Parallel MATLAB Techniques

TL;DR

This chapter discusses the benefits and methods of using parallel MATLAB for large-scale signal and image processing applications, providing practical examples and strategies for developers.

Contribution

It offers a comparative overview of parallel MATLAB options and demonstrates their application across various signal processing problems.

Findings

Parallel MATLAB enables handling larger signal processing problems.

Different parallelization techniques suit different applications.

Guidelines for developing effective parallel algorithms in MATLAB.

Abstract

In this chapter, we show why parallel MATLAB is useful, provide a comparison of the different parallel MATLAB choices, and describe a number of applications in Signal and Image Processing: Audio Signal Processing, Synthetic Aperture Radar (SAR) Processing and Superconducting Quantum Interference Filters (SQIFs). Each of these applications have been parallelized using different methods (Task parallel and Data parallel techniques). The applications presented may be considered representative of type of problems faced by signal and image processing researchers. This chapter will also strive to serve as a guide to new signal and image processing parallel programmers, by suggesting a parallelization strategy that can be employed when developing a general parallel algorithm. The objective of this chapter is to help signal and image processing algorithm developers understand the advantages of using parallel MATLAB to tackle larger problems while staying within the powerful environment of MATLAB.