MATLAB Examples

Results for Customizing the Settings for the Examples in the Parallel Computing Toolbox

MathWorks

This example shows how to change the behavior of the examples in the Parallel Computing Toolbox™. There are at least two versions of each example in the Parallel Computing Toolbox: a sequential version and a distributed version. The distributed versions will not run unless they can submit jobs to a

the pairwise distances using the Tajima-Nei metric. This gives us a matrix of distances between sequences that we use for inferring the phylogeny of HIV and SIV viruses. PWSA is a computationally expensive task Gene Sequence Alignment Parallel Computing Toolbox Parallel Computing

This example shows how the Parallel Computing Toolbox™ can be used to perform pairwise sequence alignment (PWSA). PWSA has multiple applications in bioinformatics, such as multiple sequence analysis and phylogenetic tree reconstruction. We look at a PWSA that uses a global dynamic programming

This example shows how to create a Content Based Image Retrieval (CBIR) system using a customized bag-of-features workflow. Object Detection and Recognition Computer Vision System Toolbox Image Processing and Computer Vision

This example runs a MATLAB® benchmark that has been modified for Parallel Computing Toolbox™. We execute the benchmark on our workers to determine the relative speeds of the machines on our distributed computing network. Fluctuations of 5 or 10 percent in the measured times of repeated runs on a

analysis of financial instruments. The simulation can be done completely in parallel, except for the data collection at the end. Blackjack Parallel Computing Toolbox Parallel Computing

simulation can be done completely in parallel, except for the data collection at the end. Blackjack Parallel Computing Toolbox Parallel Computing

This example uses the Parallel Computing Toolbox™ to perform a Monte Carlo simulation of a number of stocks in a portfolio. At a given confidence level, we predict the value at risk (VaR) of the portfolio as well as the marginal value at risk (mVaR) of each of the stocks in the portfolio. We also

This example performs a Monte Carlo simulation of a number of stocks in a portfolio. At a given confidence level, we predict the value at risk (VaR) of the portfolio as well as the marginal value at risk (mVaR) of each of the stocks in the portfolio. We also provide confidence intervals for our

for them. To estimate the effectiveness of the Kalman filter, we perform repeated simulations, each time having the aircraft travel along a randomly chosen path. Radar Tracking Simulations Parallel Computing Toolbox Parallel Computing

In this example, we look at two common cases when we might want to write a wrapper function for the Parallel Computing Toolbox™. Those wrapper functions will be our task functions and will allow us to use the toolbox in an efficient manner. The particular cases are: Tutorials Parallel Computing

This example uses the Parallel Computing Toolbox™ to perform a Monte Carlo simulation of a radar station that tracks the path of an aircraft. The radar station uses the radar equation to estimate the aircraft position. We introduce measurement errors as a random variable, and the radar station

In this example, we look at how we can reduce the run time of our jobs in the Parallel Computing Toolbox™ by minimizing the network traffic. It is likely that the network bandwidth is severely limited, especially when considered relative to memory transfer speeds, and we therefore have a strong

In this example we see how to use callback functions in the Parallel Computing Toolbox™ to notify us when a task has completed and to update graphics when task results are available. We also see how to use the UserData property of the job to pass data back and forth between the MATLAB® session and

The Parallel Computing Toolbox™ enables us to execute our MATLAB® programs on a cluster of computers. In this example, we look at how to divide a large collection of MATLAB operations into smaller work units, called tasks, which the workers in our cluster can execute. We do this programmatically

This example demonstrates how to measure the Physical Downlink Shared Channel (PDSCH) throughput performance using LTE System Toolbox™ for the following transmission modes (TM): Downlink End to End Simulation LTE System Toolbox Signal Processing and Communications

This example demonstrates how to measure the Physical Downlink Shared Channel (PDSCH) throughput performance using LTE System Toolbox™ for the following non-codebook based precoding transmission modes (TM): Downlink End to End Simulation LTE System Toolbox Signal Processing and Communications

Develop and use code replacement libraries to replace function and operators in generated code. Code replacement is a technique to change the code that the code generator produces for functions and operators to meet application code requirements. For example, you can replace generated code to meet

Stuart Kozola | 09.01.16

This script contains the examples shown in the webinar titled Optimization Tips and Tricks: Getting Started using Optimization with MATLAB presented live on 21 August 2008. To view the webinar, please go here and click on recorded webinars. Applications Global Optimization Toolbox

This example shows how to use the functions GlobalSearch and MultiStart. Global or Multiple Starting Point Search Global Optimization Toolbox Math, Statistics, and Optimization

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