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Parallel Computing


Introduction

 

Parallel Programming in MATLAB  

 

Parallel for loops (parfor)

 

Using parfor to Run Loops in Parallel   2:49

Convert serial applications to parallel using simple annotations, such as parfor and spmd.

 

Shorten Simulation Times with Parallel Computing   6:19

Distribute computations across workers using parfor.

 

Processing Mass Spectrometry Data (High-res Ovarian Cancer data) Using Parallel Computing  

 

Accelerating Parameter Estimation using Simulink Design Optimization with Parallel Computing   4:26

 

GPU Computing with MATLAB

 

Using FFT on the GPU for Spectral Analysis    New

 

Using FFT2 on the GPU to Simulate Diffraction Patterns    New

 

Running MATLAB® Functions on the GPU    New

 

Benchmarking A\b on the GPU    New

 

Calculate Relative Area of US States    New

 

Distributed Arrays

 

Working Interactively with Distributed Arrays to Process Large Datasets: Data Parallel Applications   6:20

Handle problems with large memory requirements with constructs such as spmd and distributed arrays

 

Setting up an Interactively Developed Data Parallel Application for Batch Processing   6:51

 

Benchmarks and Performance

 

Benchmarking Parfor Performance Using a Simple Example: Game of Blackjack  

 

Resource Contention in Task Parallel Problems  

 

Benchmarking Distributed Jobs (Task Parallel Applications on the Cluster)  

 

Benchmarking Parallel "\" Operator (A\b)  

 

Profiling Load Unbalanced Distributed Arrays in Data Parallel Applications  

 

Profiling Explicit Parallel Communication while using Message Passing Functions in MATLAB  

 

Batch processing using job and task objects

 

Parallel Air Traffic Control Radar Simulation  

 

Parallel Radar Tracking Simulation  

 

Using Parallel Computing for Analysis of Origin of Human Immunodeficiency Virus  

 

Parallel Financial Portfolio Optimization  

 

Parallel Value-at-Risk (VAR) Simulation for NASDAQ Composite Index  



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