Video and Webinar Series

Parallel and GPU Computing Tutorials

Parallel Computing Toolbox™ helps you take advantage of multicore computers and GPUs. The videos and code examples included below are intended to familiarize you with the basics of the toolbox. They can help show how to scale up to large computing resources such as clusters and the cloud. (Scaling up requires access to MATLAB Distributed Computing Server™.)

Parallel Computing Tutorial, Part 1: Product Landscape  3:42
Get an overview of parallel computing products used in this tutorial series.

Parallel Computing Tutorial, Part 2: Prerequisites and Setting Up  3:19
Review hardware and product requirements for running the parallel programs demonstrated in Parallel Computing Toolbox™ tutorials.

Parallel Computing Tutorial, Part 3: Quick Success with parfor  3:41
Review an introductory parfor example using Parallel Computing Toolbox™.

Parallel Computing Tutorial, Part 4: Deeper Insights into Using parfor  3:49
Convert for-loops to parfor-loops, and learn about factors governing the speedup of parfor-loops using Parallel Computing Toolbox™.

Parallel Computing Tutorial, Part 5: Batch Processing  6:38
Offload serial and parallel programs using batch command, and use the Job Monitor.

Parallel Computing Tutorial, Part 6: Scaling to Clusters and Cloud  6:21
Learn about considerations for using a cluster, creating cluster profiles, and running code on a cluster with MATLAB Distributed Computing Server™.

Parallel Computing Tutorial, Part 7: spmd - Parallel Code Beyond parfor  4:01
Execute code simultaneously on workers, access data on worker workspaces, and exchange data between workers using Parallel Computing Toolbox™ and MATLAB Distributed Computing Server™.

Parallel Computing Tutorial, Part 8: Distributed Arrays  5:43
Perform matrix math on very large matrices using distributed arrays in Parallel Computing Toolbox™.

Parallel Computing Tutorial, Part 9: GPU Computing with MATLAB  6:14
Learn about using GPU-enabled MATLAB functions, executing NVIDIA® CUDA™ code from MATLAB®, and performance considerations.

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