introduction to high performance computing€¦ · high performance computing • supercomputers...

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Introduction to High Performance Computing

Jon JohanssonAcademic ICT

Copyright 2008, University of Alberta

Academic ICTUniversity of Alberta

Agenda

• What is High Performance Computing?• What is a “supercomputer”?• What is a supercomputer ?

• is it a mainframe?• Supercomputer architectures• Who has the fastest computers?• Speedup• Programming for parallel computing

The GRID??


• The GRID??

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High Performance Computing

• HPC is the field that concentrates on developing supercomputers and software to run onsupercomputers and software to run on supercomputers

• a main area of this discipline is developing parallel processing algorithms and software• programs that can be divided into little pieces so that each

piece can be executed simultaneously by separate processors


p


• HPC is about “big problems”, i.e. need:• lots of memorylots of memory• many cpu cycles• big hard drives

• no matter what field you work in, perhaps your research would benefit by making problems “larger”• 2d → 3d• finer mesh

i b f l t i th i l ti


• increase number of elements in the simulation

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Grand Challenges• weather forecasting• economic modelingg• computer-aided design• drug design• exploring the origins of the universe• searching for extra-terrestrial life• computer vision


computer vision• nuclear power and weapons simulations

Grand Challenges – ProteinTo simulate the folding of a 300 amino acid protein in water:# of atoms: ~ 32,000,folding time: 1 millisecond# of FLOPs: 3 × 1022

Machine Speed: 1 PetaFLOP/sSimulation Time: 1 year

(Source: IBM Blue Gene Project)


IBM’s answer: The Blue Gene ProjectUS$ 100 M of funding to build a1 PetaFLOP/s computer

Ken Dil and Kit Lau’s protein folding model.

Charles L Brooks III, Scripps Research Institute

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Grand Challenges - Nuclear• National Nuclear Security

Administration• http://www.nnsa.doe.gov/

• use supercomputers to run three-dimensional codes to simulate instead of test

• address critical problems of materials aging• simulate the environment of

the weapon and try to gauge whether the device


continues to be usable• stockpile science, molecular

dynamics and turbulence calculations

http://archive.greenpeace.org/comms/nukes/fig05.gif

Grand Challenges - Nuclear• March 7, 2002: first full-

system three-dimensional simulations of a nuclear weapon explosion

ASCI White

weapon explosion • simulation used more than

480 million cells (grid: 780x780x780)• if the grid is a cube

• 1,920 processors on IBM ASCI White at the Lawrence Livermore National laboratory


y• 2,931 wall-clock hours

or 122.5 days • 6.6 million CPU hours Test shot “Badger”

Nevada Test Site – Apr. 1953 Yield: 23 kilotons

http://nuclearweaponarchive.org/Usa/Tests/Upshotk.html

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Grand Challenges - Nuclear


• Advanced Simulation and Computing Program (ASC)• http://www.llnl.gov/asc/asc_history/asci_mission.html

Agenda

• What is High Performance Computing?Wh t i “ t ”?• What is a “supercomputer”?• is it a mainframe?

• Supercomputer architectures• Who has the fastest computers?• Speedup• Programming for parallel computing


Programming for parallel computing• The GRID??

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What is a “Mainframe”?

• large and reasonably fast machines• the speed isn't the most important characteristic• the speed isn t the most important characteristic

• high-quality internal engineering and resulting proven reliability

• expensive but high-quality technical support• top-notch security• strict backward compatibility for older software



• these machines can, and do, run successfully for years without interruption (long uptimes)years without interruption (long uptimes)

• repairs can take place while the mainframe continues to run

• the machines are robust and dependable• IBM coined a term advertise the robustness of their

mainframe computers :• Reliability Availability and Serviceability (RAS)


• Reliability, Availability and Serviceability (RAS)

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• Introducing IBM System z9 109• Designed for the On Demand

B iBusiness• IBM is delivering a holistic

approach to systems design• Designed and optimized with a

total systems approach• Helps keep your applications

running with enhanced protection against planned and unplanned outages

• Extended security capabilities


• Extended security capabilities for even greater protection capabilities

• Increased capacity with more available engines per server

What is a Supercomputer??

• at any point in time the term “Supercomputer” refers to the fastest machines currently availableto the fastest machines currently available

• a supercomputer this year might be a mainframe in a couple of years

• a supercomputer is typically used for scientific and engineering applications that must do a great amount of computation


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• the most significant difference between a supercomputer and a mainframe:supercomputer and a mainframe:• a supercomputer channels all its power into executing a few

programs as fast as possible• if the system crashes, restart the job(s) – no great harm

done• a mainframe uses its power to execute many programs

simultaneously• e.g. – a banking system


• must run reliably for extended periods


• to see the worlds “fastest” computers look at • http://www top500 org/• http://www.top500.org/

• measure performance with the Linpack benchmark • http://www.top500.org/lists/linpack.php• solve a dense system of linear equations• the performance numbers give a good indication of peak

performance


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Terminology

• combining a number of processors to run a i ll d i lprogram is called variously:

• multiprocessing• parallel processing• coprocessing


Terminology

• parallel computing – harnessing a bunch of th hi tprocessors on the same machine to run your

computer program• note that this is one machine• generally a homogeneous architecture

• same processors, memory, operating system• all the machines in the Top 500 are in this

category


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Terminology• cluster:

• a set of generally homogeneous machinesi i ll b ilt i l t dit• originally built using low-cost commodity

hardware• to increase density, clusters are now

commonly build with 1-u rack servers or blades

• can use standard network interconnect or high performance interconnect such as I fi ib d M i tInfiniband or Myrinet

• cluster hardware is becoming quite specialized

• thought of as a single machine with a name, e.g. “glacier” – glacier.westgrid.ca


Terminology

• distributed computing - harnessing a bunch f diff t hi tof processors on different machines to run

your computer program• heterogeneous architecture

• different operating systems, cpus, memory• the terms “parallel” and “distributed”

ti ft d i t h blcomputing are often used interchangeably • the work is divided into sections so each

processor does a unique piece


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Terminology

• some distributed computing projects are built BOINC (B k l O I f t t fon BOINC (Berkeley Open Infrastructure for

Network Computing):• SETI@home – Search for Extraterrestrial

Intelligence• Proteins@home – deduces DNA sequence,

given a proteing p• Hydrogen@home – enhance clean energy

technology by improving hydrogen production and storage (this is beta now)


Terminology

• “Grid” computing• a Grid is a cluster of

supercomputers• in the ideal case:

• we submit our job with resource requirements

• the job is run on a machine with available resourcesavailable resources

• we get results back• NOTE: we don’t care where the

resources are, just that the job is run.


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Terminology

• “Utility” computing• computation and storage facilities are• computation and storage facilities are

provided as a commercial service• charges are for resources actually used

– “Pay and Use computing”

• “Cloud” computing• aka “on-demand computing”

• any IT-related capability can be provided as a “service”

• repackages grid computing and utility computing

• users can access computing resources in the “Cloud” – i.e. out in the Internet


How to Measure Speed?

• count the number of “floating point operations” required to solve the problemrequired to solve the problem• + - x /

• results of the benchmark are so many Floating point Operations Per Second (FLOPS)

• a supercomputer is a machine that can provide a very large number of FLOPS


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⎥⎥⎤

⎢⎢⎡

⎥⎥⎤

⎢⎢⎡ NN

2...21

2...21

Floating Point Operations• multiply 2 1000x1000 matrices• for each resulting array element

⎥⎥⎥

⎦⎢⎢⎢

⎣⎥⎥⎥

⎦⎢⎢⎢

⎣ NN...2

...2g y

• 1000 multiplies• 999 adds

• do this 1,000,000 times• ~109 operations needed• increasing array size has the

number of operations increasing as O(N3)


as O(N )

Agenda





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• supercomputers use many CPUs to do the work• note that all supercomputing architectures have• note that all supercomputing architectures have

• processors and some combination cache• some form of memory and IO• the processors are separated from the other processors by

some distance• there are major differences in the way that the parts

are connected


• some problems fit into different architectures better than others


• increasing computing power available to h llresearchers allows

• increasing problem dimensions• adding more particles to a system• increasing the accuracy of the result• improving experiment turnaround time


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Flynn’s Taxonomy

• Michael J. Flynn (1972)• classified computer architectures based on the• classified computer architectures based on the

number of concurrent instructions and data streams available• single instruction, single data (SISD) – basic old PC• multiple instruction, single data (MISD) – redundant systems• single instruction, multiple data (SIMD) – vector (or array)

processor • multiple instruction multiple data (MIMD) shared or


• multiple instruction, multiple data (MIMD) – shared or distributed memory systems: symmetric multiprocessors and clusters

• common extension:• single program (or process), multiple data (SPMD)

Architectures

• we can also classify supercomputers di t h th daccording to how the processors and memory

are connected• couple processors to a single large memory

address space• couple computers, each with its own memory

address space


p

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Architectures• Symmetric

Multiprocessing (SMP)• Uniform Memory Access

(UMA)• multiple CPUs, residing

in one cabinet, share the same memory

• processors and memory are tightly coupled

• the processors share


the processors share memory and the I/O bus or data path

Architectures

• SMP• a single copy of the

operating system is in charge of all the processors

• SMP systems range from two to as many as 32 or more processors


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Architectures

• SMP• SMP• "capability computing"

• one CPU can use all the memory

• all the CPUs can work on a little memory

• whatever you need


whatever you need

Architectures

• UMA-SMP negatives• as the number of CPUs get large the buses

become saturated• long wires cause latency problems


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Architectures

• Non-Uniform Memory Access (NUMA)• NUMA is similar to SMP - multiple CPUs share a singleNUMA is similar to SMP multiple CPUs share a single

memory space• hardware support for shared memory

• memory is separated into close and distant banks• basically a cluster of SMPs

• memory on the same processor board as the CPU (local memory) is accessed faster than memory on other processor boards (shared memory)


• hence "non-uniform"• NUMA architecture scales much better to higher numbers of

CPUs than SMP

Architectures


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Architectures


University of Alberta SGI Origin SGI NUMA cables

Architectures

• Cache Coherent NUMA (ccNUMA)• each CPU has an associated cache• each CPU has an associated cache• ccNUMA machines use special-purpose hardware to

maintain cache coherence • typically done by using inter-processor communication

between cache controllers to keep a consistent memory image when the same memory location is stored in more than one cache

• ccNUMA performs poorly when multiple processors attempt


ccNUMA performs poorly when multiple processors attempt to access the same memory area in rapid succession

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Architectures

Distributed Memory Multiprocessor (DMMP)

h t h it• each computer has its own memory address space

• looks like NUMA but there is no hardware support for remote memory access

• the special purpose switched network is replaced by a general purpose network such as Ethernet or more specialized interconnects:


specialized interconnects: • Infiniband• Myrinet Lattice: Calgary’s HP ES40 and ES45

cluster – each node has 4 processors

Architectures

• Massively Parallel Processing (MPP) Cluster of commodity PCscommodity PCs• processors and memory are loosely coupled• "capacity computing"• each CPU contains its own memory and copy of the

operating system and application. • each subsystem communicates with the others via a high-

speed interconnect.• in order to use MPP effectively, a problem must be


y, pbreakable into pieces that can all be solved simultaneously

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Architectures


Architectures

• lots of “how to build a cluster” tutorials on the b j t G lweb – just Google:

• http://www.beowulf.org/• http://www.cacr.caltech.edu/beowulf/tutorial/b

uilding.html


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Architectures

• Vector Processor or Array Processor• a CPU design that is able to run mathematical operations ona CPU design that is able to run mathematical operations on

multiple data elements simultaneously• a scalar processor operates on data elements one at a

time• vector processors formed the basis of most supercomputers

through the 1980s and into the 1990s• “pipeline” the data


Architectures

• Vector Processor or Array Processor• operate on many pieces of data simultaneouslyp y p y• consider the following add instruction:

• C = A + B • on both scalar and vector machines this means:

• add the contents of A to the contents of B and put the sum in C' • on a scalar machine the operands are numbers• on a vector machine the operands are vectors and the

instruction directs the machine to compute the pair-wise sum of


each pair of vector elements

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Architectures

• University of Victoria has 4 NEC SX-6/8A vector processorsp• in the School of Earth and Ocean

Sciences • each has 32 GB of RAM• 8 vector processors in the box• peak performance is 72 GFLOPS


Agenda





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BlueGene/L

• The fastest on the Nov. 2007 top 500 list:http://www top500 org/• http://www.top500.org/

• installed at the Lawrence Livermore National Laboratory (LLNL) (US Department of Energy)• Livermore California



http://www.llnl.gov/asc/platforms/bluegenel/photogallery.html

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BlueGene/L• processors: 212992• memory: 72 TB• 104 racks – each has 2048 processors

• the first 64 had 512 GB of RAM (256 MB/processor)

• the 40 new racks have 1 TB of RAM (512 MB/processor)

• a Linpack performance of 478.2 TFlop/s


a Linpack performance of 478.2 TFlop/s• in Nov 2005 it was the only system ever to

exceed the 100 TFlop/s mark• there are now 10 machines over 100 TFlop/s

The Fastest FiveSite Computer Cores Year Rmax (Gflops) Rpeak (Gflops)

DOE/NNSA/LANLUnited States

Roadrunner – BladeCenterQS22/LS21 Cluster

122400 2008 1,026,000 1,375,780Cell/OpteronIBM

DOE/NNSA/LLNLUnited States

BlueGene/L - eServer Blue Gene SolutionIBM

212992 2007 478,200 596,378

Argonne National LaboratoryUnited States

BlueGene/P SolutionIBM

163840 2007 450,300 557,060

Texas Advanced Computing Center/Univ. of Texas

Ranger – SunBlade x6420, Opteron Quad 2 GHzSGI

62976 2008 326,000 503,810

United States

DOE/OakridgeNational LaboratoryUnited States

Jaguar – Cray XT4 QuadCoreOpteron 2.1 GHz Hewlett-Packard

30976 2008 205,000 260,000


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# of Processors with Time


The number of processors in the fastest machines has increased by about a factor of 200 in the last 15 years

# of Gflops Increase with Time

O P t fl !One Petaflop!


Machine speed has increased by more than a factor of 15000 since 1993“Roadrunner” tests at > 1 petaflop for June 2008

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Future BlueGene


Roadrunner• cores: 122400

• 6,562 Opteron dual-core, 12,240 Cell• memory: 98 TB• 278 racks• a Linpack performance of 1026.00 TFlop/s• in June 2008 it was the only system ever to

exceed the 1 PetaFlop/s mark


• cost: $100 million• weight: 500,000 lbs• power: 2.35 (or 3.9) megawatts

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Roadrunner


Agenda





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Speedup

• how can we measure how much faster our program runs when using more than one processor? Tg p

• define Speedup S as:• the ratio of 2 program execution times• constant problem size

• T1 is the execution time for the problem on a single processor (use the “best” serial time)

• TP is the execution time for the problem on P processors

PTTS 1=


Speedup

• Linear speedupp p• the time to execute the

problem decreases by the number of processors

• if a job requires 1 week with 1 processor it will take less that 10 minutes with 1024 processors


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Speedup

• Sublinear speedup• the usual case• there are generally some

limitations to the amount of speedup that you get

• communication


Speedup

• Superlinear speedup• very rare• memory access patterns

may allow this for some algorithms


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Speedup• why do a speedup test?• it’s hard to tell how a

program will behave• e.g.

• “Strange” is actually fairly common behaviour for un-tuned code

• in this case:• linear speedup to ~10

cpus


• after 24 cpus speedup is starting to decrease

Speedup

• to use more processors ffi i tl h thiefficiently change this

behaviour• change loop structure • adjust algorithms• ??

• run jobs with 10-20 processors so the machines are used efficiently


are used efficiently

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Speedup

• one class of jobs that have linear speed up are called “embarrassingly parallel”embarrassingly parallel• a better name might be “perfectly” parallel

• doesn’t take much effort to turn the problem into a bunch of parts that can be run in parallel:• parameter searches• rendering the frames in a computer animation• brute force searches in cryptography


• brute force searches in cryptography

Speedup

• we have been discussing Strong Scaling• the problem size is fixed and we increase the number of• the problem size is fixed and we increase the number of

processors• decrease computational time (Amdahl Scaling)

• the amount of work available to each processor decreases as the number of processors increases

• eventually, the processors are doing more communication than number crunching and the speedup curve flattensdiffi lt t h hi h ffi i f l b f


• difficult to have high efficiency for large numbers of processors

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Speedup

• we are often interested in Weak Scaling• double the problem size when we double the number of• double the problem size when we double the number of

processors• constant computational time (Gustafson scaling)

• the amount of work for each processor has stays roughly constant

• parallel overhead is (hopefully) small compared to the real work the processor does


• e.g. Weather prediction

Amdahl’s Law

• Gene Amdahl: 1967• parallelize some of the serial parallelp

program – some must remain serial

• f is the fraction of the calculation that is serial

• 1-f is the fraction of the calculation that is parallel

• the maximum speedup that can be obtained by using P S 1

=

f 1-f

serial parallel


can be obtained by using P processors is:

Pff

S )1(max −+

=

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Amdahl’s Law

• if 25% of the calculation must remain serial th b t d bt i i 4the best speedup you can obtain is 4

• need to parallelize as much of the program as possible to get the best advantage from multiple processors


Agenda





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Parallel Programming

• need to do something to your program to use multiple processorsmultiple processors

• need to incorporate commands into your program which allow multiple threads to run

• one thread per processor• each thread gets a piece of the work• several ways (APIs) to do this


• several ways (APIs) to do this …


• OpenMP• introduce statements into your code• introduce statements into your code

• in C: #pragma• in FORTRAN: C$OMP or !$OMP

• can compile serial and parallel executables from the same source code

• restricted to shared memory machines• not clusters!


• www.openmp.org

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• OpenMP• demo: MatCrunch• demo: MatCrunch

• mathematical operations on the elements of an array• introduce 2 OMP directives before a loop

• # pragma omp parallel // define a parallel section• # pragma omp for // loop is to be parallel

• serial section: 4.03 sec• parallel section – 1 cpu: 40.27 secs

ll l ti 2 20 25


• parallel section – 2 cpu: 20.25 secs• speedup = 1.99 // not bad for adding 2 lines


• for a larger number of processors theprocessors the speedup for MatCrunch is not linear

• need to do the speedup test to see how your program will


how your program will behave

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• MPI (Message Passing Interface)• a standard set of communication subroutine librariesa standard set of communication subroutine libraries

• works for SMPs and clusters• programs written with MPI are highly portable • information and downloads

• http://www.mpi-forum.org/• MPICH: http://www-unix.mcs.anl.gov/mpi/mpich/• LAM/MPI: http://www.lam-mpi.org/

O MPI htt // i /


• Open MPI: http://www.open-mpi.org/


• MPI (Message Passing Interface)t th SPMD i l lti l• supports the SPMD, single program multiple

data model• all processors use the same program• each processor has its own data

• think of a cluster – each node is getting a copy of the program but running a specific


py p g g pportion of it with its own data

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• starting mpi jobs is not standardstandard• for mpich2 use “mpiexec”

• start a job with 6 processes

• 6 copies of the program run in the default Communicator GroupCommunicator Group “MPI_COMM_WORLD”

• each process has an ID – its “rank”



• example: start N processes to calculateprocesses to calculate N-1 factorial

• 0! = 1• 1! = 1• 2! = 2 x 1 = 2• 3! = 3 x 2 x 1 = 6• …• n! = n x (n-1) x…x 2 x 1


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• generally the master process will:• send work to other processes• receive results from processes that complete• send more work to those processes• do final calculations• output results

d i i ffi i t l ith f ll thi i• designing an efficient algorithm for all this is up to you



• it’s possible to combine OpenMP and MPI for running on clusters of SMP machinesrunning on clusters of SMP machines

• the trick in parallel programming is to keep all the processors• working (“load balancing”) • working on data that no other processor needs to

touch (there aren’t any cache conflicts)


• parallel programming is generally harder than serial programming

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Agenda





Grid Computing• A computational grid:

• is a large-scale distributed computing infrastructure• composed of geographically distributed autonomouscomposed of geographically distributed, autonomous

resource providers• lots of computers joined together• requires excellent networking that supports resource

sharing and distribution• offers access to all the resources that are part of the grid

• compute cycles• storage capacity• visualization/collaboration


• visualization/collaboration• is intended for integrated and collaborative use by multiple

organizations

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Grids

• Ian Foster (the “Father of the Grid”) says that to be a Grid three points must be metGrid three points must be met• computing resources are not administered centrally

• many sites connected• open standards are used

• not a proprietary system• non-trivial quality of service is achieved

• it is available most of the timeCERN says a Grid is “a service for sharing computer


• CERN says a Grid is “a service for sharing computer power and data storage capacity over the Internet”

Canadian Academic Computing Sites in 2000


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Canadian Grids

• Some sites in Canada have tied their resources together to form 7 Canadian Grid Consortia:7 Canadian Grid Consortia:• ACENET Atlantic Computational Excellence Network• CLUMEQ Consortium Laval UQAM McGill and Eastern

Quebec for High Performance Computing• SCINET University of Toronto• HPCVL High Performance Computing Virtual

Laboratory• RQCHP Reseau Quebecois de calcul de haute performance• SHARCNET Shared Hierarchical Academic Research


SHARCNET Shared Hierarchical Academic Research Computing Network

• WESTGRID Alberta, British Columbia

WestGrid

EdmontonSFU Campus Edmonton

Calgary

UBC Campus

SFU Campus


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Grids

• the ultimate goal of the Grid idea is to have a system that you can submit a job to, so that:that you can submit a job to, so that:• your job uses resources that fit requirements that you specify

128 nodes on an SMP200 GB of RAM

• or256 nodes on a PC cluster1 GB/processor

• when done the results come back to you


y• you don’t care where the job runs

• Vancouver or St. John’s or in between

Sharing Resources

• HPC resources are not available quite as readily as your desktop computeryour desktop computer

• the resources must be shared fairly• the idea is that each person get as much of the resource as

necessary to run their job for a “reasonable” time• if the job can’t finish in the allotted time the job needs to

“checkpoint”• save enough information to begin running again from


g g g gwhere it left off

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Sharing Resources

• Portable Batch System (T )(Torque)

• submit a job to PBS• job is placed in a queue

with other users’ jobs• jobs in the queue are

prioritized by a scheduler• your job executes at


some time in the future

An HPC Site

Sharing Resources

A Grid of HPC Sites• When connecting to a Grid we need a layer of “middleware” tools to securely access the resources

• Globus is one example• http://www globus org/

A Grid of HPC Sites


• http://www.globus.org/

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Questions?Many details in other sessions of this

seminar series!


introduction to high performance computing€¦ · high performance computing • supercomputers...

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