Antun Balaz, [e-mail]

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High Performance Cluster and Grid Computing

• Antun Balaz, antun.balaz_at_scl.rs
• Scientific Computing Laboratory
• Institute of Physics Belgrade
• Serbia

Introduction to High Performance and Grid
Computing
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Overview

• Introduction to clusters
• High performance computing
• Grid computing paradigm
• Ingredients for Grid development
• Introduction to Grid middleware

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Parallel computing

• Splitting problem in smaller tasks that are
  executed concurrently
• Why?
• Absolute physical limits of hardware components
  (speed of light, electron speed, )
• Economical reasons more complex more expensive
• Performance limits double frequency ltgt double
  performance
• Large applications demand too much memory time
• Advantages Increasing speed optimizing
  resources utilization
• Disadvantages Complex programming models
  difficult development

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Parallelism levels

• CPU
• Multiple CPUs
• Multiple CPU cores
• Threads time sharing
• Memory
• Shared
• Distributed
• Hybrid (virtual shared memory)

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Parallel architectures (1)

• Vector machines
• CPU processes multiple data sets
• shared memory
• advantages performance, programming difficulties
• issues scalability, price
• examples Cray SV, NEC SX, Athlon3/d, Pentium-
  IV/SSE/SSE2
• Massively parallel processors (MPP)
• large number of CPUs
• distributed memory
• advantages scalability, price
• issues performance, programming difficulties
• examples ConnectionSystemsCM1 i CM2, GAAP
  (GeometricArrayParallel Processor)

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Parallel architectures (2)

• Symmetric Multiple Processing (SMP)
• two or more processors
• shared memory
• advantages price, performance, programming
  difficulties
• issues scalability
• examples UltraSparcII, Alpha ES, Generic
  Itanium, Opteron, Xeon,
• Non Uniform Memory Access (NUMA)
• Solving SMPsscalability issue
• hybrid memory model
• advantages scalability
• issues price, performance, programming
  difficulties
• examples SGI Origin/Altix, Alpha GS, HP
  Superdome

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Clusters

• Poors man supercomputer Collection of
  interconnected stand-alone computers working
  together as a single, integrated computing
  resourceR. Buyya
• Cluster consists of
• Nodes
• Network
• OS
• Cluster middleware
• Standard components
• Avoiding expensive proprietary components

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Cluster classification

• High performance clusters (HPC)
• Parallel, tightly coupled applications
• High throughput clusters (HTC)
• Large number of independent tasks
• High availability clusters (HA)
• Mission critical applications
• Load balancing clusters
• Web servers, mail servers,
• Hybrid clusters
• Example HPCHA

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Beowulf clusters

• 1994
• T. Sterling M. Baker
• NASA Ames Centre
• Frontend
• Access machine
• JMS Monitoring server
• Shared storage NFS (directory /home)
• Nodes
• Multiple private networks
• Local storage (/scratch)
• Private networks
• High speed / low latency

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From clusters to Grids

• Many distributed computing resources (clusters)
  exist, even in Serbia
• Problem 1 they cannot be used by end users
  transparently
• Problem 2 even when access is granted to users
  to several clusters, they tend to neglect smaller
  clusters
• Problem 3 distribution of input/output data,
  sharing of data between clusters
• To overcome such problems, Grid paradigm was
  introduced

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Unifying concept Grid
Resource sharing and coordinated problem solving
in dynamic, multi-institutional virtual
organizations.
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Effective policy governing access within a
collaboration
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What problems Grid addresses

• Too hard to keep track of authentication data
  (ID/password) across institutions
• Too hard to monitor system and application status
  across institutions
• Too many ways to submit jobs
• Too many ways to store access files/data
• Too many ways to keep track of data
• Too easy to leave dangling resources lying
  around (robustness)

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Requirements

• Security
• Monitoring/Discovery
• Computing/Processing Power
• Moving and Managing Data
• Managing Systems
• System Packaging/Distribution
• Secure, reliable, on-demand access to data,
  software, people, and other resources (ideally
  all via a Web Browser!)

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Why Grid security is hard (1)

• Resources being used may be valuable the
  problems being solved sensitive
• Both users and resources need to be careful
• Dynamic formation and management of user groups
• Large, dynamic, unpredictable
• Resources and users are often located in distinct
  administrative domains- Cannot assume
  cross-organizational trust agreements
• Different mechanisms credentials

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Why Grid security is hard (2)

• Interactions are not just client/server, but
  service-to-service on behalf of user
• Requires delegation of rights user ? service
• Services may be dynamically instantiated
• Standardization of interfaces to allow for
  discovery, negotiation and use
• Implementation must be broadly available
  applicable
• Standard, well-tested, well-understood
  protocols integrated with wide variety of tools
• Policy from sites, user communities and users
  need to be combined
• Varying formats
• Want to hide as much as possible from
  applications!

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Grids and VOs (1)

• Virtual organizations (VOs) are groups of Grid
  users (authenticated through digital
  certificates)
• VO Management Service (VOMS) serves as a central
  repository for user authorization information,
  providing support for sorting users into a
  general group hierarchy, keeping track of their
  roles,etc.
• VO Manager, according to VO policies and rules,
  authorizes authenticated users to become VO
  members

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Grids and VOs (2)

• Resource centers (RCs) may support one or more
  VOs, and this is how users are authorized to use
  computing, storage and other Grid resources
• VOMS allows flexible approach to AA on the Grid

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User view of the Grid

• User Interface

• User Interface

• Grid services

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Ingredients for GRID development

• Right balance of push and pull factors is needed
• Supply side
• Technology inexpensive HPC resources (linux
  clusters)
• Technology network infrastructure
• Financing domestic, regional, EU, donations
  from industry
• Demand side
• Need for novel eScience applications
• Hunger for number crunching power and storage
  capacity

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Supply side - clusters

• The cheapest supercomputers massively parallel
  PC clusters
• This is possible due to
• Increase in PC processor speed (gt Gflop/s)
• Increase in networking performance (1 Gbs)
• Availability of stable OS (e.g. Linux)
• Availability of standard parallel libraries (e.g.
  MPI)
• Advantages
• Widespread choice of components/vendors, low
  price (by factor 5-10)
• Long warranty periods, easy servicing
• Simple upgrade path
• Disadvantages
• Good knowledge of parallel programming is
  required
• Hardware needs to be adjusted to the specific
  application (network topology)
• More complex administration
• Tradeoff brain power ? ? purchasing power
• The next step is GRID
• Distributed computing, computing on demand
• Should do for computing the same as the Internet
  did for information (UK PM, 2002)

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Supply side - network

• Needed at all scales
• World-wide
• Pan-European (GEANT2)
• Regional (SEEREN2, )
• National (NREN)
• Campus-wide (WAN)
• Building-wide (LAN)
• Remember it is end user to end user connection
  that matters

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GÉANT2 Pan-European IP RE network
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GÉANT2 Global Connectivity
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Future development regional network
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Supply side - financing

• National funding (Ministries responsible for
  research)
• Lobby gvnmt. to commit to Lisbon targets
• Level of financing should be following an
  increasing trend (as a of GDP)
• Seek financing for clusters and network costs
• Bilateral projects and donations
• Regional initiatives
• Networking (HIPERB)
• Action Plan for RD in SEE
• EU funding
• FP6 IST priority, eInfrastructures GRIDs
• FP7
• CARDS
• Other international sources (NATO, )
• Donations from industry (HP, SUN, )

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Demand side - eScience

• Usage of computers in science
• Trivial text editing, elementary visualization,
  elementary quadrature, special functions, ...
• Nontrivial differential eq., large linear
  systems, searching combinatorial spaces, symbolic
  algebraic manipulations, statistical data
  analysis, visualization, ...
• Advanced stochastic simulations, risk
  assessment in complex systems, dynamics of the
  systems with many degrees of freedom, PDE
  solving, calculation of partition
  functions/functional integrals, ...
• Why is the use of computation in science growing?
• Computational resources are more and more
  powerful and available (Moores law)
• Standard approaches are having problemsExperiment
  s are more costly, theory more difficult
• Emergence of new fields/consumers finance,
  economy, biology, sociology
• Emergence of new problems with unprecedented
  storage and/or processor requirements

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Demand side - consumers

• Those who study
• Complex discrete time phenomena
• Nontrivial combinatorial spaces
• Classical many-body systems
• Stress/strain analysis, crack propagation
• Schrodinger eq diffusion eq.
• Navier-Stokes eq. and its derivates
• functional integrals
• Decision making processes w. incomplete
  information
• Who can deliver? Those with
• Adequate training in mathematics/informatics
• Stamina needed for complex problems solving
• Answer rocket scientists (natural sciences and
  engineering)

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Scenario
User interface
stderr.txt
stdout.txt
stderr.txt
stdout.txt
publish state
Input sandbox
Output sandbox
A worker node is allocated by the local jobmanager
Logging and bookkeeping

• STD input stream is read from file
• STD out and err. streams are redirected into
  files

stderr.txt
/bin/hostname
Computing Element
stdout.txt