Parallel computing toolbox

GPUSupply a gpuArray argument to automatically run functions on a GPU.Train Network in the Cloud Using Automatic Parallel Support (Deep Learning Toolbox)This example shows how to train a convolutional neural network using MATLAB® automatic support for parallel training. Learn MoreWhat Is Parallel Computing?Learn about MATLAB and Parallel Computing Toolbox. Run Code on Parallel PoolsLearn about starting and stopping parallel pools, pool size, and cluster selection.Choose Between Thread-Based and Process-Based EnvironmentsWith Parallel Computing Toolbox, you can run your parallel code in different parallel environments, such as thread-based or process-based environments. Run MATLAB Functions in Thread-Based EnvironmentCheck support for MATLAB functions that you want to run in the background.Set Environment Variables on WorkersCopy system environment variables from the client to workers in a cluster.Write Portable Parallel CodeWrite parallel code that can use parallel resources if you have Parallel Computing Toolbox and that still runs if you do not have Parallel Computing Toolbox.Featured Examples

The output Q is always the same as the size of the input P. Data Types: single | doubleExtended CapabilitiesC/C++ Code Generation Generate C and C++ code using MATLAB® Coder™. Usage notes and limitations: Row vector input is only supported when the first two inputs are vectors and nonscalar.Performs arithmetic in the output class. Therefore, results might not match MATLAB® due to different rounding errors.Thread-Based Environment Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.GPU Arrays Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.The unwrap function fully supports GPU arrays. To run the function on a GPU, specify the input data as a gpuArray (Parallel Computing Toolbox). For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).Distributed Arrays Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).Version HistoryIntroduced before R2006a

ExpressionsInput arguments, specified as a comma-separated list of variables or expressions. The parallel pool worker inputs these arguments to the function fcn.pool — Pool parallel.Pool object Pool, specified as a parallel.Pool object. To create a parallel pool, use parpool.To get the background pool, use backgroundPool. Example: parpool("Processes"); Example: backgroundPool; Output Argumentscollapse allF — Future parallel.FevalFuture object Future, returned as a parallel.FevalFuture object.Use fetchOutputs or fetchNext to retrieve results from F.Use afterEach or afterAll to run a function when F completes a computation or all computations, respectively. Extended CapabilitiesAutomatic Parallel Support Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.Usage notes and limitations:The syntax parfeval(fcn,numFcnOut,X1,...,Xm) has automatic parallel support if you have Parallel Computing Toolbox.For more information, see Run MATLAB Functions with Automatic Parallel Support.Thread-Based Environment Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.Version HistoryIntroduced in R2013bexpand allR2021b: parfeval can now run in serial with no poolStarting in R2021b, you can now run parfeval in serial with no pool. This behavior allows you to share parallel code that you write with users who do not have Parallel Computing Toolbox.When you use the syntax parfeval(fcn,numFcnOut,X1,...,Xm), MATLAB tries to use an open parallel pool if you have Parallel Computing Toolbox. If a parallel pool is not open, MATLAB will create one if automatic pool creation is enabled.If parallel pool creation is disabled or if you do not have Parallel Computing Toolbox, the function is evaluated in serial. In previous releases, MATLAB threw an error instead.

Perform parallel computations on multicore computers, GPUs, and computer clustersRelease NotesPDF DocumentationParallel Computing Toolbox™ lets you solve compute- and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—enable you to scale MATLAB® applications without CUDA® or MPI programming. Parallel Computing Toolbox also lets you use parallel-enabled functions in MATLAB and other toolboxes and run multiple Simulink® simulations in parallel. Programs and models can run in both interactive and batch modes.The toolbox lets you use the full processing power of multicore and GPU-enabled desktops by executing applications on thread and process workers (MATLAB computational engines) that run locally. Without changing the code, you can run the same applications on clusters or clouds (using MATLAB Parallel Server™). You can also use the toolbox with MATLAB Parallel Server to execute matrix calculations that are too large to fit into the memory of a single machine.Get Started Learn the basics of Parallel Computing ToolboxParallel Computing FundamentalsChoose a parallel computing solutionParallel for-Loops (parfor)Use parallel processing by running parfor on workers in a parallel poolAsynchronous Parallel ProgrammingEvaluate functions in the background using parfevalBig Data ProcessingAnalyze big data sets in parallel using distributed arrays, tall arrays, datastores, or mapreduce, on Spark® and Hadoop® clustersBatch ProcessingOffload execution of functions to run in the background GPU ComputingAccelerate your code by running it on a GPUClusters and CloudsDiscover cluster resources, and work with cluster profilesPerformance ProfilingImprove performance of parallel code

Documentation Examples Functions Videos Answers Main Content Choose a parallel computing solutionParallel computing can help you to solve big computing problems in different ways. MATLAB® and Parallel Computing Toolbox™ provide an interactive programming environment to help tackle your computing tasks. If your code runs too slowly, you can profile it, vectorize it, and use built-in MATLAB parallel computing support. Then you can try to accelerate your code by using parfor on multiple MATLAB workers in a parallel pool. If you have big data, you can scale up using distributed arrays or datastore. You can also execute a task without waiting for it to complete, using parfeval, so that you can carry on with other tasks. You can use different types of hardware to solve your parallel computing problems, including desktop computers, GPUs, clusters, and clouds. To get started, see Quick Start Parallel Computing in MATLAB.Functionsexpand allKey functions in Parallel Computing ToolboxparforExecute for-loop iterations in parallel on workersparfevalRun function on parallel pool workergpuArrayArray stored on GPUdistributedCreate and access elements of distributed arrays from clientbatchRun MATLAB script or function on workerparpoolCreate parallel pool on clusterticBytesStart counting bytes transferred within parallel pooltocBytesRead how many bytes have been transferred since calling ticBytesdelete (Pool)Shut down parallel poolTopicsBasicsChoose a Parallel Computing SolutionDiscover the most important functionalities offered by MATLAB and Parallel Computing Toolbox to solve your parallel computing problem.Parallel Language Decision TablesDiscover example use cases for common parallel computing language features.Quick Start Parallel Computing in MATLABLearn about parallel computing in MATLAB and Parallel Computing Toolbox.Run MATLAB Functions with Automatic Parallel SupportTake advantage of parallel computing resources without requiring any extra coding.Interactively Run Loops in Parallel Using parforConvert a for-loop into a scalable parfor-loop.Choose How to Manage Data in Parallel ComputingDetermine the data management approach that meets your parallel computing requirements.Plot During Parameter Sweep with parforPerform a parameter sweep in parallel and plot progress during parallel computations. Scale Up from Desktop to ClusterDevelop your parallel MATLAB® code on your local machine and scale up to a cluster. Run Batch Parallel JobsUse batch to offload work from your MATLAB session to run in the background.Process Big Data in the CloudThis example shows how to access a large data set in the cloud and process it in a cloud cluster using MATLAB® capabilities for big data.Evaluate Functions in the Background Using parfevalBreak out of an optimizing loop early and collect results as they become available.Run MATLAB Functions on a

Lines.'off' — Do not label the contour lines. Contour line width, specified as a positive value in points. One point equals 1/72 inch. Label spacing along the contour lines, specified as a scalar value in points, where one point is 1/72 inch. Use this property to control the number of contour labels along the contour lines. Smaller values produce more labels. You must set the ShowText property to 'on' for the LabelSpacing property to have an effect. If you use the clabel function to display the labels, then the LabelSpacing property has no effect and the plot displays one label per line. Output Argumentscollapse allM — Contour matrix matrixContour matrix, returned as a two-row matrix of following form. Z1, x1,1, x1,2, ..., x1,N1, Z2, x2,1, x2,2, ..., x2,N2, Z3, ...N1, y1,1, y1,2, ..., y1,N1, N2, y2,1, y2,2, ..., y2,N2, N3, ...The columns of the matrix define the contour lines. Each contour line starts with a column containing Z and N values:Zi — The height of the ith contour lineNi — The number of vertices in the ith contour line(xij, yij) — The coordinates of the vertices for the ith contour line, where j ranges from 1 to Nic — Contour object Contour objectContour object. Use this object to set properties after displaying the contour plot.Extended CapabilitiesGPU Arrays Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.The contourf function supports GPU array input with these usage notes and limitations:This function accepts GPU arrays, but does not run on a GPU.For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).Distributed Arrays Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.Usage notes and limitations:This function operates on distributed arrays, but executes in the client MATLAB.For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).Version HistoryIntroduced before R2006a