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parallel computing fundamentals -凯发k8网页登录

choose a parallel computing solution

parallel 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.

functions

parforexecute for-loop iterations in parallel on workers
run function on parallel pool worker
gpuarrayarray stored on gpu
access elements of distributed arrays from client
run matlab script or function on worker
parpoolcreate parallel pool on cluster
start counting bytes transferred within parallel pool
read how many bytes have been transferred since calling ticbytes

topics

basics

  • choose a parallel computing solution
    discover the most important functionalities offered by matlab and parallel computing toolbox to solve your parallel computing problem.

  • discover example use cases for common parallel computing language features.

  • take advantage of parallel computing resources without requiring any extra coding.
  • interactively run loops in parallel using parfor
    convert a for-loop into a scalable parfor-loop.

  • perform a parameter sweep in parallel and plot progress during parallel computations.
  • scale up from desktop to cluster
    develop your parallel matlab® code on your local machine and scale up to a cluster.
  • run batch parallel jobs
    use batch to offload work from your matlab session to run in the background.
  • process big data in the cloud
    this 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.

  • break out of a loop early and collect results as they become available.
  • run matlab functions on a gpu
    supply a gpuarray argument to automatically run functions on a gpu.
  • (deep learning toolbox)
    this example shows how to train a convolutional neural network using matlab automatic support for parallel training.

learn more


  • learn about matlab and parallel computing toolbox.

  • learn about starting and stopping parallel pools, pool size, and cluster selection.

  • with 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 environment
    check support for matlab functions that you want to run in the background.

  • copy system environment variables from the client to workers in a cluster.

  • write 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.

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