From the series: Parallel and GPU Computing Tutorials

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Harald Brunnhofer, MathWorks
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Convert `for`

-loops to `parfor`

-loops, and learn about factors governing the speedup of `parfor`

-loops using Parallel Computing Toolbox™.

- Parallel Computing Toolbox

Part 1: Product Landscape

Get an overview of parallel computing products used in this tutorial series.

Part 2: Prerequisites and Setting Up

Review hardware and product requirements for running the parallel programs demonstrated in Parallel Computing Toolbox™ tutorials.

Part 3: Quick Success with parfor

Review an introductory `parfor`

example using Parallel Computing Toolbox™.

Part 4: Deeper Insights into Using parfor

Convert `for`

-loops to `parfor`

-loops, and learn about factors governing the speedup of `parfor`

-loops using Parallel Computing Toolbox™.

Part 5: Batch Processing

Offload serial and parallel programs using `batch`

command, and use the Job Monitor.

Part 6: Scaling to Clusters and Cloud

Learn about considerations for using a cluster, creating cluster profiles, and running code on a cluster with MATLAB Distributed Computing Server™.

Part 7: spmd - Parallel Code Beyond parfor

Execute code simultaneously on workers, access data on worker workspaces, and exchange data between workers using Parallel Computing Toolbox™ and MATLAB Distributed Computing Server™.

Part 8: Distributed Arrays

Perform matrix math on very large matrices using distributed arrays in Parallel Computing Toolbox™.

Part 9: GPU Computing with MATLAB

Learn about using GPU-enabled MATLAB functions, executing NVIDIA^{®} CUDA™ code from MATLAB^{®}, and performance considerations.