Parallel And Distributed Computing With MATLAB
Parallel and Distributed Computing with MATLAB 2018 The MathWorks, Inc.1
Practical Application of Parallel Computing Why parallel computing? Need faster insight on more complex problems with larger datasets Computing infrastructure is broadly available (multicore desktops, GPUs, clusters) Why parallel computing with MATLAB Leverage computational power of more hardware Accelerate workflows with minimal to no code changes to your original code Focus on your engineering and research, not the computation2
Steps for Improving Performance First get code working Speed up code with core MATLAB Include compiled languages and additional hardwareWebinar: Optimizing and Accelerating Your MATLAB Code3
Programming Parallel Applications Built-in multithreading– Automatically enabled in MATLAB since R2008a– Multiple threads in a single MATLAB computation engine Parallel computing using explicit techniques– Multiple computation engines controlled by a single session– High-level constructs to let you parallelize MATLAB applications– Perform MATLAB computations on GPUs4
Parallel rWorkerWorker5
Agenda Utilizing multiple cores on a desktop computer Scaling up to cluster and cloud resources Tackling data-intensive problems on desktops and clusters Accelerating applications with NVIDIA GPUs Summary and resources6
Programming Parallel ApplicationsBuilt in supportGreater Control– ., ‘UseParallel’, true)Ease of Use 7
Parallel-enabled ToolboxesImage ProcessingBatch Image Processor, BlockProcessing, GPU-enabled functionsSignal Processing andCommunicationsGPU-enabled FFT filtering,cross correlation, BERsimulationsStatistics and Machine LearningResampling Methods, k-Meansclustering, GPU-enabled functionsComputer VisionParallel-enabled functionsin bag-of-words workflowNeural NetworksDeep Learning, Neural Networktraining and simulationOptimizationParallel estimation ofgradientsOther Parallel-enabled Toolboxes8
Programming Parallel Applications Built in supportSimple programming constructs– parfor, batchEase of Use Greater Control– ., ‘UseParallel’, true)9
Embarrassingly Parallel: Independent Tasks or Iterations No dependencies or communication between tasks Examples:– Monte Carlo simulations– Parameter sweeps– Same operation on many filesTimeTime10
Mechanics of parfor Loops11223 34 4 55 6678 8 9 910 10Workera(i) i;a zeros(10, 1)parfor i 1:10a(i) i;endaWorkera(i) i;Workera(i) i;Workera(i) i;11
Tips for Leveraging PARFOR Consider creating smaller arrays on each worker versus one large arrayprior to the parfor loop Take advantage of parallel.pool.Constant to establish variableson pool workers prior to the loop Encapsulate blocks as functions when neededUnderstanding parfor12
Programming Parallel Applications Built in supportSimple programming constructs– parfor, batch Full control of parallelizationEase of Use Greater Control– ., ‘UseParallel’, true)– spmd, parfeval13
Agenda Utilizing multiple cores on a desktop computer Scaling up to cluster and cloud resources Tackling data-intensive problems on desktops and clusters Accelerating applications with NVIDIA GPUs Summary and resources14
Offloading dulerWorkerWorkerWorker15
Offloading Serial Computations job batch(.);WorkMATLABDesktop (Client)WorkerWorkerWorkerResultWorker16
Offloading and Scaling Computations job batch(. , 'Pool', n);WorkMATLABDesktop (Client)WorkerWorkerWorkerResultWorker17
Migrate to Cluster / Cloud Use MATLAB Distributed Computing Server Change hardware without changing algorithm18
Use MATLAB
Programming Parallel Applications Built-in multithreading –Automatically enabled in MATLAB since R2008a –Multiple threads in a single MATLAB computation engine Parallel computing using explicit techniques –Multiple computation engines controlled by a single session –High-level constructs to let you parallelize MATLAB applications
Cloud Computing J.B.I.E.T Page 5 Computing Paradigm Distinctions . The high-technology community has argued for many years about the precise definitions of centralized computing, parallel computing, distributed computing, and cloud computing. In general, distributed computing is the opposite of centralized computing.
Parallel computing, distributed computing, java, ITU-PRP . 1 Introduction . ITU-PRP provides an all-in-one solution for Parallel Programmers, with a Parallel Programming Framework and a . JADE (Java Agent Development Framework) [6] as another specific Framework implemented on Java, provides a framework for Parallel Processing.
Practical Application of Parallel Computing Why parallel computing? Need faster insight on more complex problems with larger datasets Computing infrastructure is broadly available (multicore desktops, GPUs, clusters) Why parallel computing with MATLAB Leverage computational power of more hardware
In distributed computing we have multiple autonomous computers which seems to the user as single system. In distributed systems there is no shared memory and computers communicate with each other through message passing. In distributed computing a single task is divided among different computers. Difference between Parallel Computing and .
Progress! "DryadLINQ: A System for General-Purpose Distributed Data-Parallel Computing Using a High-Level Language", OSDI 2008 "SCOPE: Easy and efficient parallel processing of massive data sets", VLDB 2008 "Distributed Data-Parallel Computing Using a High- Level Programming Language", SIGMOD 2009
Parallel computing is a form of High Performance computing. By using the strength of many smaller computational units, parallel computing can pro-vide a massive speed boost for traditional algorithms.[3] There are multiple programming solutions that o er parallel computing. Traditionally, programs are written to be executed linearly. Languages
Parallel and Distributed Computing Chapter 1: Introduction to Parallel Computing Jun Zhang Laboratory for High Performance Computing & Computer Simulation Department of Computer Science University of Kentucky Lexington, KY 40506. Chapter 1: CS621 2 1.1a: von Neumann Architecture
Parallel Computing Toolbox Ordinary Di erential Equations Partial Di erential Equations Conclusion Lecture 8 Scienti c Computing: Symbolic Math, Parallel Computing, ODEs/PDEs Matthew J. Zahr CME 292 Advanced MATLAB for Scienti c Computing Stanford University 30th April 2015 CME 292: Advanced MATLAB for SC Lecture 8. Symbolic Math Toolbox .
