High Performance Computing: Concepts, Methods & Means High Capacity (Throughput) Computing

High Performance Computing: Concepts, Methods & MeansHigh Capacity (Throughput) Computing Prof. Thomas Sterling Department of Computer Science Louisiana State University January 25th, 2007

Topics Key terms and concepts Basic definitions Models of parallelism Speedup and Overhead Capability Computing & Unix utilities Condor : Overview Condor : Useful commands Performance Issues in Capacity Computing Material for Test

Topics • Key terms and concepts • Basic definitions • Models of parallelism • Speedup and Overhead • Capability Computing & Unix utilities • Condor : Overview • Condor : Useful commands • Performance Issues in Capacity Computing • Material for Test

Key Terms and Concepts Parallel execution of a problem involves partitioning of the problem into multiple executable parts that are mutually exclusive and collectively exhaustive represented as a partially ordered set exhibiting concurrency. Conventional serial executionwhere the problem is represented as a series of instructions that are executed by the CPU Problem Problem Task Task Task Task CPU • Parallel computing takes advantage of concurrency to : • Solve larger problems within bounded time • Save on Wall Clock Time • Overcoming memory constraints • Utilizing non-local resources instructions instructions CPU CPU CPU CPU

Key Terms and Concepts Speedup : Relative reduction of execution time of a fixed size workload through parallel execution Efficiency : Ratio of the actual performance to the best possible performance.

Defining the 3 C’s … • Main Classes of computing : • High capacity parallel computing: A strategy for employing distributed computing resources to achieve high throughput processing among decoupled tasks. Aggregate performance of the total system is high if sufficient tasks are available to be carried out concurrently on all separate processing elements. No single task is accelerated. • High capability parallel computing : A strategy for employing tightly couple structures of computing resources to achieve reduced execution time of a given application through partitioning in to concurrently executable tasks. • Cooperative computing : A strategy for employing moderately coupled ensemble of computing resources to increase size of the data set of a user application while limiting its execution time.

Defining the 3 C’s … Adapted from : High-performance throughput computingS Chaudhry, P Caprioli, S Yip, M Tremblay - IEEE Micro, 2005 - doi.ieeecomputersociety.org High capacity computing systems emphasize the overall work performed over a fixed time period. Work is defined as the aggregate amount of computation performed across all functional units, all threads, all cores, all chips, all coprocessors and network interface cards in the system. High capability computing systems emphasize improvement (reduction) in execution time of a single user application program of fixed data set size.

Models of Parallel Processing • Conventional models of parallel processing • Decoupled Work Queue (covered in segment 1) • Shared memory multiple thread (covered in segment 2) • Communicating Sequential Processing (CSP message passing) (covered in segment 3) • Alternative models of parallel processing • SIMD • Single instruction stream multiple data stream processor array • Vector Machines • Hardware execution of value sequences to exploit pipelining • Systolic • An interconnection of basic arithmetic units to match algorithm • Data Flow • Data precedent constraint self-synchronizing fine grain execution units supporting functional (single assignment) execution

Decoupled Work Queue Model • Concurrent disjoint tasks • Parametric Studies • SPMD (single program multiple data) • Very coarse grained • Example software package : Condor • Processor farms and clusters • Last part of Segment 1 covers this model of parallelism

Shared memory multiple Thread CPU 1 CPU 2 CPU 3 Network memory memory memory Symmetric Multi Processor (SMP usually cache coherent ) CPU 1 CPU 2 CPU 3 Orion JPL NASA memory memory memory Network Distributed Shared Memory (DSM often not cache coherent) Static or dynamic Fine Grained OpenMP Distributed shared memory systems Covered in Segment 2

Caches and Cache Coherence • Caches are part of memory hierarchy • Match high processor demand to high capacity, long access time main memory • Caches are low capacity (relatively) short access time • Caches hold temporary copies of data in memory but they can’t hold all of it at any one time • Processors may share memory but caches are private • Cache coherence keeps copies of the same data consistent across caches of different processors • This is tough to do, slows things down, and doesn’t scale very well SIDE BAR Discussion

Shared Memory Multiple Thread Model • Hardware view : All system memory directly accessible from all processing elements, possibly with cache coherence. Instruction streams performed concurrently, possibly switching contexts for shared resources. • Software view : A flow of control in a process that can have concurrent execution paths. Threads share the same address space and have self contained state information. Synchronization achieved through shared variables in memory. • Advantages : • Threads are inexpensive to create, represent and destroy. • Relatively faster to switch context between threads than processes. • Better resource management due to shared address spaces utilization • Disadvantages : • Cache coherent and symmetric multiprocessor systems not scalable • Scalable systems are none uniform memory access (NUMA) • System call through threads can potentially block the process and consequently degrading CPU utilization. • More in-depth coverage on this topic in segment 2 of the course

Communicating Sequential Processes memory memory memory CPU 1 CPU 2 CPU 3 Network Distributed Memory (DM often not cache coherent) SuperMike LSU • One process is assigned to each processor • Work done by the processor is performed on the local data • Data values are exchanged by messages • Synchronization constructs for inter process coordination • Distributed Memory • Coarse Grained • MPI • Clusters and MPP • MPP is acronym for “Massively Parallel Processor” • Covered in Segment 3

Ideal Speedup Example W w1 w210 210 Units : steps P28 Processors P1 220 210 210 210 210 T(1)=220 T(28)=212 212

Ideal Speedup Issues W is total workload measured in elemental pieces of work (e.g. operations, instructions, etc.) T(p) is total execution time measured in elemental time steps (e.g. clock cycles) where p is # of execution sites (e.g. processors, threads) wi is work for a given task i Example: here we divide a million (really Mega) operation workload, W, in to a thousand tasks, w1 to w1024 each of a 1 K operations Assume 256 processors performing workload in parallel T(256) = 4096 steps, speedup = 256, Eff = 1

Granularities in Parallelism Coarse Grained Computation Overhead Finely Grained Computation Overhead Overhead • The additional work that needs to be performed in order to manage the parallel resources and concurrent abstract tasks that is in the critical time path. Coarse Grained • Decompose problem into large independent tasks. Usually there is no communication between the tasks. Also defined as a class of parallelism where: “relatively large amounts of computational work is done between communication” Fine Grained • Decompose problem into smaller inter-dependent tasks. Usually these tasks are usually communication intensive. Also defined as a class of parallelism where: “relatively small amounts of computational work is done between communication events” –www.llnl.gov/computing/tutorials/parallel_comp Images adapted from : http://www.mhpcc.edu/training/workshop/parallel_intro/

Overhead Assumption : Workload is infinitely divisible v = overhead w = work unit W = Total work Ti = execution time with i processors P = # processors v w W=4v+4w

Overhead Overhead: Additional critical path (in time) work required to manage parallel resources and concurrent tasks that would not be necessary for purely sequential execution V is total overhead of workload execution vi is overhead for individual task wi Each task takes v+w time steps to complete Overhead imposes upper bound on scalability

Scalability & Overhead when W >> v v = overhead wg = work unit W = Total work Ti = execution time with i processors P = # Processors J = # Tasks

Scalability and Overhead for fixed sized work tasks W is divided in to Jwg sized tasks Each task requires v overhead work to manage For P processors there are approximates J/P tasks to be performed in sequence so, TP is J(wg + v)/P Note that S = T1 / TP So, S = P / (1 + v / wg)

Capacity Computing with basic Unix tools • Combination of common Unix utilities such as ssh, scp, rsh, rcp can be used to remotely create jobs. ( to get more information about these commands try man ssh, man scp, man rsh, man rcp on any Unix shell ) • For small workloads it can be convenient to translate the execution of the program into a simple shell script. • Relying on simple Unix utilities poses several application management constraints for cases such as : • Aborting started jobs • Querying for free machines • Querying for job status • Retrieving job results • etc..

Demo 1 Using Unix utilities to executing Capability Computing

BOINC , Seti@Home BOINC (Berkley Open Infrastructure for Network Computing) Opensource software that enables distributed coarse grained computations over the internet. Follows the Master-Worker model, in BOINC : no communication takes place among the worker nodes SETI@Home Einstein@Home Climate prediction And many more…

Management Middleware : Condor • Designed, developed and maintained at University of Wisconsin Madison by a team lead by Miron Livny • Condor is a versatile workload management system for managing pool of distributed computing resources to provide high capacity computing. • Assists job management by providing mechanisms for job queuing, scheduling, priority management, tools that facilitate utilization of resources across Condor pools • Condor also enables resource management by providing monitoring utilities, authentication & authorization mechanisms, condor pool management utilities and support for Grid Computing middlewares such as Globus. • Condor Components • ClassAds • Matchmaker • Problem Solvers

Management Middleware : Condor Src : Douglas Thain, Todd Tannenbaum, and MironLivny, "Distributed Computing in Practice: The Condor Experience" Concurrency and Computation: Practice and Experience, Vol. 17, No. 2-4, pages 323-356, February-April, 2005. http://www.cs.wisc.edu/condor/doc/condor-practice.pdf Condor Components : Class Ads ClassAds (Classified Advertisements) concept is very similar to the newspaper classifieds concepts where buyers and sellers advertise their products using abstract yet uniquely defining named expressions. Example : Used Car Sales ClassAds language in Condor provides well defined means of describing the User Job and the end resources ( storage / computational ) so that the Condor MatchMaker can match the job with the appropriate pool of resources.

Job ClassAd & Machine ClassAd

Management Middleware : Condor Src : Douglas Thain, Todd Tannenbaum, and MironLivny, "Distributed Computing in Practice: The Condor Experience" Concurrency and Computation: Practice and Experience, Vol. 17, No. 2-4, pages 323-356, February-April, 2005. http://www.cs.wisc.edu/condor/doc/condor-practice.pdf Condor MatchMaker • MatchMaker, a crucial part of the Condor architecture, uses the job description classAd provided by the user and matches the Job to the best resource based on the Machine description classAd • MatchMaking in Condor is performed in 4 steps : • Job Agent (A) and resources (R) advertise themselves. • Matchmaker (M) processes the known classAds and generates pairs that best match resources and jobs • Matchmaker informs each party of the job-resource pair of their prospective match. • The Job agent and resource establish connection for further processing. (Matchmaker plays no role in this step, thus ensuring separation between selection of resources and subsequent activities)

Management Middleware : Condor Master w1 w..N Condor Problem Solvers Master-Worker (MW)is a problem solving system that is useful for solving a coarse grained problem of indeterminate size such as parameter sweep etc. The MW Solver in Condor consists of 3 main components : work-list, a tracking module, and a steering module. The work-list keeps track of all pending work that master needs done. The tracking module monitors progress of work currently in progress on the worker nodes. The steering module directs computation based on results gathered and the pending work-list and communicates with the matchmaker to obtain additional worker processes. DAGManis used to execute multiple jobs that have dependencies represented as a Directed Acyclic Graph where the nodes correspond to the jobs and edges correspond to the dependencies between the jobs. DAGMan provides various functionalities for job monitoring and fault tolerance via creation of rescue DAGs.

Core components of Condor Source : http://www.cs.wisc.edu/condor/tutorials/cw2005-condor/intro.html condor_master: This program runs constantly and ensures that all other parts of Condor are running. If they hang or crash, it restarts them. condor_collector: This program is part of the Condor central manager. It collects information about all computers in the pool as well as which users want to run jobs. It is what normally responds to the condor_status command. It's not running on your computer, but on the main Condor pool host (Celeritas head node). condor_negotiator: This program is part of the Condor central manager. It decides what jobs should be run where. It's not running on your computer, but on on the main Condor pool host (Celeritas head node). condor_startd: If this program is running, it allows jobs to be started up on this computer--that is, hal is an "execute machine". This advertises hal to the central manager (more on that later) so that it knows about this computer. It will start up the jobs that run. condor_schedd If this program is running, it allows jobs to be submitted from this computer--that is, hal is a "submit machine". This will advertise jobs to the central manager so that it knows about them. It will contact a condor_startd on other execute machines for each job that needs to be started. condor_shadow For each job that has been submitted from this computer, there is one condor_shadow running. It will watch over the job as it runs remotely. In some cases it will provide some assistance You may or may not see any condor_shadow processes running, depending on what is happening on the computer when you try it out.

Condor : A Walkthrough of Condor commands condor_status : provides current pool status condor_q : provides current job queue condor_submit : submit a job to condor pool condor_rm : delete a job from job queue

What machines are available ? (condor_status) • Some common condor_status command line options : • -help : displays usage information • -avail : queries condor_startd ads and prints information about available resources • -claimed : queries condor_startd ads and prints information about claimed resources • -ckptsrvr : queries condor_ckpt_server ads and display checkpoint server attributes • -pool hostname queries the specified central manager (by default queries $COLLECTOR_HOST) • -verbose : displays entire classads • For more options and what they do run “condor_status –help” condor_status queries resource information sources and provides the current status of the condor pool of resources

condor_status : Resource States Owner : The machine is currently being utilized by a user. The machine is currently unavailable for jobs submitted by condor until the current user job completes. Claimed : Condor has selected the machine for use by other users. Unclaimed : Machine is unused and is available for selection by condor. Matched : Machine is in a transition state between unclaimed and claimed Preempting : Machine is currently vacating the resource to make it available to condor.

Example : condor_status [cdekate@celeritas ~]$ condor_status Name OpSys Arch State Activity LoadAv Mem ActvtyTime vm1@compute-0 LINUX X86_64 Unclaimed Idle 0.000 1964 3+13:42:23 vm2@compute-0 LINUX X86_64 Unclaimed Idle 0.000 1964 3+13:42:24 vm3@compute-0 LINUX X86_64 Unclaimed Idle 0.010 1964 0+00:45:06 vm4@compute-0 LINUX X86_64 Owner Idle 1.000 1964 0+00:00:07 vm1@compute-0 LINUX X86_64 Unclaimed Idle 0.000 1964 3+13:42:25 vm2@compute-0 LINUX X86_64 Unclaimed Idle 0.000 1964 1+09:05:58 vm3@compute-0 LINUX X86_64 Unclaimed Idle 0.000 1964 3+13:37:27 vm4@compute-0 LINUX X86_64 Unclaimed Idle 0.000 1964 0+00:05:07 … … vm3@compute-0 LINUX X86_64 Unclaimed Idle 0.000 1964 3+13:42:33 vm4@compute-0 LINUX X86_64 Unclaimed Idle 0.000 1964 3+13:42:34 Total Owner Claimed Unclaimed Matched Preempting Backfill X86_64/LINUX 32 3 0 29 0 0 0 Total 32 3 0 29 0 0 0

What jobs are currently in the queue? condor_q • Some common condor_q command line options : • -global : queries all job queues in the pool • -name : queries based on the schedd name provides a queue listing of the named schedd • -claimed : queries condor_startd ads and prints information about claimed resources • -goodput : displays job goodput statistics (“goodputis the allocation time when an application uses a remote workstation to make forward progress.” – Condor Manual) • -cputime : displays the remote CPU time accumulated by the job to date... • For more options run : “condor_q –help” condor_q provides a list of job that have been submitted to the Condor pool Provides details about jobs including which cluster the job is running on, owner of the job, memory consumption, the name of the executable being processed, current state of the job, when the job was submitted and how long has the job been running.

Example : condor_q [cdekate@celeritas ~]$ condor_q -- Submitter: celeritas.cct.lsu.edu : <130.39.128.68:40472> : celeritas.cct.lsu.edu ID OWNER SUBMITTED RUN_TIME ST PRI SIZE CMD 30.0 cdekate 1/23 07:52 0+00:01:13 R 0 9.8 fib 100 30.1 cdekate 1/23 07:52 0+00:01:09 R 0 9.8 fib 100 30.2 cdekate 1/23 07:52 0+00:01:07 R 0 9.8 fib 100 30.3 cdekate 1/23 07:52 0+00:01:11 R 0 9.8 fib 100 30.4 cdekate 1/23 07:52 0+00:01:05 R 0 9.8 fib 100 5 jobs; 0 idle, 5 running, 0 held [cdekate@celeritas ~]$

How to submit your Job ? condor_submit [cdekate@celeritas NPB3.2-MPI]$ condor_submit -h Usage: condor_submit [options] [cmdfile] Valid options: -verbose verbose output -name <name> submit to the specified schedd -remote <name> submit to the specified remote schedd (implies -spool) -append <line> add line to submit file before processing (overrides submit file; multiple -a lines ok) -disable disable file permission checks -spool spool all files to the schedd -password <password> specify password to MyProxy server -pool <host> Use host as the central manager to query If [cmdfile] is omitted, input is read from stdin • Create a job classAd (condor submit file) that contains Condor keywords and user configured values for the keywords. • Submit the job classAd using “condor_submit” • Example : condor_submit matrix.submit • condor_submit –h provides additional flags

condor_submit : Example [cdekate@celeritas ~]$ condor_submit fib.submit Submitting job(s)..... Logging submit event(s)..... 5 job(s) submitted to cluster 35. [cdekate@celeritas ~]$ condor_q -- Submitter: celeritas.cct.lsu.edu : <130.39.128.68:51675> : celeritas.cct.lsu.edu ID OWNER SUBMITTED RUN_TIME ST PRI SIZE CMD 35.0 cdekate 1/24 15:06 0+00:00:00 I 0 9.8 fib 10 35.1 cdekate 1/24 15:06 0+00:00:00 I 0 9.8 fib 15 35.2 cdekate 1/24 15:06 0+00:00:00 I 0 9.8 fib 20 35.3 cdekate 1/24 15:06 0+00:00:00 I 0 9.8 fib 25 35.4 cdekate 1/24 15:06 0+00:00:00 I 0 9.8 fib 30 5 jobs; 5 idle, 0 running, 0 held [cdekate@celeritas ~]$

How to delete a submitted job ? condor_rm [cdekate@celeritas ~]$ condor_rm -h Usage: condor_rm [options] [constraints] where [options] is zero or more of: -help Display this message and exit -version Display version information and exit -name schedd_name Connect to the given schedd -pool hostname Use the given central manager to find daemons -addr <ip:port> Connect directly to the given "sinful string" -reason reason Use the given RemoveReason -forcex Force the immediate local removal of jobs in the X state (only affects jobs already being removed) and where [constraints] is one or more of: cluster.proc Remove the given job cluster Remove the given cluster of jobs user Remove all jobs owned by user -constraint expr Remove all jobs matching the boolean expression -all Remove all jobs (cannot be used with other constraints) [cdekate@celeritas ~]$ condor_rm : Deletes one or more jobs from Condor job pool. If a particular Condor pool is specified as one of the arguments then the condor_schedd matching the specification is contacted for job deletion, else the local condor_schedd is contacted.

condor_rm : Example [cdekate@celeritas ~]$ condor_q -- Submitter: celeritas.cct.lsu.edu : <130.39.128.68:51675> : celeritas.cct.lsu.edu ID OWNER SUBMITTED RUN_TIME ST PRI SIZE CMD 41.0 cdekate 1/24 15:43 0+00:00:03 R 0 9.8 fib 100 41.1 cdekate 1/24 15:43 0+00:00:01 R 0 9.8 fib 150 41.2 cdekate 1/24 15:43 0+00:00:00 R 0 9.8 fib 200 41.3 cdekate 1/24 15:43 0+00:00:00 R 0 9.8 fib 250 41.4 cdekate 1/24 15:43 0+00:00:00 R 0 9.8 fib 300 5 jobs; 0 idle, 5 running, 0 held [cdekate@celeritas ~]$ condor_rm 41.4 Job 41.4 marked for removal [cdekate@celeritas ~]$ condor_rm 41 Cluster 41 has been marked for removal. [cdekate@celeritas ~]$

Creating Condor submit file ( Job a ClassAd ) executable = (path to the executable to run on Condor) input = (standard input provided as a file) output = (standard output stored in a file log = (output to log file ) arguments = (arguments to be supplied to the ) queue Condor submit file contains key-value pairs that help describe the application to condor. Condor submit files are job ClassAds. Some of the common descriptions found in the job ClassAds are :

DEMO 2 : Steps involved in running a job on Condor. Creating a Condor submit file Submitting the Condor submit file to a Condor pool Checking the current state of a submitted job Job status Notification

Condor Usage Statistics

Example DAG for 10 input files Maps an abstract workflow to an executable form mProject Pegasus http://pegasus.isi.edu/ mDiff mFitPlane mConcatFit mBgModel Grid Information Systems mBackground Information about available resources, data location mAdd Data Stage-in nodes Condor DAGMan Montage compute nodes Executes the workflow Data stage-out nodes Grid MyProxy Registration nodes User’s grid credentials Montage workload implemented and executed using Condor ( Source : Dr. Dan Katz ) Mosaicking astronomical images : Powerful Telescopes taking high resolution (and highest zoom) pictures of the sky can cover small region over time Problem being solved in this project is “stitching” these images together to make a high-resolution zoomed in snapshot of the sky. Aggregate requirements of 140000 CPU hours (~16 years on a single machine) output ranging in the order of 6 TeraBytes

Montage Use By IPHAS: The INT/WFC Photometric H-alpha Survey of the Northern Galactic Plane (Source : Dr. Dan Katz) Nebulosity in vicinity of HII region, IC 1396B, in Cepheus Crescent Nebula NGC 6888 Study extreme phases of stellar evolution that involve very large mass loss Supernova remnant S147

High Performance Computing: Concepts, Methods & Means High Capacity (Throughput) Computing