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Parallel Computing Toolbox?

Parallel Computing Toolbox?
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Parallel Computing Toolbox Perform parallel computations on multicore computers, GPUs, and computer clusters Parallel Computing Toolbox™ lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—let you parallelize MATLAB® applications without CUDA or MPI programming. You can use the toolbox with Simulink® to run multiple simulations of a model in parallel. The toolbox provides twelve workers (MATLAB computational engines) to execute applications locally on a multicore desktop. Without changing the code, you can run the same application on a computer cluster or a grid computing service (using MATLAB Distributed Computing Server™). You can run parallel applications interactively or in batch. Built-in Parallel Computing Support in MathWorks Products Key Features ▪ Parallel for-loops (parfor) for running task-parallel algorithms on multiple processors ▪ Support for CUDA-enabled NVIDIA GPUs ▪ Ability to run twelve workers locally on a multicore desktop ▪ Computer cluster and grid support (with MATLAB Distributed Computing Server) ▪ Interactive and batch execution of parallel applications ▪ Distributed arrays and single program multiple data (spmd) construct for large dataset handling and data-parallel algorithms

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Multicore Desktop with GPUs Parallel computing with MATLAB. You can use Parallel Computing Toolbox to run applications on a multicore desktop with twelve workers available in the toolbox, take advantage of GPUs, and scale up to a cluster (with MATLAB Distributed Computing Server). Programming Parallel Applications Parallel Computing Toolbox provides several high-level programming constructs that let you convert your applications to take advantage of computers equipped with multicore processors and GPUs. Constructs such as parallel f or-loops (parf or) and special array types for distributed...

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Farald DtdCip Wndow hteb » % Start a pool of MftTLflB *orkeii — Gorm«ct*<t Co rattlabpool with 6 *<ork*ri - - » 4 yet option tr> enable parallel op t ir-.i E-TL 11 a n B3 Optim Lurtiori Tool Fit E*i VM hJirt T«* Dtdiop Win*™ The niji, flUffl '. Iijr.ji' differences forward parallel [□ uvaluatu firrtu mini firel ifi up ytur parAi • When Ihe Sut^ar FBIIB » When Ihe 5nM:r Mqhl Have Using built-in parallel algorithms inMathWorks products. Built-in parallel algorithms can speed upMATLAB and Simulink computations as well as code generation from Simulink models. Speeding Up Task-Parallel...

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dataParfor = zeros {numberOfRuns, numel {ml. Species) ) ; □ %% Run an ensemble of stochastic simulations Using parallel for-loops for a task-parallel application. You can use parallel for-loops in MATLAB scripts and functions and execute them both interactively and offline. Speeding Up MATLAB Computations with GPUs Parallel Computing Toolbox provides GPUArray, a special array type with several associated functions that lets you perform computations on CUDA-enabled NVIDIA GPUs directly from MATLAB. Functions include f f t, element-wise operations, and several linear algebra operations such...

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File Edit Debug Parallel Desktop double (complex] Window Help Command Window % Create arrays that reside on the GPU % Use GPU-enabled MATLAB functions % Bring data back from GPU memory into MATLAB workspace GPU computing with MATLAB. Using GPUArrays and GPU-enabled MATLAB functions help speed upMATLAB operations without low-level CUDA programming. Scaling Up to Clusters, Grids, and Clouds Using MATLAB Distributed Computing Server Parallel Computing Toolbox provides the ability to use up to twelve workers to execute parallel applications locally on a multicore computer. Using the toolbox in...

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Implementing Data-Parallel Applications using the Toolbox and MATLAB Distributed Computing Server Distributed arrays in Parallel Computing Toolbox are special arrays that hold several times the amount of data that your desktop computer's memory (RAM) can hold. Distributed arrays apportion the data across several MATLAB worker processes running on a computer cluster (using MATLAB Distributed Computing Server). As a result, with distributed arrays you can overcome the memory limits of your desktop computer and solve problems that require manipulating very large matrices. With over 150...

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Running applications interactively is suitable when execution time is relatively short. When your applications need to run for a long time, you can use the toolbox to set them up to run as batch jobs. This enables you to free your MATLAB session for other activities while you execute large MATLAB and Simulink applications. While your application executes in batch, you can shut down your MATLAB session and retrieve results later. The toolbox provides several mechanisms to manage offline execution of parallel programs, such as the batch function and j ob and task objects. Both the batch...

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