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Introduction to Parallel Computing Introduction to Parallel Computing Elwin Chan © 2015 The MathWorks, Inc.1 Who Uses Parallel Computing? Optimizing JIT Steel Manufacturing Schedule Cut simulation time from 1 hour to 5 minutes Heart Transplant Studies 3-4 weeks reduced to 5 days Flight Test Data Analysis 16x Faster Mobile Communications Technology Simulation time reduced from weeks to hours, 5x more scenarios Hedge Fund Portfolio Management Simulation time reduced from 6 hours to 1.2 hours 2 Demo: Getting Data from a Web API 3 Demo: Getting Data from a Web API using parfor 4 Demo: Getting Data from a Web API using parfeval 5 Going Parallel: Multicore Multicore Desktop MATLAB workers MATLAB client 6 Going Parallel: Clusters Computer Cluster MATLAB client 7 Going Parallel: Cloud Cloud Cluster MATLAB client 8 Going Parallel: GPUs NVIDIA GPUs GPU Cores MATLAB client Device Memory 9 Explicit Parallelism: Independent Tasks or Iterations parfor, parfeval, jobs and tasks . Examples: parameter sweeps, Monte Carlo simulations . No dependencies or communications between tasks MATLAB client MATLAB workers Time Time 10 Speed-up using NVIDIA GPUs Ideal Problems • Massively Parallel and/or Vectorized operations • Computationally Intensive • Algorithm consists of supported functions GPU Cores 300+ GPU-enabled MATLAB functions Additional GPU-enabled Toolboxes Device Memory • Neural Networks • Image Processing • Communications • Signal Processing ..... Learn More 11 Too Much Data for One Machine? Use Distributed Memory Using More Computers (RAM) MATLAB client RAM RAM 12 Distributed Arrays . Distributed Arrays hold data remotely on workers running on a cluster . Manipulate directly from client MATLAB (desktop) . 200+ MATLAB functions overloaded for distributed arrays Supported functions for Distributed Arrays 13 Too Much Data for Your Cluster? Use Datastore Datastore HDFS Node Data Node Data Node Data Datastore access data stored in HDFS from MATLAB Hadoop 14 Too Much Data for Your Cluster? Use Datastore, MapReduce and tall MATLAB Distributed Computing Datastore Server HDFS Node Data Map Reduce Node Data Map Reduce Node Data Map Reduce map.m reduce.m Hadoop 15 Offload and Scale Computations with batch Work Worker MATLAB Desktop (Client) Worker Worker Result Worker batch(…) 16 Going Parallel: Other MATLAB® Toolboxes Enable parallel computing support by setting a flag or preference Image Processing Statistics and Machine Learning Neural Networks Batch Image Processor, Block Resampling Methods, k-Means Deep Learning, Neural Network Processing, GPU-enabled functions clustering, GPU-enabled functions training and simulation Signal Processing and Computer Vision Optimization Communications Parallel-enabled functions Parallel estimation of GPU-enabled FFT filtering, in bag-of-words workflow gradients cross correlation, BER simulations Other Parallel-enabled Toolboxes 17 Going Parallel: Simulink® Enable parallel computing support by setting a flag or preference Simulink Design Optimization Simulink Control Design Response optimization, sensitivity Frequency response estimation analysis, parameter estimation Communication Systems Toolbox Simulink/Embedded Coder GPU-based System objects for Generating and building code Simulation Acceleration Other Parallel-enabled Toolboxes 18 Why use Parallel Computing Toolbox? . Reduce computation time by GPU Cores – Using more cores Device Memory – Accessing Graphical Processing Units . Overcome memory limitations by – Distributing data to available hardware – Using datastore and mapreduce RAM RAM . Offload computations to a cluster and – Free up your desktop – Access better computer hardware 19.
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  • 2.5 Classification of Parallel Computers
    52 // Architectures 2.5 Classification of Parallel Computers 2.5 Classification of Parallel Computers 2.5.1 Granularity In parallel computing, granularity means the amount of computation in relation to communication or synchronisation Periods of computation are typically separated from periods of communication by synchronization events. • fine level (same operations with different data) ◦ vector processors ◦ instruction level parallelism ◦ fine-grain parallelism: – Relatively small amounts of computational work are done between communication events – Low computation to communication ratio – Facilitates load balancing 53 // Architectures 2.5 Classification of Parallel Computers – Implies high communication overhead and less opportunity for per- formance enhancement – If granularity is too fine it is possible that the overhead required for communications and synchronization between tasks takes longer than the computation. • operation level (different operations simultaneously) • problem level (independent subtasks) ◦ coarse-grain parallelism: – Relatively large amounts of computational work are done between communication/synchronization events – High computation to communication ratio – Implies more opportunity for performance increase – Harder to load balance efficiently 54 // Architectures 2.5 Classification of Parallel Computers 2.5.2 Hardware: Pipelining (was used in supercomputers, e.g. Cray-1) In N elements in pipeline and for 8 element L clock cycles =) for calculation it would take L + N cycles; without pipeline L ∗ N cycles Example of good code for pipelineing: §doi =1 ,k ¤ z ( i ) =x ( i ) +y ( i ) end do ¦ 55 // Architectures 2.5 Classification of Parallel Computers Vector processors, fast vector operations (operations on arrays). Previous example good also for vector processor (vector addition) , but, e.g. recursion – hard to optimise for vector processors Example: IntelMMX – simple vector processor.
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  • Analysis of Multithreaded Architectures for Parallel Computing
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  • Vector Processors
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  • Introduction to Parallel Computing
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  • Parallel Processing: Past, Present and Future
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