Condor Tutorial
First EuroGlobus Workshop
June 2001

Tutorial Outline

	Overview
	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-G
	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!!!

	Sender: goux@dantec.ece.nwu.edu 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 !
	cheers,
	JP.

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

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: 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
	%

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 = email@address.here

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
Screenshot

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: 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+00:02:11 R  0   0.0  my_job
	% condor_prio +5 1.0
	% 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+00:02:13 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

DAGMan

	Directed Acyclic Graph Manager
	DAGMan allows you to specify the dependencies between your Condor jobs, so it can manage them automatically for you.
	(e.g., “Don’t run job “B” until job “A” has completed successfully.”)

What is a DAG?

	A DAG is the data structure used by DAGMan to represent these dependencies.
	Each job is a “node” in the DAG.
	Each node can have any number of “parent” or “children” nodes – as long as there are no loops!

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
		Fork()
		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

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
	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.

condor_startd

	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)

condor_schedd

	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, …

condor_collector

	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)

condor_negotiator

	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

condor_status

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
	queue

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
	RANK  =
	SUSPEND  = False
	CONTINUE  =
	PREEMPT = False
	KILL  = False

Freida’s New Settings for the Chemistry nodes

	START = True
	RANK  = Department == “Chemistry”
	SUSPEND  = False
	CONTINUE  =
	PREEMPT = False
	KILL  = False

Submit file with Custom Attribute

	Executable = charm-run
	Universe = standard
	+Department = Chemistry
	queue

What if “Department” not specified?

	START = True
	RANK  = Department =!= UNDEFINED && Department == “Chemistry”
	SUSPEND  = False
	CONTINUE  =
	PREEMPT = False
	KILL  = False

Another example

	START = True
	RANK  = Department =!= UNDEFINED && ((Department == “Chemistry”)*2 + Department == “Physics”)
	SUSPEND  = False
	CONTINUE  =
	PREEMPT = 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

	Globus
	middleware deployed across entire Grid
	remote access to computational resources
	dependable, robust data transfer
	Condor
	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 = beak.cs.wisc.edu/jobmanager
		executable   = progname
		queue

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)
		Software
			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!

	Check us out on the Web:
	http://www.cs.wisc.edu/condor
	Email:
	condor-admin@cs.wisc.edu