MATLAB Acceleration

Techniques for accelerating MATLAB algorithms and applications

There are several ways to accelerate MATLAB algorithms and applications. The optimal approach depends on your programming expertise, the type of algorithms you wish to accelerate, and the hardware available to you.

Top 5 MATLAB Acceleration Techniques

1. Adopt Efficient (Serial) Programming Practices

   It’s a good practice to optimize your serial code for performance before considering other approaches. Two effective programming techniques for MATLAB acceleration are preallocation and vectorization.

2. Leverage Existing Optimized Algorithms

   MATLAB and related toolboxes have already been optimized for performance. For example, System objects are object-oriented implementations of MATLAB algorithms that can accelerate MATLAB code, particularly for signal processing and communications applications.

3. Use Parallel Computing

   Multicore CPUs, GPUs, and clusters can accelerate MATLAB code. High-level parallel constructs in Parallel Computing Toolbox let you take advantage of high-performance hardware with minimal programming effort. The toolbox also enables the parallel computing support found in many functions and algorithms in MATLAB products.

4. Generate C Code from MATLAB Code

   To accelerate some MATLAB algorithms, you can generate readable and portable C code and compile it into a MATLAB executable. Much of the MATLAB language and several toolboxes support code generation through MATLAB Coder.

5. All of the Above

   For some applications, you can combine these methods for additional MATLAB acceleration.

Examples and How To

• Speeding up MATLAB Applications (Webinar)
• Parallel Computing with MATLAB on Multicore Desktops and GPUs (Webinar)
• MATLAB to C Made Easy (Webinar)
• Simple Benchmarking of parfor Using Blackjack (Example)
• Using GPUs to Accelerate Turbo Coding Bit Error Rate Simulations for Communications Systems (Example)
• MEX Generation for Faster Simulation: Accelerating Simulation of Bouncing Balls (Example)
• Simulation Acceleration Using System Objects, MATLAB Coder, and Parallel Computing Toolbox (Example)

Software Reference

• Profiling for Improving Performance (Documentation)
• Techniques for Improving Performance (Documentation)
• Communications System Toolbox System Objects (Documentation)
• DSP System Toolbox System Objects (Documentation)
• Getting Started with parfor (Documentation)
• Best Practices for Generating C-code and MEX-files Using MATLAB Coder (Quick Start Guide)

See also: Parallel Computing Toolbox, MATLAB Distributed Computing Server, MATLAB Coder, DSP System Toolbox, Communications System Toolbox, multicore MATLAB, MATLAB GPU computing, parallel computing, System objects