Gpu Programming In Matlab
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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
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
Image Processing With Matlab And Gpu
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Author : Antonios Georgantzoglou
language : en
Publisher:
Release Date : 2014
Image Processing With Matlab And Gpu written by Antonios Georgantzoglou and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Computers categories.
Image Processing with MATLAB and GPU.
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.
Image Processing In Matlab Roi Processing Image Degradation Color Blocks And Gpu Computing
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Author : A. Smith
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-12-30
Image Processing In Matlab Roi Processing Image Degradation Color Blocks And Gpu Computing 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 2017-12-30 with categories.
This book develops a set of reference-standard algorithms and workflow apps for image processing, analysis, visualization, and algorithm development. You can perform image segmentation, image enhancement, noise reduction, geometric transformations, image registration, and 3D image processing. Image Processing Toolbox apps let you automate common image processing workflows. You can interactively segment image data, compare image registration techniques, and batch-process large datasets. Visualization functions and apps let you explore images, 3D volumes, and videos; adjust contrast; create histograms; and manipulate regions of interest (ROIs). Image Processing Toolbox supports four methods to generate a binary mask. The binary mask defines a region of interest (ROI) of the original image. Mask pixel values of 1 indicate the image pixel belongs to the ROI. Mask pixel values of 0 indicate the image pixel is part of the background. Any binary image can be used as a mask, provided that the binary image is the same size as the image being filtered. You can create a mask from a grayscale image by classifying each pixel as belonging to either the region of interest or the background. Filtering a region of interest (ROI) is the process of applying a filter to a region in an image, where a binary mask defines the region. For example, you can apply an intensity adjustment filter to certain regions of an image. The blurring, or degradation, of an image can be caused by many factors: Movement during the image capture process (by the camera or, when long exposure times are used, by the subject), Out-of-focus optics (use of a wide-angle lens, atmospheric turbulence, or a short exposure time, which reduces the number of photons captured) and Scattered light distortion in confocal microscopy Based on this model, the fundamental task of deblurring is to deconvolve the blurred image with the PSF that exactly describes the distortion. The Image Processing Toolbox software provides functions that help you work with color image data. This toolbox supports conversions between members of the CIE family of device-independent color spaces. Certain image processing operations involve processing an image in sections, called blocks or neighborhoods, rather than processing the entire image at once. Several functions in the toolbox, such as linear filtering and morphological functions, use this approach. The toolbox includes several functions that you can use to implement image processing algorithms as a block or neighborhood operation. These functions break the input image into blocks or neighborhoods, call the specified function to process each block or neighborhood, and then reassemble the results into an output image. If you have a Parallel Computing Toolbox license, you can take advantage of multiple processor cores on your machine by specifying the blockproc setting 'UseParallel' as true. The Image Processing Toolbox includes many functions that support the generation of efficient C code using MATLAB Coder. To take advantage of the performance benefits offered by a modern graphics processing unit (GPU), certain Image Processing Toolbox functions have been enabled to perform image processing operations on a GPU. This can provide GPU acceleration for complicated image processing workflows.
Matlab Guide
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Author : Desmond J. Higham
language : en
Publisher: SIAM
Release Date : 2016-12-27
Matlab Guide written by Desmond J. Higham and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-27 with Science categories.
MATLAB is an interactive system for numerical computation that is widely used for teaching and research in industry and academia. It provides a modern programming language and problem solving environment, with powerful data structures, customizable graphics, and easy-to-use editing and debugging tools. This third edition of MATLAB Guide completely revises and updates the best-selling second edition and is more than 30 percent longer. The book remains a lively, concise introduction to the most popular and important features of MATLAB and the Symbolic Math Toolbox. Key features are a tutorial in Chapter 1 that gives a hands-on overview of MATLAB; a thorough treatment of MATLAB mathematics, including the linear algebra and numerical analysis functions and the differential equation solvers; and a web page at http://www.siam.org/books/ot150 that provides example program files, updates, and links to MATLAB resources. The new edition contains color figures throughout; includes pithy discussions of related topics in new ?Asides" boxes that augment the text; has new chapters on the Parallel Computing Toolbox, object-oriented programming, graphs, and large data sets; covers important new MATLAB data types such as categorical arrays, string arrays, tall arrays, tables, and timetables; contains more on MATLAB workflow, including the Live Editor and unit tests; and fully reflects major updates to the MATLAB graphics system. This book is suitable for both beginners and more experienced users, including students, researchers, and practitioners.
Mastering Matlab
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Author : Cybellium Ltd
language : en
Publisher: Cybellium Ltd
Release Date : 2023-09-26
Mastering Matlab written by Cybellium Ltd and has been published by Cybellium Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-26 with Computers categories.
Are you ready to revolutionize your approach to scientific computing and data analysis? "Mastering MATLAB" is your ultimate guide to harnessing the full potential of the MATLAB programming language. Whether you're a seasoned researcher seeking to enhance your capabilities or a data enthusiast ready to dive into the world of numerical analysis, this comprehensive guide will equip you with the knowledge and tools to develop sophisticated solutions for complex problems. Key Features: 1. Comprehensive Exploration of MATLAB Fundamentals: Immerse yourself in the core concepts of MATLAB programming, from its intuitive syntax to its versatile features. Build a strong foundation that empowers you to tackle intricate computational challenges with ease. 2. Data Analysis and Visualization Excellence: Dive into MATLAB's capabilities for data analysis and visualization. Learn how to manipulate and analyze data, create insightful visualizations, and extract meaningful insights from complex datasets. 3. Numerical Computing and Simulation: Uncover MATLAB's power in numerical computing and simulation. Explore techniques for solving mathematical problems, performing simulations, and optimizing algorithms to deliver accurate and efficient results. 4. Advanced Mathematics and Engineering Applications: Push the boundaries of your MATLAB knowledge by exploring advanced mathematical and engineering applications. From linear algebra to differential equations, learn how to apply MATLAB's tools to solve real-world problems. 5. Image and Signal Processing: Discover MATLAB's capabilities in image and signal processing. Master techniques for image enhancement, filtering, and feature extraction, and explore signal processing tools that enable you to work with audio and other time-based data. 6. Machine Learning and Data Science: Navigate the world of machine learning and data science using MATLAB. Learn how to implement algorithms, build predictive models, and perform classification and regression tasks with ease. 7. Optimization and Algorithm Development: Dive into MATLAB's optimization toolbox and learn how to design and implement efficient algorithms. Explore techniques for solving linear and nonlinear optimization problems to achieve optimal solutions. 8. Graphical User Interfaces (GUIs): Discover how to create interactive and user-friendly graphical user interfaces using MATLAB's App Designer. Build custom GUIs that enable users to interact with your applications intuitively. 9. Deployment and Integration: Navigate the process of deploying MATLAB applications to various platforms. Learn about integration with other programming languages, tools, and libraries, and explore techniques for sharing your work with a wider audience. Who This Book Is For: "Mastering MATLAB" is a must-have resource for professionals, researchers, students, and enthusiasts who are eager to harness the capabilities of the MATLAB programming language. Whether you're a novice or an experienced programmer seeking to explore MATLAB's vast capabilities, this book will guide you through the language's intricacies and empower you to develop advanced solutions.
Practical Gpu Graphics With Wgpu And Rust
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Author : Jack Xu
language : en
Publisher: UniCAD
Release Date : 2022-01-19
Practical Gpu Graphics With Wgpu And Rust written by Jack Xu and has been published by UniCAD this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-19 with Computers categories.
wgpu is the next-generation graphics API and future standard in Rust for both native devices and the web, aiming to provide modern 3D graphics and computation capabilities using GPU acceleration. This book provides all the tools you need to create advanced 3D graphics and GPU computing in Rust using this new wgpu API. First, this book will take you through the development environment for building wgpu applications in Rust, and then introduce Rust and wgpu basics, shader programs, GPU buffers, and rendering pipelines. Next, you will learn how to create primitives and simple objects in wgpu. As you progress through the chapters, you will get to grips with advanced wgpu topics, including 3D transformations, lighting calculations, colormaps, and textures. At the same time, you will learn how to create advanced 3D wgpu objects, including various 3D wireframes, 3D shapes, and simple and parametric 3D surfaces with colormaps and textures, as well as beautiful 2D and 3D fractal images described by complex functions. In addition, you will explore new wgpu features such as the compute shader and storage buffers, and use them to simulate large particle systems. By the end of this book, you will have the solid skills you need to build your own GPU-accelerated graphics and computing applications on both native devices and the web in Rust with the wgpu API. This book includes: - Development environment and tools for building wgpu apps in Rust. - Rust and wgpu basics, WGSL shaders, and rendering pipeline. - Primitives and simple shapes in wgpu. - 3D transformations, model, viewing, projection, and various coordinate systems. - GPU buffers, uniform buffer objects, animation, and camera controls. - Normal vectors, lighting model, ambient, diffuse, and specular light calculations. - UV coordinates, texture mapping. - Color model, colormaps, and color interpolation. - 3D shapes, wireframes, surfaces, and 3D charts. - 2D and 3D fractal images created in the fragment shader. - Compute shaders, storage buffers, and large particle system simulation.
Advanced Topics In Neural Networks With Matlab Parallel Computing Optimize And Training
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Author : PEREZ C.
language : en
Publisher: CESAR PEREZ
Release Date : 2023-12-13
Advanced Topics In Neural Networks With Matlab Parallel Computing Optimize And Training written by PEREZ C. and has been published by CESAR PEREZ this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-13 with Computers categories.
Neural networks are inherently parallel algorithms. Multicore CPUs, graphical processing units (GPUs), and clusters of computers with multiple CPUs and GPUs can take advantage of this parallelism. Parallel Computing Toolbox, when used in conjunction with Neural Network Toolbox, enables neural network training and simulation to take advantage of each mode of parallelism. Parallel Computing Toolbox allows neural network training and simulation to run across multiple CPU cores on a single PC, or across multiple CPUs on multiple computers on a network using MATLAB Distributed Computing Server. Using multiple cores can speed calculations. Using multiple computers can allow you to solve problems using data sets too big to fit in the RAM of a single computer. The only limit to problem size is the total quantity of RAM available across all computers. Distributed and GPU computing can be combined to run calculations across multiple CPUs and/or GPUs on a single computer, or on a cluster with MATLAB Distributed Computing Server. It is desirable to determine the optimal regularization parameters in an automated fashion. One approach to this process is the Bayesian framework. In this framework, the weights and biases of the network are assumed to be random variables with specified distributions. The regularization parameters are related to the unknown variances associated with these distributions. You can then estimate these parameters using statistical techniques. It is very difficult to know which training algorithm will be the fastest for a given problem. It depends on many factors, including the complexity of the problem, the number of data points in the training set, the number of weights and biases in the network, the error goal, and whether the network is being used for pattern recognition (discriminant analysis) or function approximation (regression). This book compares the various training algorithms. One of the problems that occur during neural network training is called overfitting. The error on the training set is driven to a very small value, but when new data is presented to the network the error is large. The network has memorized the training examples, but it has not learned to generalize to new situations. This book develops the following topics: Neural Networks with Parallel and GPU Computing Deep Learning Optimize Neural Network Training Speed and Memory Improve Neural Network Generalization and Avoid Overfitting Create and Train Custom Neural Network Architectures Deploy Training of Neural Networks Perceptron Neural Networks Linear Neural Networks Hopfield Neural Network Neural Network Object Reference Neural Network Simulink Block Library Deploy Neural Network Simulink Diagrams
Gpu Computing Gems Jade Edition
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Author : Wen-mei Hwu
language : en
Publisher: Elsevier
Release Date : 2011-09-28
Gpu Computing Gems Jade Edition written by Wen-mei Hwu and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-09-28 with Computers categories.
"Since the introduction of CUDA in 2007, more than 100 million computers with CUDA capable GPUs have been shipped to end users. GPU computing application developers can now expect their application to have a mass market. With the introduction of OpenCL in 2010, researchers can now expect to develop GPU applications that can run on hardware from multiple vendors"--