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Summary. Background Why do we need parallel processing? Applications Introduction in algorithms and applications Methodology to develop efficient parallel (distributed-memory) algorithms 

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Summary

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  1. Summary • Background • Why do we need parallel processing? Applications • Introduction in algorithms and applications • Methodology to develop efficient parallel (distributed-memory) algorithms  • Understand various forms of overhead (communication, load imbalance, search overhead, synchronization) and various distributions (blockwise, cyclic) • Understand correctness problems (e.g. message ordering)

  2. Summary • Parallel machines and architectures • Processor organizations, topologies, criteria • Types of parallel machines • arrays/vectors, shared-memory, distributed memory • Routing • Flynn’s taxonomy • What are cluster computers? • What networks do real machines (like the Blue Gene) use? • Speedup, efficiency (+ their implications), Amdahl’s law

  3. Summary • Programming methods, languages, and environments • Different forms of message passing • naming, explicit/implicit receive, synchronous/asynchronous sending • Select statement • SR primitives (not syntax) • Master/worker model, search overhead (TSP) • MPI: message passing primitives, collective communication • Java parallel programming model and primitives • HPF: problems with automatic parallelization; division of work between programmer and HPF compiler; alignment/distribution primitives; performance implications

  4. Summary • Applications • N-body problems: load balancing and communication (locality) optimizations, costzones, performance comparison • Search algorithm (TDS): use asynchronous communication + clever (transposition-driven) scheduling • Bioinformatics (repeats detection): combine many forms of parallelism • Grid computing • What are grids? Why do we need them? • Application-level tools for grid programming and execution • Why is parallel computing on grids feasible? • Ibis: goals, applications, design, programming vs. deployment, communication primitives, divide&conquer parallelism, performance behavior on a grid

  5. Summary • Multimedia content analysis on Grids • What is Multimedia Content Analysis (MMCA) ? Imaging applications, feature vectors, low-level patterns • Why parallel computing in MMCA - and how? Need for speed and transparency • Software Platform: Parallel-Horus. Task/data parallelism, Separable Recursive Filtering, Lazy Parallelization • Example – Parallel Image Processing on Clusters • Grids and their specific problems. Promise and problems of the grid • Towards a Software Platform for MMCA on Grids. Problems with wide-area servers • Large-scale MMCA applications on Grids (TRECVID, object recognition)

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