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Outline

Outline. Course Administration Parallel Archtectures Overview Details Applications Special Approaches Our Class Computer Four Bad Parallel Algorithms. Parallel Computer Architectures. MPP – Massively Parallel Processors

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Outline

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  1. Outline • Course Administration • Parallel Archtectures • Overview • Details • Applications • Special Approaches • Our Class Computer • Four Bad Parallel Algorithms

  2. Parallel Computer Architectures • MPP – Massively Parallel Processors • Top of the top500 list consists of mostly mpps but clusters are “rising” • Clusters • “simple” cluster (1 processor in each node) • Cluster of small smp’s (small # processors / node) • Constellations (large # processors / node) • Older Architectures • SIMD – Single Instruction Multiple Data (CM2) • Vector Processors (Old Cray machines)

  3. Architecture Details • MPPs are built with speicalized networks by vendors with the intent of being used as a parallel computer. Clusters are built from independent computers integrated through an aftermarket network. • Buzzwords: “COTS” Commodity off the shelf componets rather than custom archictures. • Clusters are a market reactions to MPPS with the thought of being cheaper • Originally considered to have solower communications but are catching up.

  4. More details • 2. “NOW”-Networks of Workstations • Beowulf (Goddard in Greenbelt MD) – • Clusters of a small number of pc’s • (pre 10th century poem in Old English about a Scandinaavian warrior from the 6th century)

  5. M M M M M P P P P P D D D D C C C C More Details • Computers  SMPs World’s simplest computer Standard computer MPP

  6. More Details SMP (Symmetric Multiprocessor) P/C P/C M + Disk P/C P/C NUMA – Non uniform memory access 4. Constellation: Every nodes ia large smps

  7. More details • 5) SIMD – SIngle inmctruction multiple data • 6: Speeds: • Megaflops 106 flops • Gigaflops 109 flops workstations • Teraflops 1012 top 17 supercomputers by 2005 every supercomputer in the top 500 • Petaflops 1015 2010?

  8. Moore’s Law: Number transistors per square in in an integrated circuit doubles every 18 months • Every decade – computer performance increases 2 order of magnitude

  9. Applications of Parallel Computers • Traditionally: government labs, numerically intensive applications • Research Institutions • Recent Growth in Industrial Applications • 236 of the top 500 • Financial analysis, drug design and analysis, oil exploration, aerospace and automotive

  10. Goal of Parallel Computing • Solve bigger problems faster Challenge of Parallel Computing Coordinate, control, and monitor the computation

  11. Easiest Applictions • Embarassingly Parallel – Lots of work that can be dived out with little coordination or communication • Example: integration, Monte Carlo methods, Adding numbers

  12. Special Approaches • Distributed Computing on the internet • Seti@home signal processing 15 Teraflops • Distributed.net factor product of 2 large primes • Parabon – biomedical, protein folding, gene expression • Akamai Network – Tom Leighton, Danny Lewin • Thousands of servers spread globally that caches web pages and routes traffic away from congested areas • Embedded Computing : Mercury (inverse to the worldwide distribution)

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