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In Summary
Need more computing power Improve the operating speed of processors
& other components constrained by the speed of light,
thermodynamic laws, & the high financial costs for processor fabrication
Connect multiple processors together & coordinate their computational efforts
parallel computers allow the sharing of a computational task
among multiple processors
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Technology Trends...
Performance of PC/Workstations components has almost reached performance of those used in supercomputers… Microprocessors (50% to 100% per year) Networks (Gigabit SANs); Operating Systems (Linux,...); Programming environment (MPI,…); Applications (.edu, .com, .org, .net, .shop, .bank);
The rate of performance improvements of commodity systems is much rapid compared to specialized systems.
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Trend
[Traditional Usage] Workstations with Unix for science & industry vs PC-based machines for administrative work & work processing
[Trend] A rapid convergence in processor performance and kernel-level functionality of Unix workstations and PC-based machines
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Rise and Fall of Computer Architectures
Vector Computers (VC) - proprietary system: provided the breakthrough needed for the emergence of
computational science, buy they were only a partial answer.
Massively Parallel Processors (MPP) -proprietary systems:
high cost and a low performance/price ratio. Symmetric Multiprocessors (SMP):
suffers from scalability Distributed Systems:
difficult to use and hard to extract parallel performance. Clusters - gaining popularity:
High Performance Computing - Commodity Supercomputing
High Availability Computing - Mission Critical Applications
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The Dead Supercomputer Societyhttp://www.paralogos.com/DeadSup
er/ ACRI Alliant American
Supercomputer Ametek Applied Dynamics Astronautics BBN CDC Convex Cray Computer Cray Research
(SGI?Tera) Culler-Harris Culler Scientific Cydrome
Convex C4600
Dana/Ardent/Stellar Elxsi ETA Systems Evans & Sutherland
Computer Division Floating Point Systems Galaxy YH-1 Goodyear Aerospace
MPP Gould NPL Guiltech Intel Scientific
Computers Intl. Parallel Machines KSR MasPar
Meiko Myrias Thinking
Machines Saxpy Scientific
Computer Systems (SCS)
Soviet Supercomputers
Suprenum
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Computer Food Chain: Causing the demise of specialize systems
•Demise of mainframes, supercomputers, & MPPs
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Towards Commodity Parallel Computing
linking together two or more computers to jointly solve computational problems
since the early 1990s, an increasing trend to move away from expensive and specialized proprietary parallel supercomputers towards clusters of workstations
Hard to find money to buy expensive systems the rapid improvement in the availability of commodity
high performance components for workstations and networks
Low-cost commodity supercomputing from specialized traditional supercomputing platforms
to cheaper, general purpose systems consisting of loosely coupled components built up from single or multiprocessor PCs or workstations
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Why PC/WS Clustering Now ?
Individual PCs/workstations are becoming increasing powerful
Commodity networks bandwidth is increasing and latency is decreasing
PC/Workstation clusters are easier to integrate into existing networks
Typical low user utilization of PCs/WSs Development tools for PCs/WS are more mature PC/WS clusters are a cheap and readily available Clusters can be easily grown
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What is Cluster ?
A cluster is a type of parallel or distributed processing system, which consists of a collection of interconnected stand-alone computers cooperatively working together as a single, integrated computing resource.
A node a single or multiprocessor system with memory, I/O
facilities, & OS generally 2 or more computers (nodes) connected
together in a single cabinet, or physically separated & connected
via a LAN appear as a single system to users and applications provide a cost-effective way to gain features and benefits
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Cluster Architecture
Sequential Applications
Parallel Applications
Parallel Programming Environment
Cluster Middleware
(Single System Image and Availability Infrastructure)
Cluster Interconnection Network/Switch
PC/Workstation
Network Interface Hardware
Communications
Software
PC/Workstation
Network Interface Hardware
Communications
Software
PC/Workstation
Network Interface Hardware
Communications
Software
PC/Workstation
Network Interface Hardware
Communications
Software
Sequential Applications
Sequential Applications
Parallel ApplicationsParallel
Applications
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So What’s So Different about Clusters?
Commodity Parts? Communications Packaging? Incremental Scalability? Independent Failure? Intelligent Network Interfaces? Complete System on every node
virtual memory scheduler files …
Nodes can be used individually or jointly...
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Windows of Opportunities
Parallel Processing Use multiple processors to build MPP/DSM-like systems for
parallel computing Network RAM
Use memory associated with each workstation as aggregate DRAM cache
Software RAID Redundant array of inexpensive disks Use the arrays of workstation disks to provide cheap, highly
available, & scalable file storage Possible to provide parallel I/O support to applications Use arrays of workstation disks to provide cheap, highly
available, and scalable file storage Multipath Communication
Use multiple networks for parallel data transfer between nodes
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• Enhanced Performance (performance @ low cost)
• Enhanced Availability (failure management)
• Single System Image (look-and-feel of one system)
• Size Scalability (physical & application)
• Fast Communication (networks & protocols)
• Load Balancing (CPU, Net, Memory, Disk)
• Security and Encryption (clusters of clusters)
• Distributed Environment (Social issues)
• Manageability (admin. And control)
• Programmability (simple API if required)
• Applicability (cluster-aware and non-aware app.)
Cluster Design Issues
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Summary: Cluster Advantage
Price/performance ratio is low when compared with a dedicated parallel supercomputer.
Incremental growth that often matches with the demand patterns.
The provision of a multipurpose system Scientific, commercial, Internet applications
Have become mainstream enterprise computing systems: In 2003 List of Top 500 Supercomputers, over
50% of them are based on clusters and many of them are deployed in industries.
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