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Technical Articles

  • Prototyping Algorithms, Testing CUDA Kernels in MATLAB

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  • Accelerating MATLAB Algorithms and Applications

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  • Simulating Blackjack with MATLAB

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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.
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.
Topics covered include assessing code performance, adopting efficient serial programming practices, working with System objects, performing parallel computing, and generating C code.
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.
Tips and techniques to make your model run faster.
Recent enhancements to MATLAB® and Image Processing Toolbox™ dramatically increase image processing speed
Run your MATLAB code on a GPU by making a few simple changes to the code.
Mercedes engineers use a custom calibration tool to extract the highest possible performance from AMG powertrains.
This article describes how to solve large linear algebra problems by spreading them across multiple machines using distributed arrays and the single program multiple data (SPMD) language construct, available in Parallel Computing Toolbox.
We performed coupled electro-mechanical finite element analysis of an electro-statically actuated micro-electro-mechanical (MEMS) device.
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.
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.
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.
This article describes two ways to use parallel computing to accelerate the solution of computationally expensive optimization problems.
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.
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.
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.
The proliferation of multicore systems and clusters sets the stage for parallel computing with MATLAB.
Accelerator physicists at the University of London use multiple simulations and high-throughput computing to test beam-alignment algorithms.
This article describes a land-cover aggregation and mosaic process implemented with MATLAB distributed computing tools.

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