From the series: Parallel and GPU Computing Tutorials
Harald Brunnhofer, MathWorks
Learn about using GPU-enabled MATLAB functions, executing NVIDIA® CUDA™ code from MATLAB®, and performance considerations.
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
example using Parallel Computing Toolbox.
Part 4: Deeper Insights into Using parfor
-loops, and learn about factors governing the speedup of
-loops using Parallel Computing Toolbox.
Part 5: Batch Processing
Offload serial and parallel programs using
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.