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Accelerating & Parallelizing MATLAB Code
Next level after the fundamental
Accelerating and Parallelizing
MATLAB Code
Learn how to accelerate and parallelize MATLAB code
Complimentary Services: Post training email support & 1-hr consultation session within 1 month after the course completion!
Course
Highlights
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
Who Should
Attend
Engineers who wish to use multiple systems to shorten up the simulation time and to be able to process large data sets.
Course
Prerequisites
Fundamentals or equivalent experience using MATLAB
Course
Benefits
Upon the completion of the course, the participants will be able to:
- Profile code to identify bottlenecks
- Distribute independent loop iterations across multiple MATLAB processes
- Submit parallel code to a cluster environment
- Utilize GPU for computations
Partners
Upcoming Program
- Please keep me posted on the next schedule
- Please contact me to arrange customized/ in-house training
Course Outline
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
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