MATLAB Distributed, and Other Toolboxes Rahman Tashakkori, ASU "A Consortium to Promote Computational Science and High Performance Computing" Video Conference April 18, 2005

Distributed Toolbox The MathWorks web site was the main source of the MATLAB related information on this presentation The Distributed Computing Toolbox works with the MATLAB Distributed Computing Engine to execute coarse-grained MATLAB algorithms and Simulink models in a cluster of computers You can prototype and develop applications in the MATLAB environment and then use the Distributed Computing Toolbox to divide them into independent tasks. The MATLAB Distributed Computing Engine evaluates these tasks on remote MATLAB sessions.

Distributed Toolbox: Key Features Distributed execution of coarse-grained MATLAB algorithms and Simulink models on remote MATLAB sessions Control of the distributed computing process via a function-based or an object-based interface Distributed processing on both homogeneous and heterogeneous platforms Support for synchronous and asynchronous operations Access to single or multiple clusters by single or multiple users https://tagteamdbserver.mathworks.com/ttserverroot/Download/23958_91263v01_ML_DCT_DS.pdf

Distributed Toolbox http://www.mathworks.com/products/distribtb

Creating and Submitting Jobs with the Distributed Computing Toolbox The toolbox includes functions for defining jobs, dividing them into tasks, sending them to the MATLAB Distributed Computing Engine for execution, and retrieving the results. The complete process includes five steps: Finding a job manager Creating a job Creating tasks Submitting the job to the job queue Retrieving results

Image Processing Toolbox The Image Processing Toolbox is a collection of functions that support a wide range of image processing operations, including: Spatial image transformations Morphological operations Neighborhood and block operations Linear filtering and filter design Transforms Image analysis and enhancement Image registration Deblurring Region of interest operations

Reading and displaying images The Image Processing Toolbox supports four basic types of images: Indexed images Intensity images Binary images RGB images Reading and Writing Image Data         Reading a Graphics Image         Writing a Graphics Image         Querying a Graphics File         Converting Image Storage Classes         Converting Graphics File Formats         Reading and Writing DICOM Files

Wavelet Toolbox Wavelet analysis has become very popular in image processing. The main idea is to break an image into sub-bands in different frequency and time domain. MATLAB provides an excellent toolbox for performing wavelet analysis on both one- and two-dimensional data.

Bioinformatics Toolbox The Bioinformatics Toolbox offers computational molecular biologists and other research scientists an open and extensible environment in which to explore ideas, prototype new algorithms, and build applications in drug research, genetic engineering, and other genomics and proteomics projects. The toolbox provides access to genomic and proteomic data formats, analysis techniques, and specialized visualizations for genomic and proteomic sequence and microarray analysis. Most functions are implemented in the open MATLAB language, enabling you to customize or develop your own algorithms.

Bioinformatics Toolbox Key Features Genomic, proteomic, and gene expression file formats Internet database access Functions for sequence alignment and manipulation Phylogenetic tree analysis tools Capabilities for microarray data analysis and visualization Support for mass spectrometry preprocessing and analysis Statistical learning functionality The Bioinformatics Toolbox requires the Statistics Toolbox

Signal Processing Toolbox The Signal Processing Toolbox is a collection of industry-standard algorithms for analog and digital signal processing. It provides graphical user interfaces for interactive design and analysis and command-line functions for advanced algorithm development.

SPT Key Features Comprehensive set of signal and linear system models Tools for finite impulse response (FIR) and infinite impulse response (IIR) digital filter design, analysis, and implementation Tools for analog filter design Access to the most widely used transforms, such as fast Fourier and discrete cosine Tools for spectral analysis and statistical signal processing Functions for parametric time-series modeling Routines for waveform generation, including a Gaussian pulse generator, a periodic sinc generator, and a pulse train generator Extensive data windowing algorithms

Fully or Partially Supported MATLAB-based Research Projects ASU An Efficient Medical Image Content-based Search Approach, May 2005 Steve Heffner’s honor’s thesis

Current Results Sequential (sec) Distributed (sec) 4 nodes only Image 1 406.30 99.85 Image 2 950.56 248.66 Image 3 5405.82 1323.99

Fully or Partially Supported MATLAB-based Research Projects ASU A Lifting-Based Knowledge Discovery in Microarray Data Scott A. Barllowe M.S. Thesis Current Results Sequential (sec) Distributed (sec) 2 nodes only Wave Cluster 136.78 46.17