Technical Articles

  • Selecting a MATLAB Application Deployment Strategy

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  • Solving Geometric Problems with GPU Computing in MATLAB

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  • The Gatlinburg and Householder Symposia

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This article guides you through the process of selecting the best deployment approach for your MATLAB application. It covers three strategies: using batch processing, generating C code with MATLAB Coder, and deploying with MATLAB Compiler.
Oct 2014
In this paper, a model represents a battery pack and features cell creation, placement, and connection using automation scripts. The paper presents an assessment of model partitioning schemes for real-time execution on multicore targets.
Sep 2014
This article describes how to modify an algorithm to run on a GPU, and then solve a geometric problem involving millions of lines and shapes in under a second.
Jul 2014
From 1961 to the present, the Gatlinburg and Householder symposia on numerical computing have played an important role in the history and development of MATLAB.
Oct 2013
Use a layered approach to break the parameter estimation problem into a subset of data and parameter values so that the optimizer can focus on a specific problem.
Apr 2013
MATLAB supports CUDA kernel development by providing a language and development environment for quickly evaluating kernels, analyzing and visualizing kernel results, and writing test harnesses to validate kernel results.
Apr 2013
Cleve Moler presents MATLAB code for simulating basic strategy, and explains why simulating blackjack play in MATLAB is both an instructive programming exercise and a useful parallel computing benchmark.
Oct 2012
Examples of how financial professionals from more than 2300 organizations worldwide use MATLAB to develop and implement financial models, analyze substantial volumes of data, and operate under tightening regulation.
Oct 2012
With accurate vehicle simulation models enable engineers to quantitatively determine the optimal tradeoff between the conflicting demands of vehicle performance and fuel economy.
Nov 2011
UC Berkeley's Center for Astronomy Signal Processing and Electronics Research developed a Simulink based component library for building and deploying sophisticated instruments at a fraction of the cost of custom-hardware instruments.
May 2011
This paper recommends best practices for creating an infrastructure and deploying large-scale models for embedded applications using Model-Based Design.
Dec 2010
We performed coupled electro-mechanical finite element analysis of an electro-statically actuated micro-electro-mechanical (MEMS) device.
Jul 2010
University of Illinois researchers use advanced statistical methods to explain how changes in climate affect the ecosystem and how human changes to landscape affect the regional climate.
Feb 2010
In this paper, the minimum number of test measurements required to optimally calibrate the steady-state spark advance and cam-phaser settings of a SI DIVCP engine is determined using a high-fidelity model of a 2.2L SI DIVCP engine.
Dec 2009
This article provides brief profiles of 7 customers who use parallel computing to solve computationally intensive problems: Max Planck Institute, EIM Group, Argonne National Laboratory, C-COR, MIT, Univ of London, Univ of Geneva.
Nov 2009
Using an aerospace system model as an example, this article describes the parallelization of a controller parameter tuning task using Parallel Computing Toolbox and Simulink Design Optimization.
May 2009
This article describes two ways to use parallel computing to accelerate the solution of computationally expensive optimization problems.
Mar 2009
Using a typical numerical computing problem as an example, this article describes how to threads and parallel for loops to get code to work well in a multicore system.
Sep 2008
This paper uses a hydromechanical actuator as an example to illustrate techniques for modeling, optimizing, and testing plant models in MATLAB® and Simulink®. High-performance computing clusters are used to speed up Monte Carlo techniques.
Aug 2008
This paper presents new methods for distributing Monte Carlo analyses of system models across multiple machines. These methods reduce testing time and enable more complete analyses, ensuring better quality when designs go into production.
Jan 2008
Short Description/Meta Description (250 character limit): This paper studies techniques that can be used to reduce the time needed to run block diagram simulations, including automatic code generation and cluster computing.
Nov 2007
The proliferation of multicore systems and clusters sets the stage for parallel computing with MATLAB.
Jun 2007
Accelerator physicists at the University of London use multiple simulations and high-throughput computing to test beam-alignment algorithms.
Oct 2006
This article describes a land-cover aggregation and mosaic process implemented with MATLAB distributed computing tools.
Jan 2006

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