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 Parallel 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 Generating CUDA^® MEX files using GPU Coder^™ Using pre-existing CUDA code

Guaranteed to Run

When you register for one of these courses, you can rely on the fact that it won't be canceled or rescheduled for any reason.

Course Schedule

Results 1 - 11 of 11

Dates	Location	Language	Price
26 Oct 2021- 27 Oct 2021	Virtual 9:00 am - 5:00 pm Central European Summer Time Register today - only 2 seat(s) remaining	English	USD 1,500
25 Nov 2021- 26 Nov 2021	Virtual 9:00 am - 5:00 pm Japan Standard Time	Japanese	USD 1,500
14 Dec 2021- 15 Dec 2021	Virtual 9:00 am - 5:00 pm Central European Time	Spanish	USD 1,500
05 Jan 2022- 06 Jan 2022	Virtual 9:00 am - 5:00 pm US Eastern Standard Time	English	USD 1,500
16 Feb 2022- 17 Feb 2022	Virtual 9:00 am - 5:00 pm Australian Eastern Daylight Time	English	USD 1,500
03 Mar 2022- 04 Mar 2022	Virtual 9:00 am - 5:00 pm Indian Standard Time	English	USD 1,500
15 Mar 2022- 16 Mar 2022	Virtual 9:00 am - 5:00 pm Central European Time	German	USD 1,500
20 Apr 2022- 21 Apr 2022	Virtual 9:00 am - 5:00 pm US Pacific Daylight Time	English	USD 1,500
04 May 2022- 05 May 2022	Virtual 9:00 am - 5:00 pm Central European Summer Time	English	USD 1,500
01 Jun 2022- 02 Jun 2022	Virtual 9:00 am - 5:00 pm Central European Summer Time	Spanish	USD 1,500
08 Jun 2022- 09 Jun 2022	Virtual 9:00 am - 5:00 pm Korea Standard Time	Korean	USD 1,500

Results 1 - 11 of 11

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