Introduction to High Performance and Grid Computing

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Antun Balaz, antun.balaz@scl.rs Scientific Computing Laboratory Institute of Physics Belgrade SerbiaHigh Performance Cluster and Grid ComputingIntroduction to High Performance and Grid Computing

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

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Parallel computingSplitting 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 <> 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 levelsCPU 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 SMP’sscalability issue hybrid memory model advantages: scalability issues: price, performance, programming difficulties examples: SGI Origin/Altix, Alpha GS, HP Superdome

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ClustersPoor’s man supercomputer “…Collection of interconnected stand-alone computers working together as a single, integrated computing resource”–R. Buyya Cluster consists of: Nodes Network OS Cluster middleware Standard components Avoiding expensive proprietary components

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Cluster classificationHigh 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: HPC+HA

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Beowulf clusters1994 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 GridsMany 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: GridResource 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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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) What problems Grid addresses

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RequirementsSecurity 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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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 Why Grid security is hard (1)

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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 A&A on the Grid

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User view of the GridUser InterfaceGrid servicesUser Interface

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Ingredients for GRID developmentRight 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 - clustersThe cheapest supercomputers – massively parallel PC clusters This is possible due to: Increase in PC processor speed (> 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 - networkNeeded 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 R&E network

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GÉANT2 Global Connectivity

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Future development: regional network

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Supply side - financingNational 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 R&D in SEE EU funding FP6 – IST priority, eInfrastructures & GRIDs FP7 CARDS Other international sources (NATO, …) Donations from industry (HP, SUN, …)

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Last Updated: 8th March 2018

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