Accelerating Matlab With Gpu Computing

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Accelerating Matlab With Gpu Computing

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Author : Jung W. Suh
language : en
Publisher: Newnes
Release Date : 2013-11-18

Accelerating Matlab With Gpu Computing written by Jung W. Suh and has been published by Newnes this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-18 with Computers categories.

Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB codes run faster by leveraging the distributed parallelism of Graphics Processing Units (GPUs). While MATLAB successfully provides high-level functions as a simulation tool for rapid prototyping, the underlying details and knowledge needed for utilizing GPUs make MATLAB users hesitate to step into it. Accelerating MATLAB with GPUs offers a primer on bridging this gap. Starting with the basics, setting up MATLAB for CUDA (in Windows, Linux and Mac OS X) and profiling, it then guides users through advanced topics such as CUDA libraries. The authors share their experience developing algorithms using MATLAB, C++ and GPUs for huge datasets, modifying MATLAB codes to better utilize the computational power of GPUs, and integrating them into commercial software products. Throughout the book, they demonstrate many example codes that can be used as templates of C-MEX and CUDA codes for readers' projects. Download example codes from the publisher's website: http://booksite.elsevier.com/9780124080805/ - Shows how to accelerate MATLAB codes through the GPU for parallel processing, with minimal hardware knowledge - Explains the related background on hardware, architecture and programming for ease of use - Provides simple worked examples of MATLAB and CUDA C codes as well as templates that can be reused in real-world projects

Accelerating Matlab Performance

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Author : Yair M. Altman
language : en
Publisher: CRC Press
Release Date : 2014-12-11

Accelerating Matlab Performance written by Yair M. Altman and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-11 with Computers categories.

The MATLAB programming environment is often perceived as a platform suitable for prototyping and modeling but not for "serious" applications. One of the main complaints is that MATLAB is just too slow. Accelerating MATLAB Performance aims to correct this perception by describing multiple ways to greatly improve MATLAB program speed. Packed with tho

Accelerating Matlab Performance

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Author : Yair M. Altman
language : en
Publisher: CRC Press
Release Date : 2014-12-11

Accelerating Matlab Performance written by Yair M. Altman and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-11 with Computers categories.

The MATLAB® programming environment is often perceived as a platform suitable for prototyping and modeling but not for "serious" applications. One of the main complaints is that MATLAB is just too slow. Accelerating MATLAB Performance aims to correct this perception by describing multiple ways to greatly improve MATLAB program speed. Packed with thousands of helpful tips, it leaves no stone unturned, discussing every aspect of MATLAB. Ideal for novices and professionals alike, the book describes MATLAB performance in a scale and depth never before published. It takes a comprehensive approach to MATLAB performance, illustrating numerous ways to attain the desired speedup. The book covers MATLAB, CPU, and memory profiling and discusses various tradeoffs in performance tuning. It describes both the application of standard industry techniques in MATLAB, as well as methods that are specific to MATLAB such as using different data types or built-in functions. The book covers MATLAB vectorization, parallelization (implicit and explicit), optimization, memory management, chunking, and caching. It explains MATLAB’s memory model and details how it can be leveraged. It describes the use of GPU, MEX, FPGA, and other forms of compiled code, as well as techniques for speeding up deployed applications. It details specific tips for MATLAB GUI, graphics, and I/O. It also reviews a wide variety of utilities, libraries, and toolboxes that can help to improve performance. Sufficient information is provided to allow readers to immediately apply the suggestions to their own MATLAB programs. Extensive references are also included to allow those who wish to expand the treatment of a particular topic to do so easily. Supported by an active website, and numerous code examples, the book will help readers rapidly attain significant reductions in development costs and program run times.

Gpu Programming In Matlab

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Author : Nikolaos Ploskas
language : en
Publisher: Morgan Kaufmann
Release Date : 2016-08-25

Gpu Programming In Matlab written by Nikolaos Ploskas and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-25 with Computers categories.

GPU programming in MATLAB is intended for scientists, engineers, or students who develop or maintain applications in MATLAB and would like to accelerate their codes using GPU programming without losing the many benefits of MATLAB. The book starts with coverage of the Parallel Computing Toolbox and other MATLAB toolboxes for GPU computing, which allow applications to be ported straightforwardly onto GPUs without extensive knowledge of GPU programming. The next part covers built-in, GPU-enabled features of MATLAB, including options to leverage GPUs across multicore or different computer systems. Finally, advanced material includes CUDA code in MATLAB and optimizing existing GPU applications. Throughout the book, examples and source codes illustrate every concept so that readers can immediately apply them to their own development. - Provides in-depth, comprehensive coverage of GPUs with MATLAB, including the parallel computing toolbox and built-in features for other MATLAB toolboxes - Explains how to accelerate computationally heavy applications in MATLAB without the need to re-write them in another language - Presents case studies illustrating key concepts across multiple fields - Includes source code, sample datasets, and lecture slides

The Iee Irish Signals And Systems Conference 2005

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Author :
language : en
Publisher: Institution of Electrical Engineers
Release Date : 2005

The Iee Irish Signals And Systems Conference 2005 written by and has been published by Institution of Electrical Engineers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Technology & Engineering categories.

Presents a major forum for engineers and researchers in Ireland on communications, control and DSP. This conference is one of the premier conferences held in Ireland, addressing a number of aspects.

Big Data Processing With Matlab

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Author : A. Smith
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-11-12

Big Data Processing With Matlab written by A. Smith and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-12 with Big data categories.

Big data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights. With today's technology, it's possible to analyze your data and get answers from it almost immediately - an effort that's slower and less efficient with more traditional business intelligence solutions. MATLAB has the tools to work with large datasets and apply the necessary data analysis techniques. Parallel computing allows you to carry out many calculations simultaneously. Large problems can often be split into smaller ones, which are then solved at the same time. The main reasons to consider parallel computing are to: - Save time by distributing tasks and executing these simultaneously - Solve big data problems by distributing data - Take advantage of your desktop computer resources and scale up to clusters and cloud computing Parallel Computing Toolbox provides you with tools for a local cluster of workers on your client machine. MATLAB Distributed Computing Server software allows you to run as many MATLAB workers on a remote cluster of computers as your licensing allows. Most MathWorks products enable you to run applications in parallel. For example, Simulink models can run simultaneously in parallel. MATLAB Compiler and MATLAB Compiler SDK software let you build and deploy parallel applications. Several MathWorks products now offer built-in support for the parallel computing products, without requiring extra coding. Many applications involve multiple segments of code, some of which are repetitive. Often you can use for-loops to solve these cases. The ability to execute code in parallel, on one computer or on a cluster of computers, can significantly improve performance in many cases. Parallel Computing Toolbox software improves the performance of such loop execution by allowing several MATLAB workers to execute individual loop iterations simultaneously. Even running local workers all on the same machine as the client, you might see significant performance improvement on a multicore/multiprocessor machine. So whether your loop takes a long time to run because it has many iterations or because each iteration takes a long time, you can improve your loop speed by distributing iterations to MATLAB workers. When working interactively in a MATLAB session, you can offload work to a MATLAB worker session to run as a batch job. The command to perform this job is asynchronous, which means that your client MATLAB session is not blocked, and you can continue your own interactive session while the MATLAB worker is busy evaluating your code. The MATLAB worker can run either on the same machine as the client, or if using MATLAB Distributed Computing Server, on a remote cluster machine. If you have an array that is too large for your computer's memory, it cannot be easily handled in a single MATLAB session. Parallel Computing Toolbox software allows you to distribute that array among multiple MATLAB workers, so that each worker contains only a part of the array. Yet you can operate on the entire array as a single entity. Each worker operates only on its part of the array, and workers automatically transfer data between themselves when necessary, as, for example, in matrix multiplication. A large number of matrix operations and functions have been enhanced to work directly with these arrays without further modification. When writing code for Parallel Computing Toolbox software, you should advance one step at a time in the complexity of your application. Verifying your program at each step prevents your having to debug several potential problems simultaneously. If you run into any problems at any step along the way, back up to the previous step and reverify your code.

Matlab Functions For Neural Networks Models

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Author : Smith A.
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-11-23

Matlab Functions For Neural Networks Models written by Smith A. and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-23 with categories.

The MATLAB Neural Network Toolbox provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the next: .Deep learning, including convolutional neural networks and autoencoders .Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) .Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) .Unsupervised learning algorithms, including self-organizing maps and competitive layers .Apps for data-fitting, pattern recognition, and clustering .Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance .Simulink blocks for building and evaluating neural networks and for control systems applications

Neural Networks Using Matlab Pattern Recognition And Classification

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Author : K. Taylor
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-02-12

Neural Networks Using Matlab Pattern Recognition And Classification written by K. Taylor and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-12 with MATLAB. categories.

MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the following: - Deep learning, including convolutional neural networks and autoencoders - Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) - Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) - Unsupervised learning algorithms, including self-organizing maps and competitive layers - Apps for data-fitting, pattern recognition, and clustering - Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance - Simulink blocks for building and evaluating neural networks and for control systems applications This book especially develops the applications of the neural networks to the classification and the patterns recognition

Deep Learning Using Matlab Neural Network Applications

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Author : K. Taylor
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-02-16

Deep Learning Using Matlab Neural Network Applications written by K. Taylor and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-16 with categories.

Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. Deep learning is part of a broader family of machine learning methods based on learning representations of data. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction. Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain. MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the following: -Deep learning, including convolutional neural networks and autoencoders -Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) -Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) -Unsupervised learning algorithms, including self-organizing maps and competitive layers -Apps for data-fitting, pattern recognition, and clustering -Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance -Simulink(R) blocks for building and evaluating neural networks and for control systems applications This book develops deep learning, including convolutional neural networks and autoencoders and other types of advanced neural networks

Neural Networks Applications And Examples Using Matlab

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Author : J. Smith
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-02-24

Neural Networks Applications And Examples Using Matlab written by J. Smith and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-24 with MATLAB. categories.

MATLAB has the tool Neural Network Toolbox that provides algorithms, functions, and apps to create, train, visualize, and simulate neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. The toolbox includes convolutional neural network and autoencoder deep learning algorithms for image classification and feature learning tasks. To speed up training of large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox. The more important features are the following: *Deep learning, including convolutional neural networks and autoencoders *Parallel computing and GPU support for accelerating training (with Parallel Computing Toolbox) *Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) *Unsupervised learning algorithms, including self-organizing maps and competitive layers *Apps for data-fitting, pattern recognition, and clustering *Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance *Simulink(r) blocks for building and evaluating neural networks and for control systems applications