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
Gpu Computing Gems Jade Edition
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Author :
language : en
Publisher: Elsevier
Release Date : 2011-11-02
Gpu Computing Gems Jade Edition written by and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-11-02 with Computers categories.
GPU Computing Gems, Jade Edition, offers hands-on, proven techniques for general purpose GPU programming based on the successful application experiences of leading researchers and developers. One of few resources available that distills the best practices of the community of CUDA programmers, this second edition contains 100% new material of interest across industry, including finance, medicine, imaging, engineering, gaming, environmental science, and green computing. It covers new tools and frameworks for productive GPU computing application development and provides immediate benefit to researchers developing improved programming environments for GPUs. Divided into five sections, this book explains how GPU execution is achieved with algorithm implementation techniques and approaches to data structure layout. More specifically, it considers three general requirements: high level of parallelism, coherent memory access by threads within warps, and coherent control flow within warps. Chapters explore topics such as accelerating database searches; how to leverage the Fermi GPU architecture to further accelerate prefix operations; and GPU implementation of hash tables. There are also discussions on the state of GPU computing in interactive physics and artificial intelligence; programming tools and techniques for GPU computing; and the edge and node parallelism approach for computing graph centrality metrics. In addition, the book proposes an alternative approach that balances computation regardless of node degree variance. Software engineers, programmers, hardware engineers, and advanced students will find this book extremely usefull. For useful source codes discussed throughout the book, the editors invite readers to the following website: ..." - This second volume of GPU Computing Gems offers 100% new material of interest across industry, including finance, medicine, imaging, engineering, gaming, environmental science, green computing, and more - Covers new tools and frameworks for productive GPU computing application development and offers immediate benefit to researchers developing improved programming environments for GPUs - Even more hands-on, proven techniques demonstrating how general purpose GPU computing is changing scientific research - Distills the best practices of the community of CUDA programmers; each chapter provides insights and ideas as well as 'hands on' skills applicable to a variety of fields
Proceedings Of 2013 Chinese Intelligent Automation Conference
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Author : Zengqi Sun
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-07-10
Proceedings Of 2013 Chinese Intelligent Automation Conference written by Zengqi Sun and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-07-10 with Technology & Engineering categories.
Proceedings of the 2013 Chinese Intelligent Automation Conference presents selected research papers from the CIAC’13, held in Yangzhou, China. The topics include e.g. adaptive control, fuzzy control, neural network based control, knowledge based control, hybrid intelligent control, learning control, evolutionary mechanism based control, multi-sensor integration, failure diagnosis, and reconfigurable control. Engineers and researchers from academia, industry, and government can gain an inside view of new solutions combining ideas from multiple disciplines in the field of intelligent automation. Zengqi Sun and Zhidong Deng are professors at the Department of Computer Science, Tsinghua University, China.
Frontiers In Fractional Calculus
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Author : Sachin Bhalekar
language : en
Publisher: Bentham Science Publishers
Release Date : 2018-03-21
Frontiers In Fractional Calculus written by Sachin Bhalekar and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-21 with Mathematics categories.
This book brings together eleven topics on different aspects of fractional calculus in a single volume. It provides readers the basic knowledge of fractional calculus and introduces advanced topics and applications. The information in the book is presented in four parts: 1. Fractional Diffusion Equations: (i) solutions of fractional diffusion equations using wavelet methods, (ii) the maximum principle for time fractional diffusion equations, (iii) nonlinear sub-diffusion equations. 2. Mathematical Analysis: (i) shifted Jacobi polynomials for solving and identifying coupled fractional delay differential equations, (ii) the monotone iteration principle in the theory of Hadamard fractional delay differential equations, (iii) dynamics of fractional order modified Bhalekar-Gejji System, (iv) Grunwald-Letnikov derivatives. 3. Computational Techniques: GPU computing of special mathematical functions used in fractional calculus. 4. Reviews: (i) the popular iterative method NIM, (ii) fractional derivative with non-singular kernels, (iii) some open problems in fractional order nonlinear system This is a useful reference for researchers and graduate level mathematics students seeking knowledge about of fractional calculus and applied mathematics.
High Performance Computing For Solving Large Sparse Systems Optical Diffraction Tomography As A Case Of Study
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Author : Gloria Ortega López
language : en
Publisher: Universidad Almería
Release Date : 2015-04-14
High Performance Computing For Solving Large Sparse Systems Optical Diffraction Tomography As A Case Of Study written by Gloria Ortega López and has been published by Universidad Almería this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-04-14 with categories.
This thesis, entitled €High Performance Computing for solving large sparse systems. Optical Diffraction Tomography as a case of study€ investigates the computational issues related to the resolution of linear systems of equations which come from the discretization of physical models described by means of Partial Differential Equations (PDEs). These physical models are conceived for the description of the space-temporary behavior of some physical phenomena f(x, y, z, t) in terms of their variations (partial derivative) with respect to the dependent variables of the phenomena. There is a wide variety of discretization methods for PDEs. Two of the most well-known methods are the Finite Difference Method (FDM) and the Finite Element Method (FEM). Both methods result in an algebraic description of the model that can be translated into the approach of a linear system of equations of type (Ax = b), where A is a sparse matrix (a high percentage of zero elements) whose size depends on the required accuracy of the modeled phenomena. This thesis begins with the algebraic description of the model associated with the physical phenomena, and the work herein has been focused on the design of techniques and computational models that allow the resolution of these linear systems of equations. The main interest of this study is specially focused on models which require a high level of discretization and usually generate sparse matrices, A, which have a highly sparse structure and large size. Literature characterizes these types of problems by their high demanding computational requirements (because of their fine degree of discretization) and the sparsity of the matrices involved, suggesting that these kinds of problems can only be solved using High Performance Computing techniques and architectures. One of the main goals of this thesis is the research of the possible alternatives which allow the implementation of routines to solve large and sparse linear systems of equations using High Performance Computing (HPC). The use of massively parallel platforms (GPUs) allows the acceleration of these routines, because they have several advantages for vectorial computation schemes. On the other hand, the use of distributed memory platforms allows the resolution of problems defined by matrices of enormous size. Finally, the combination of both techniques, distributed computation and multi-GPUs, will allow faster resolution of interesting problems in which large and sparse matrices are involved. In this line, one of the goals of this thesis is to supply the scientific community with implementations based on multi-GPU clusters to solve sparse linear systems of equations, which are the key in many scientific computations. The second part of this thesis is focused on a real physical problem of Optical Diffractional Tomography (ODT) based on holographic information. ODT is a non-damaging technique which allows the extraction of the shapes of objects with high accuracy. Therefore, this technique is very suitable to the in vivo study of real specimens, microorganisms, etc., and it also makes the investigation of their dynamics possible. A preliminary physical model based on a bidimensional reconstruction of the seeding particle distribution in fluids was proposed by J. Lobera and J.M. Coupland. However, its high computational cost (in both memory requirements and runtime) made compulsory the use of HPC techniques to extend the implementation to a three dimensional model. In the second part of this thesis, the implementation and validation of this physical model for the case of three dimensional reconstructions is carried out. In such implementation, the resolution of large and sparse linear systems of equations is required. Thus, some of the algebraic routines developed in the first part of the thesis have been used to implement computational strategies capable of solving the problem of 3D reconstruction based on ODT.
Robotic Vision
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Author : Wei Qi Yan
language : en
Publisher: Springer Nature
Release Date : 2026-01-02
Robotic Vision written by Wei Qi Yan and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-01-02 with Computers categories.
Robotic vision represents the cutting edge of modern computing, combining artificial intelligence, deep learning, and advanced robotics to enable intelligent machines. As universities worldwide pivot from conventional machine learning to robotic vision, this book serves as an essential guide for researchers, educators, and students entering this transformative field. This comprehensive resource introduces core topics such as humanoid and arm-type robots, robotic image processing, stereo vision, 3D reconstruction, scene understanding, and vision-based control. Advanced algorithms, including Kalman filters, imitation learning, inverse reinforcement learning, diffusion transformers, and multimodal approaches, are explored in depth. Practical applications are seamlessly integrated with theoretical knowledge, offering lab-based exercises and discussions to enhance hands-on learning. Readers will gain unique insights into robotic navigation and planning, visual servoing, federated learning, and cutting-edge techniques like the “third eye algorithm” and camera retreat. Designed for accessibility, the book assumes no prerequisites beyond foundational courses in machine learning and deep learning, making it suitable for diverse audiences. With its structured learning approach and emphasis on both foundational principles and emerging innovations, this book is an indispensable tool for mastering robotic vision. Whether readers aim to advance research, develop autonomous systems, or integrate AI-driven robotics into real-world applications, this book provides the knowledge and skills to succeed.
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.
Archives Of Pathology Laboratory Medicine
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Author :
language : en
Publisher:
Release Date : 2008
Archives Of Pathology Laboratory Medicine written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with Diagnosis, Laboratory categories.
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.