Accelerating and Parallelizing MATLAB Code

This two-day course covers a variety of techniques for making your MATLAB^® code run faster. You will identify and remove computational bottle-necks using techniques like preallocation and vectorization. In addition, you will compile MATLAB code into MEX-files using MATLAB Coder^™. On top of that, you will take advantage of multiple cores on your computer by parallelizing for-loops with Parallel Computing Toolbox^™, and scale up across multiple computers using MATLAB Distributed Computing Server^™. Interplay between those concepts will be explored throughout the course. If you are working with long-running simulations, you will benefit from the hands-on demonstrations and exercises in the course. Topics include:

Improving performance within core MATLAB
Generating MEX-files
Parallelizing computations
Offloading execution
Working with clusters
GPU computing

See detailed course outline.

Prerequisites

MATLAB Fundamentals, or equivalent experience using MATLAB

Day 1 of 2
Improving Performance	Objective: Analyze code performance and utilize techniques for acceleration within MATLAB. Identifying bottle necks Preallocating arrays Vectorizing operations in various ways Rewriting algorithms
Generating MEX-Files	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
Parallelizing Computations	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

Day 2 of 2
Parallel for-Loops	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
Offloading Execution	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
Working with Clusters	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
GPU Computing	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 Using pre-existing CUDA code

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Guaranteed to Run

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Course Schedule

Results 1 - 10 of 10

Dates	Location	Language	Price
19 Jul 2017- 20 Jul 2017	Online 9:00 a.m. - 5:00 p.m. US Eastern Daylight Time	English	USD 1,500
27 Jul 2017- 28 Jul 2017	Japan, 名古屋市	Japanese	JPY 120,000
22 Aug 2017- 23 Aug 2017	Australia, Sydney (Chatswood)	English	AUD 1,800
28 Sep 2017- 29 Sep 2017	Germany, München (Ismaning)	German	EUR 1,400
26 Oct 2017- 27 Oct 2017	US, Massachusetts, Natick	English	USD 1,500
14 Nov 2017- 15 Nov 2017	Japan, 東京都	Japanese	JPY 120,000
23 Nov 2017- 24 Nov 2017	Netherlands, Eindhoven	English	EUR 1,400
30 Nov 2017- 01 Dec 2017	US, California, Los Angeles (Torrance)	English	USD 1,500
07 Dec 2017- 08 Dec 2017	US, North Carolina, Durham	English	USD 1,500
20 Dec 2017- 21 Dec 2017	Online 9:00 a.m. - 5:00 p.m. US Eastern Daylight Time	English	USD 1,500

Results 1 - 10 of 10

The pricing applies for purchase and use in United States, For pricing in other regions Contact Sales. The product price does not include sales, use, excise, value-added, or other taxes. Any applicable taxes, duties, levies, assessments and governmental charges payable in connection with this purchase will be assessed on the order. Refer to Training Policies for more information

Prerequisites

Accelerating and Parallelizing MATLAB Code

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