Objective: Analyze code performance and utilize techniques for acceleration within MATLAB.

• Identifying bottle necks
• Preallocating arrays
• Vectorizing operations in various ways
• Rewriting algorithms

Objective: Generate compiled code files from MATLAB code for better performance.

• MATLAB Coder overview and workflow
• Generating and verifying MEX-files
• Calling unsupported functions
• Adjusting settings for MEX-file generation

Objective: Parallelize code execution to take advantage of multiple cores.

• Opening additional MATLAB processes
• Running parallel for-loops
• Measuring speedup
• Processing multiple files in parallel

Objective: Explore parallel for-loops in more detail and apply techniques for converting for-loops to parfor-loops.

• Requirements of parallel for-loops
• Parallelizing for-loops
• Retrieving intermediate results

Objective: Offload computations to another MATLAB process in order to be able to use MATLAB for other tasks in the meantime. This is also a preparation step for working with clusters.

• Processing in batch
• Creating batch jobs
• Retrieving results
• Using the Job Monitor

Objective: Accelerate computations and realize more extensive simulations by utilizing multiple computers.

• Local and remote clusters
• Dynamic licensing
• Cluster discovery and connection
• File access considerations

Objective: Execute MATLAB code on your computer’s graphics card (GPU) as another option for speeding up calculations.

• Overview of GPU architecture and processing
• Applications suitable for GPU processing
• Invoking MATLAB functions on the GPU
• Generating CUDA^® MEX files using GPU Coder^™
• Using pre-existing CUDA code