Scaling up and Big Data using the Cloud


Overview

Large-scale simulations and data processing tasks that support engineering and scientific activities such as mathematical modeling, algorithm development, and testing can take an unreasonably long time to complete or require a lot of computer memory. You can speed up these tasks by taking advantage of high-performance computing resources, such as multicore computers, GPUs, computer clusters, and cloud computing services.

Using the Parallel Computing capabilities in MATLAB allows you to take advantage of additional hardware resources that may be available either locally on your desktop or on clusters and clouds. By using more hardware, you can reduce the cycle time for your workflow and solve computationally and data-intensive problems faster.

We will discuss and demonstrate how to perform parallel and distributed computing in MATLAB with minimal changes to your code. We will introduce you to parallel processing constructs such as parallel for-loops, batch processing, and distributed arrays. Discover how you can easily scale your MATLAB applications and leverage cluster and cloud resources from providers such as AWS and Azure.

Highlights

  • Built-in support for parallel computing
  • Creating parallel applications to speed up independent tasks
  • Scaling up to computer clusters, grid environments or clouds
  • Employing GPUs to speed up your computations
  • Programming with tall and distributed arrays to work with large data sets

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