobs
# Example condor_submit input file that defines
# a cluster of 600 jobs with different iwd
Universe   = vanilla
Executable = my_job
Arguments  = -arg1 –arg2
InitialDir = run_$(Process)
Queue 600

Using condor_rm
If you want to remove a job from the Condor queue, you use condor_rm
You can only remove jobs that you own (you can’t run condor_rm on someone else’s jobs unless you are root)
You can give specific job ID’s (cluster or cluster.proc), or you can remove all of your jobs with the “-a” option.

Temporarily halt a Job
Use condor_hold to place a job on hold
Kills job if currently running
Will not attempt to restart job until released
Use condor_release to remove a hold and permit job to be scheduled again

Using condor_history
Once your job completes, it will no longer show up in condor_q
You can use condor_history to view information about a completed job
The status field (“ST”) will have either a “C” for “completed”, or an “X” if the job was removed with condor_rm

Getting Email from Condor
By default, Condor will send you email when your jobs completes
With lots of information about the run
If you don’t want this email, put this in your submit file:
notification = never
If you want email every time something happens to your job (preempt, exit, etc), use this:
notification = always

Getting Email from Condor (cont’d)
If you only want email in case of errors, use this:
notification = error
By default, the email is sent to your account on the host you submitted from.  If you want the email to go to a different address, use this:
notify_user =

A Job’s life story: The “User Log” file
A UserLog must be specified in your submit file:
Log = filename
You get a log entry for everything that happens to your job:
When it was submitted, when it starts executing, preempted, restarted, completes, if there are any problems, etc.
Very useful!  Highly recommended!

Sample Condor User Log

Uses for the User Log
Easily read by human or machine
C++ library and Perl Module  for parsing UserLogs is available
Event triggers for meta-schedulers
Like DagMan…
Visualizations of job progress
Condor JobMonitor Viewer

Condor JobMonitor

Job Priorities w/ condor_prio
condor_prio allows you to specify the order in which your jobs are started
Higher the prio #, the earlier the job will start
% condor_q
-- Submitter: : <> :
  1.0    frieda           6/16 06:52  0+00:02:11 R  0   0.0  my_job
% condor_prio +5 1.0
% condor_q
-- Submitter: : <> :
  1.0    frieda           6/16 06:52  0+00:02:13 R  5   0.0  my_job

Want other Scheduling possibilities?
Extend with the Scheduler Universe
In addition to VANILLA, another job universe is the Scheduler Universe.
Scheduler Universe jobs run on the submitting machine and serve as a meta-scheduler.
DAGMan meta-scheduler included

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!

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

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  submits a Scheduler Universe Job with DAGMan as the executable.
Thus the DAGMan daemon itself runs as a Condor job, so you don’t have to baby-sit it.

Running a DAG
DAGMan acts as a “meta-scheduler”, managing the submission of your jobs to Condor based on the DAG dependencies.

Running a DAG (cont’d)
DAGMan holds & submits jobs to the Condor queue at the appropriate times.

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.

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.

Recovering a DAG (cont’d)
Once that job completes, DAGMan will continue the DAG as if the failure never happened.

Finishing a DAG
Once the DAG is complete, the DAGMan job itself is finished, and exits.

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”

We’ve seen how 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 ?

What if each job needed to run for 20 days?

What if I wanted to interrupt a job with a higher priority job?

Condor’s Standard Universe to the rescue!
Condor can support various combinations of features/environments in different “Universes”
Different Universes provide different functionality for your job:
Vanilla – Run any Serial Job
Scheduler – Plug in a meta-scheduler
Standard – Support for transparent process checkpoint and restart

Process Checkpointing
Condor’s Process Checkpointing mechanism saves all the state of a process into a checkpoint file
Memory, CPU, I/O, etc.
The process can then be restarted from right where it left off
Typically no changes to your job’s source code needed – however, your job must be relinked with Condor’s Standard Universe support library

Relinking Your Job for submission to the
Standard Universe
To do this, just place “condor_compile” in front of the command you normally use to link your job:

Limitations in the
Standard Universe
Condor’s checkpointing is not at the kernel level.  Thus in the Standard Universe the job may not
Use kernel threads
Use some forms of IPC, such as pipes and shared memory
Many typical scientific jobs are OK

When will Condor checkpoint your job?
Periodically, if desired
For fault tolerance
To free the machine to do a higher priority task (higher priority job, or a job from a user with higher priority)
Preemptive-resume scheduling
When you explicitly run condor_checkpoint, condor_vacate, condor_off or condor_restart command

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

Condor Daemon Layout

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
Checks the time stamps on the binaries of the other Condor daemons, and if new binaries appear, the master will gracefully shutdown the currently running version and start the new version

condor_master (cont’d)
Acts as the server for many Condor remote administration commands:
condor_reconfig, condor_restart, condor_off, condor_on, condor_config_val, etc.

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 soon)

Represents users to the Condor system
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, …

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)

Performs “matchmaking” in Condor
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

Happy Day!  Frieda’s organization purchased a Beowulf Cluster!
Frieda Installs Condor on all the dedicated Cluster nodes, and configures them with her machine as the central manager…
Now her Condor Pool can run multiple jobs at once

Slide 74

Layout of the Condor Pool


Frieda tries out parallel jobs…
MPI Universe & PVM Universe
Schedule and start an MPICH job on dedicated resources
Executable = my-mpi-job
Universe = MPI
Machine_count = 8

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.

Layout of the Condor Pool

Some of the machines in the Pool do not have enough memory or scratch disk space to run my job!

Specify Requirements!
An expression (syntax similar to C or Java)
Must evaluate to True for a match to be made

Specify Rank!
All matches which meet the requirements can be sorted by preference with a Rank expression.
Higher the Rank, the better the match

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
Automatically send back changed files
Atomic transfer of multiple files
Standard Universe can use Remote System Calls

Remote System Calls
I/O System calls trapped and sent back to submit machine
Allows Transparent Migration Across Administrative Domains
Checkpoint on machine A, restart on B
No Source Code changes required
Language Independent
Opportunities for Application Steering
Example: Condor tells customer process “how” to open files

Job Startup

condor_q -io

I am adding nodes to the Cluster… but the Engineering Department has priority on these nodes.

The Machine (Startd) Policy Expressions
START – When is this machine willing to start a job
RANK  - Job Preferences
SUSPEND  - When to suspend a job
CONTINUE  - When to continue a suspended job
PREEMPT – When to nicely stop running a job
KILL  - When to immediately kill a preempting job

Freida’s Current Settings
START = True
SUSPEND  = False
KILL  = False

Freida’s New Settings for the Chemistry nodes
START = True
RANK  = Department == “Chemistry”
SUSPEND  = False
KILL  = False

Submit file with Custom Attribute
Executable = charm-run
Universe = standard
+Department = Chemistry

What if “Department” not specified?
START = True
RANK  = Department =!= UNDEFINED && Department == “Chemistry”
SUSPEND  = False
KILL  = False

Another example
START = True
RANK  = Department =!= UNDEFINED && ((Department == “Chemistry”)*2 + Department == “Physics”)
SUSPEND  = False
KILL  = False

The Cluster is fine.  But not the desktop machines.  Condor can only use the desktops when they would otherwise be idle.

So Frieda decides she wants the desktops to:
START jobs when their has been no activity on the keyboard/mouse for 5 minutes and the load average is low
SUSPEND jobs as soon as activity is detected
PREEMPT jobs if the activity continues for 5 minutes or more
KILL jobs if they take more than 5 minutes to preempt

Macros in the Config File
NonCondorLoadAvg = (LoadAvg - CondorLoadAvg)
BackgroundLoad = 0.3
HighLoad = 0.5
KeyboardBusy = (KeyboardIdle < 10)
CPU_Busy = ($(NonCondorLoadAvg) >= $(HighLoad))
MachineBusy = ($(CPU_Busy) || $(KeyboardBusy))
ActivityTimer = (CurrentTime - EnteredCurrentActivity)

Desktop Machine Policy
START = $(CPU_Idle) && KeyboardIdle > 300
SUSPEND = $(MachineBusy)
CONTINUE = $(CPU_Idle) && KeyboardIdle > 120
PREEMPT = (Activity == "Suspended") &&  $(ActivityTimer) > 300
KILL = $(ActivityTimer) > 300

Policy Review
Users submitting jobs can specify Requirements and Rank expressions
Administrators can specify Startd Policy expressions individually for each machine (Start,Suspend,etc)
Expressions can use any job or machine ClassAd attribute
Custom attributes easily added
Bottom Line: Enforce almost any policy!

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 Specify Dependencies
condor_checkpoint Force a checkpoint
condor_compile Link Condor library

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

CondorView Usage Graph

Back to the Story:
Disaster Strikes!

Frieda Goes to the Grid!
First Frieda takes advantage of her Condor friends!
She knows people with their own Condor pools, and gets permission to access their resources
She then configures her Condor pool to “flock” to these pools

Slide 105

How Flocking Works
Add a line to your condor_config :
FLOCK_HOSTS = Pool-Foo, Pool-Bar

Condor Flocking
Remote pools are contacted in the order specified until jobs are satisfied
The list of remote pools is a property of the Schedd, not the Central Manager
So different users can Flock to different pools
And remote pools can allow specific users
User-priority system is “flocking-aware”
A pool’s local users can have priority over remote users “flocking” in.

Condor Flocking, cont.
Flocking is “Condor” specific technology…
Frieda also has access to Globus resources she wants to use
She has certificates and access to Globus gatekeepers at remote institutions
But Frieda wants Condor’s queue management features for her Globus jobs!
She installs Condor-G so she can submit “Globus Universe” jobs to Condor

Condor-G: Globus + Condor
middleware deployed across entire Grid
remote access to computational resources
dependable, robust data transfer
job scheduling across multiple resources
strong fault tolerance with checkpointing and migration
layered over Globus as “personal batch system” for the Grid

Condor-G Installation: Tell it what you need…

… and watch it go!

Frieda Submits a Globus Universe Job
In her submit description file, she specifies:
Universe = Globus
Which Globus Gatekeeper to use
Optional: Location of file containing your Globus certificate (thanks, Massimo!)
universe     = globus
globusscheduler =
executable   = progname

How It Works

How It Works

How It Works

How It Works

How It Works

Condor Globus Universe

Globus Universe Concerns
What about Fault Tolerance?
Local Crashes
What if the submit machine goes down?
Network Outages
What if the connection to the remote Globus jobmanager is lost?
Remote Crashes
What if the remote Globus jobmanager crashes?
What if the remote machine goes down?

Changes to the Globus JobManager for Fault Tolerance
Ability to restart a JobManager
Enhanced two-phase commit submit protocol

Globus Universe Fault-Tolerance: Submit-side Failures
All relevant state for each submitted job is stored persistently in the Condor job queue.
This persistent information allows the Condor GridManager upon restart to read the state information and reconnect to JobManagers that were running at the time of the crash.
 If a JobManager fails to respond…

Globus Universe Fault-Tolerance:
Lost Contact with Remote Jobmanager

Globus Universe Fault-Tolerance: Credential Management
Authentication in Globus is done with limited-lifetime X509 proxies
Proxy may expire before jobs finish executing
Condor can put jobs on hold and email user to refresh proxy
Todo: Interface with MyProxy…

But Frieda Wants More…
She wants to run standard universe jobs on Globus-managed resources
For matchmaking and dynamic scheduling of jobs
For job checkpointing and migration
For remote system calls

Solution: Condor GlideIn
Frieda can use the Globus Universe to run Condor daemons on Globus 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 Globus resources

How It Works

How It Works

How It Works

How It Works

How It Works

How It Works

How It Works

Slide 133

GlideIn Concerns
What if a Globus 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

Common Questions, cont.
My Personal Condor is flocking with a bunch of Solaris machines, and also doing a GlideIn to a Silicon Graphics O2K.  I do not want to statically partition my jobs.

In Review
With Condor Frieda can…
… manage her compute job workload
… access local machines
… access remote Condor Pools via flocking
… access remote compute resources on the Grid via Globus Universe jobs
… carve out her own personal Condor Pool from the Grid with GlideIn technology

Slide 137

Case Study: CMS Production
An ongoing collaboration between:
Physicists & Computer Scientists
Vladimir Litvin (Caltech CMS)
Scott Koranda, Bruce Loftis, John Towns (NCSA)
Miron Livny, Peter Couvares, Todd Tannenbaum, Jamie Frey (UW-Madison Condor)
Condor, Globus, CMS

CMS Physics
The CMS detector at the LHC will probe fundamental forces in our Universe and search for the yet-undetected Higgs Boson
Detector expected to come online 2006

CMS Physics

ENORMOUS Data Challenges Ahead
One sec of CMS running will equal data volume equivalent to 10,000 Encyclopaedia Britannicas
Data rate handled by the CMS event builder (~500 Gbit/s) will be equivalent to amount of data currently exchanged by the world's telecom networks
Number of processors in the CMS event filter will equal number of workstations at CERN today (~4000)

Leveraging Grid Resources
The Caltech CMS group is using Grid resources today for detector simulation and data processing prototyping
Even during this simulation and prototyping phase the computational and data challenges are substantial…

Challenges of a CMS Run
CMS run naturally divided into two phases
Specific challenges
each run generates ~100 GB of data to be moved and archived elsewhere
many, many runs necessary
simulation & reconstruction jobs at different sites
this can require major human effort starting & monitoring jobs, moving data

CMS Run on the Grid
Caltech CMS staff prepares input files on local workstation
Pushes “one button” to submit a DAGMan job to Condor
DAGMan job at Caltech submits secondary DAGMan job to UW Condor pool (~700 CPUs)
Input files transferred by Condor to UW pool using Globus GASS file transfer

CMS Run on the Grid
Secondary DAGMan job launches 100 Monte Carlo jobs on Wisconsin Condor pool
each job runs 12~24 hours
each generates ~1GB data
Condor handles checkpointing & migration
no staff intervention

CMS Run on the Grid
When each Monte Carlo job completes, data automatically transferred to UniTree at NCSA by a POST script
each file ~ 1 GB
transferred by calling Globus-enabled FTP client “gsiftp”
NCSA UniTree runs Globus-enabled FTP server
authentication to FTP server on user’s behalf using digital certificate

CMS Run on the Grid
When all Monte Carlo jobs complete, Condor DAGMan at UW reports success to DAGMan at Caltech
DAGMan at Caltech submits another Globus-universe job to Condor to stage data from NCSA UniTree to NCSA Linux cluster
data transferred using Globus-enabled FTP
authentication on user’s behalf using digital certificate

CMS Run on the Grid
Condor DAGMan at Caltech launches physics reconstruction jobs on NCSA Linux cluster
job launched via Globus jobmanager on NCSA cluster
no user intervention required
authentication on user’s behalf using digital certificate

CMS Run on the Grid
When reconstruction jobs at NCSA complete, data automatically archived to NCSA UniTree
data transferred using Globus-enabled FTP
After data transferred, DAGMan run is complete, and Condor at Caltech emails notification to staff

CMS Run Details
Condor + Globus
allows Condor to submit jobs to remote host via a Globus jobmanager
any Globus-enabled host reachable (with authorization)
Condor jobs run in the “Globus” universe
use familiar Condor classads for submitting jobs

CMS Run Details
At Caltech, DAGMan ensures reconstruction job B runs only after simulation job A completes successfully & data is transferred
At UW, no job dependencies, but DAGMan POST scripts used to stage out data

Future Directions
Include additional sites in both steps:
allow Monte Carlo jobs at Wisconsin to “glide-in” to Grid sites not running Condor
add path so that physics reconstruction jobs may run on other sites in addition to NCSA cluster

Slide 153

Thank you!
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