# GPU Programming in MATLAB

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## GPU Programming in MATLAB

Author | : Nikolaos Ploskas,Nikolaos Samaras |

Publsiher | : Morgan Kaufmann |

Total Pages | : 318 |

Release | : 2016-08-25 |

ISBN 10 | : 0128051337 |

ISBN 13 | : 9780128051337 |

Language | : EN, FR, DE, ES & NL |

**GPU Programming in MATLAB Book Review:**

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

## Gpu Programming in Matlab

Author | : Nikolaos Ploskas,Nikolaos Samaras |

Publsiher | : Morgan Kaufmann Publishers |

Total Pages | : 318 |

Release | : 2016-08-22 |

ISBN 10 | : 9780128051320 |

ISBN 13 | : 0128051329 |

Language | : EN, FR, DE, ES & NL |

**Gpu Programming in Matlab Book Review:**

"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 specialize knowledge of GPU programming. Following sections cover built-in GPU-enabled features of MATLAB, including options to leverage GPUs across multicore or different computer systems. Finally, advanced material, including CUDA code in MATLAB and optimizing existing GPU applications are presented. Throughout the book, examples and source code 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 toolboxesExplains 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 fieldsIncludes source code, sample datasets, and lecture slides

## Accelerating MATLAB with GPU Computing

Author | : Jung W. Suh,Youngmin Kim |

Publsiher | : Newnes |

Total Pages | : 258 |

Release | : 2013-11-18 |

ISBN 10 | : 0124079164 |

ISBN 13 | : 9780124079168 |

Language | : EN, FR, DE, ES & NL |

**Accelerating MATLAB with GPU Computing Book Review:**

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

Author | : Yair M. Altman |

Publsiher | : CRC Press |

Total Pages | : 785 |

Release | : 2014-12-11 |

ISBN 10 | : 1482211300 |

ISBN 13 | : 9781482211306 |

Language | : EN, FR, DE, ES & NL |

**Accelerating MATLAB Performance Book Review:**

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.

## Learn CUDA Programming

Author | : Jaegeun Han,Bharatkumar Sharma |

Publsiher | : Packt Publishing Ltd |

Total Pages | : 508 |

Release | : 2019-09-27 |

ISBN 10 | : 178899129X |

ISBN 13 | : 9781788991292 |

Language | : EN, FR, DE, ES & NL |

**Learn CUDA Programming Book Review:**

Explore different GPU programming methods using libraries and directives, such as OpenACC, with extension to languages such as C, C++, and Python Key Features Learn parallel programming principles and practices and performance analysis in GPU computing Get to grips with distributed multi GPU programming and other approaches to GPU programming Understand how GPU acceleration in deep learning models can improve their performance Book Description Compute Unified Device Architecture (CUDA) is NVIDIA's GPU computing platform and application programming interface. It's designed to work with programming languages such as C, C++, and Python. With CUDA, you can leverage a GPU's parallel computing power for a range of high-performance computing applications in the fields of science, healthcare, and deep learning. Learn CUDA Programming will help you learn GPU parallel programming and understand its modern applications. In this book, you'll discover CUDA programming approaches for modern GPU architectures. You'll not only be guided through GPU features, tools, and APIs, you'll also learn how to analyze performance with sample parallel programming algorithms. This book will help you optimize the performance of your apps by giving insights into CUDA programming platforms with various libraries, compiler directives (OpenACC), and other languages. As you progress, you'll learn how additional computing power can be generated using multiple GPUs in a box or in multiple boxes. Finally, you'll explore how CUDA accelerates deep learning algorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By the end of this CUDA book, you'll be equipped with the skills you need to integrate the power of GPU computing in your applications. What you will learn Understand general GPU operations and programming patterns in CUDA Uncover the difference between GPU programming and CPU programming Analyze GPU application performance and implement optimization strategies Explore GPU programming, profiling, and debugging tools Grasp parallel programming algorithms and how to implement them Scale GPU-accelerated applications with multi-GPU and multi-nodes Delve into GPU programming platforms with accelerated libraries, Python, and OpenACC Gain insights into deep learning accelerators in CNNs and RNNs using GPUs Who this book is for This beginner-level book is for programmers who want to delve into parallel computing, become part of the high-performance computing community and build modern applications. Basic C and C++ programming experience is assumed. For deep learning enthusiasts, this book covers Python InterOps, DL libraries, and practical examples on performance estimation.

## MATLAB

Author | : Kelly Bennett |

Publsiher | : BoD – Books on Demand |

Total Pages | : 666 |

Release | : 2014-09-08 |

ISBN 10 | : 953511719X |

ISBN 13 | : 9789535117193 |

Language | : EN, FR, DE, ES & NL |

**MATLAB Book Review:**

MATLAB is an indispensable asset for scientists, researchers, and engineers. The richness of the MATLAB computational environment combined with an integrated development environment (IDE) and straightforward interface, toolkits, and simulation and modeling capabilities, creates a research and development tool that has no equal. From quick code prototyping to full blown deployable applications, MATLAB stands as a de facto development language and environment serving the technical needs of a wide range of users. As a collection of diverse applications, each book chapter presents a novel application and use of MATLAB for a specific result.

## Recent Progress in Parallel and Distributed Computing

Author | : Wen-Jyi Hwang |

Publsiher | : BoD – Books on Demand |

Total Pages | : 124 |

Release | : 2017-07-19 |

ISBN 10 | : 9535133152 |

ISBN 13 | : 9789535133155 |

Language | : EN, FR, DE, ES & NL |

**Recent Progress in Parallel and Distributed Computing Book Review:**

Parallel and distributed computing has been one of the most active areas of research in recent years. The techniques involved have found significant applications in areas as diverse as engineering, management, natural sciences, and social sciences. This book reports state-of-the-art topics and advances in this emerging field. Completely up-to-date, aspects it examines include the following: 1) Social networks; 2) Smart grids; 3) Graphic processing unit computation; 4) Distributed software development tools; 5) Analytic hierarchy process and the analytic network process

## MATLAB for Neuroscientists

Author | : Pascal Wallisch,Michael E. Lusignan,Marc D. Benayoun,Tanya I. Baker,Adam Seth Dickey,Nicholas G. Hatsopoulos |

Publsiher | : Academic Press |

Total Pages | : 570 |

Release | : 2014-01-09 |

ISBN 10 | : 0123838371 |

ISBN 13 | : 9780123838377 |

Language | : EN, FR, DE, ES & NL |

**MATLAB for Neuroscientists Book Review:**

MATLAB for Neuroscientists serves as the only complete study manual and teaching resource for MATLAB, the globally accepted standard for scientific computing, in the neurosciences and psychology. This unique introduction can be used to learn the entire empirical and experimental process (including stimulus generation, experimental control, data collection, data analysis, modeling, and more), and the 2nd Edition continues to ensure that a wide variety of computational problems can be addressed in a single programming environment. This updated edition features additional material on the creation of visual stimuli, advanced psychophysics, analysis of LFP data, choice probabilities, synchrony, and advanced spectral analysis. Users at a variety of levels—advanced undergraduates, beginning graduate students, and researchers looking to modernize their skills—will learn to design and implement their own analytical tools, and gain the fluency required to meet the computational needs of neuroscience practitioners. The first complete volume on MATLAB focusing on neuroscience and psychology applications Problem-based approach with many examples from neuroscience and cognitive psychology using real data Illustrated in full color throughout Careful tutorial approach, by authors who are award-winning educators with strong teaching experience

## Financial Modelling

Author | : Joerg Kienitz,Daniel Wetterau |

Publsiher | : John Wiley & Sons |

Total Pages | : 734 |

Release | : 2013-02-18 |

ISBN 10 | : 0470744898 |

ISBN 13 | : 9780470744895 |

Language | : EN, FR, DE, ES & NL |

**Financial Modelling Book Review:**

Financial modelling Theory, Implementation and Practice with Matlab Source Jörg Kienitz and Daniel Wetterau Financial Modelling - Theory, Implementation and Practice with MATLAB Source is a unique combination of quantitative techniques, the application to financial problems and programming using Matlab. The book enables the reader to model, design and implement a wide range of financial models for derivatives pricing and asset allocation, providing practitioners with complete financial modelling workflow, from model choice, deriving prices and Greeks using (semi-) analytic and simulation techniques, and calibration even for exotic options. The book is split into three parts. The first part considers financial markets in general and looks at the complex models needed to handle observed structures, reviewing models based on diffusions including stochastic-local volatility models and (pure) jump processes. It shows the possible risk-neutral densities, implied volatility surfaces, option pricing and typical paths for a variety of models including SABR, Heston, Bates, Bates-Hull-White, Displaced-Heston, or stochastic volatility versions of Variance Gamma, respectively Normal Inverse Gaussian models and finally, multi-dimensional models. The stochastic-local-volatility Libor market model with time-dependent parameters is considered and as an application how to price and risk-manage CMS spread products is demonstrated. The second part of the book deals with numerical methods which enables the reader to use the models of the first part for pricing and risk management, covering methods based on direct integration and Fourier transforms, and detailing the implementation of the COS, CONV, Carr-Madan method or Fourier-Space-Time Stepping. This is applied to pricing of European, Bermudan and exotic options as well as the calculation of the Greeks. The Monte Carlo simulation technique is outlined and bridge sampling is discussed in a Gaussian setting and for Lévy processes. Computation of Greeks is covered using likelihood ratio methods and adjoint techniques. A chapter on state-of-the-art optimization algorithms rounds up the toolkit for applying advanced mathematical models to financial problems and the last chapter in this section of the book also serves as an introduction to model risk. The third part is devoted to the usage of Matlab, introducing the software package by describing the basic functions applied for financial engineering. The programming is approached from an object-oriented perspective with examples to propose a framework for calibration, hedging and the adjoint method for calculating Greeks in a Libor market model. Source code used for producing the results and analysing the models is provided on the author's dedicated website, http://www.mathworks.de/matlabcentral/fileexchange/authors/246981.

## Spectral Methods in MATLAB

Author | : Lloyd N. Trefethen |

Publsiher | : SIAM |

Total Pages | : 165 |

Release | : 2000-07-01 |

ISBN 10 | : 0898714656 |

ISBN 13 | : 9780898714654 |

Language | : EN, FR, DE, ES & NL |

**Spectral Methods in MATLAB Book Review:**

Mathematics of Computing -- Numerical Analysis.

## Understanding LTE with MATLAB

Author | : Houman Zarrinkoub |

Publsiher | : John Wiley & Sons |

Total Pages | : 512 |

Release | : 2014-01-28 |

ISBN 10 | : 1118443454 |

ISBN 13 | : 9781118443453 |

Language | : EN, FR, DE, ES & NL |

**Understanding LTE with MATLAB Book Review:**

An introduction to technical details related to the PhysicalLayer of the LTE standard with MATLAB® The LTE (Long Term Evolution) and LTE-Advanced are among thelatest mobile communications standards, designed to realize thedream of a truly global, fast, all-IP-based, secure broadbandmobile access technology. This book examines the Physical Layer (PHY) of the LTE standardsby incorporating three conceptual elements: an overview of thetheory behind key enabling technologies; a concise discussionregarding standard specifications; and the MATLAB® algorithmsneeded to simulate the standard. The use of MATLAB®, a widely used technical computinglanguage, is one of the distinguishing features of this book.Through a series of MATLAB® programs, the author explores eachof the enabling technologies, pedagogically synthesizes an LTE PHYsystem model, and evaluates system performance at each stage.Following this step-by-step process, readers will achieve deeperunderstanding of LTE concepts and specifications throughsimulations. Key Features: • Accessible, intuitive, and progressive; one of the fewbooks to focus primarily on the modeling, simulation, andimplementation of the LTE PHY standard • Includes case studies and testbenches in MATLAB®,which build knowledge gradually and incrementally until afunctional specification for the LTE PHY is attained • Accompanying Web site includes all MATLAB® programs,together with PowerPoint slides and other illustrative examples Dr Houman Zarrinkoub has served as a development manager andnow as a senior product manager with MathWorks, based inMassachusetts, USA. Within his 12 years at MathWorks, he has beenresponsible for multiple signal processing and communicationssoftware tools. Prior to MathWorks, he was a research scientist inthe Wireless Group at Nortel Networks, where he contributed tomultiple standardization projects for 3G mobile technologies. Hehas been awarded multiple patents on topics related to computersimulations. He holds a BSc degree in Electrical Engineering fromMcGill University and MSc and PhD degrees in Telecommunicationsfrom the Institut Nationale de la Recherche Scientifique, inCanada. ahref="http://www.wiley.com/go/zarrinkoub"www.wiley.com/go/zarrinkoub/a

## Hands On GPU Computing with Python

Author | : Avimanyu Bandyopadhyay |

Publsiher | : Packt Publishing Ltd |

Total Pages | : 452 |

Release | : 2019-05-14 |

ISBN 10 | : 1789342406 |

ISBN 13 | : 9781789342406 |

Language | : EN, FR, DE, ES & NL |

**Hands On GPU Computing with Python Book Review:**

Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda Accelerate Key Features Understand effective synchronization strategies for faster processing using GPUs Write parallel processing scripts with PyCuda and PyOpenCL Learn to use the CUDA libraries like CuDNN for deep learning on GPUs Book Description GPUs are proving to be excellent general purpose-parallel computing solutions for high performance tasks such as deep learning and scientific computing. This book will be your guide to getting started with GPU computing. It will start with introducing GPU computing and explain the architecture and programming models for GPUs. You will learn, by example, how to perform GPU programming with Python, and you’ll look at using integrations such as PyCUDA, PyOpenCL, CuPy and Numba with Anaconda for various tasks such as machine learning and data mining. Going further, you will get to grips with GPU work flows, management, and deployment using modern containerization solutions. Toward the end of the book, you will get familiar with the principles of distributed computing for training machine learning models and enhancing efficiency and performance. By the end of this book, you will be able to set up a GPU ecosystem for running complex applications and data models that demand great processing capabilities, and be able to efficiently manage memory to compute your application effectively and quickly. What you will learn Utilize Python libraries and frameworks for GPU acceleration Set up a GPU-enabled programmable machine learning environment on your system with Anaconda Deploy your machine learning system on cloud containers with illustrated examples Explore PyCUDA and PyOpenCL and compare them with platforms such as CUDA, OpenCL and ROCm. Perform data mining tasks with machine learning models on GPUs Extend your knowledge of GPU computing in scientific applications Who this book is for Data Scientist, Machine Learning enthusiasts and professionals who wants to get started with GPU computation and perform the complex tasks with low-latency. Intermediate knowledge of Python programming is assumed.

## CUDA Programming

Author | : Shane Cook |

Publsiher | : Newnes |

Total Pages | : 576 |

Release | : 2013 |

ISBN 10 | : 0124159338 |

ISBN 13 | : 9780124159334 |

Language | : EN, FR, DE, ES & NL |

**CUDA Programming Book Review:**

If you need to learn CUDA but don't have experience with parallel computing, CUDA Programming: A Developer's Introduction offers a detailed guide to CUDA with a grounding in parallel fundamentals. It starts by introducing CUDA and bringing you up to speed on GPU parallelism and hardware, then delving into CUDA installation. Chapters on core concepts including threads, blocks, grids, and memory focus on both parallel and CUDA-specific issues. Later, the book demonstrates CUDA in practice for optimizing applications, adjusting to new hardware, and solving common problems. Comprehensive introduction to parallel programming with CUDA, for readers new to both Detailed instructions help readers optimize the CUDA software development kit Practical techniques illustrate working with memory, threads, algorithms, resources, and more Covers CUDA on multiple hardware platforms: Mac, Linux and Windows with several NVIDIA chipsets Each chapter includes exercises to test reader knowledge

## Matlab

Author | : Stormy Attaway |

Publsiher | : Butterworth-Heinemann |

Total Pages | : 560 |

Release | : 2013-06-03 |

ISBN 10 | : 0124058930 |

ISBN 13 | : 9780124058934 |

Language | : EN, FR, DE, ES & NL |

**Matlab Book Review:**

MatLab, Third Edition is the only book that gives a full introduction to programming in MATLAB combined with an explanation of the software’s powerful functions, enabling engineers to fully exploit its extensive capabilities in solving engineering problems. The book provides a systematic, step-by-step approach, building on concepts throughout the text, facilitating easier learning. Sections on common pitfalls and programming guidelines direct students towards best practice. The book is organized into 14 chapters, starting with programming concepts such as variables, assignments, input/output, and selection statements; moves onto loops; and then solves problems using both the ‘programming concept’ and the ‘power of MATLAB’ side-by-side. In-depth coverage is given to input/output, a topic that is fundamental to many engineering applications. Vectorized Code has been made into its own chapter, in order to emphasize the importance of using MATLAB efficiently. There are also expanded examples on low-level file input functions, Graphical User Interfaces, and use of MATLAB Version R2012b; modified and new end-of-chapter exercises; improved labeling of plots; and improved standards for variable names and documentation. This book will be a valuable resource for engineers learning to program and model in MATLAB, as well as for undergraduates in engineering and science taking a course that uses (or recommends) MATLAB. Presents programming concepts and MATLAB built-in functions side-by-side Systematic, step-by-step approach, building on concepts throughout the book, facilitating easier learning Sections on common pitfalls and programming guidelines direct students towards best practice

## Professional CUDA C Programming

Author | : John Cheng,Max Grossman,Ty McKercher |

Publsiher | : John Wiley & Sons |

Total Pages | : 528 |

Release | : 2014-09-09 |

ISBN 10 | : 1118739329 |

ISBN 13 | : 9781118739327 |

Language | : EN, FR, DE, ES & NL |

**Professional CUDA C Programming Book Review:**

Break into the powerful world of parallel GPU programming with this down-to-earth, practical guide Designed for professionals across multiple industrial sectors, Professional CUDA C Programming presents CUDA -- a parallel computing platform and programming model designed to ease the development of GPU programming -- fundamentals in an easy-to-follow format, and teaches readers how to think in parallel and implement parallel algorithms on GPUs. Each chapter covers a specific topic, and includes workable examples that demonstrate the development process, allowing readers to explore both the "hard" and "soft" aspects of GPU programming. Computing architectures are experiencing a fundamental shift toward scalable parallel computing motivated by application requirements in industry and science. This book demonstrates the challenges of efficiently utilizing compute resources at peak performance, presents modern techniques for tackling these challenges, while increasing accessibility for professionals who are not necessarily parallel programming experts. The CUDA programming model and tools empower developers to write high-performance applications on a scalable, parallel computing platform: the GPU. However, CUDA itself can be difficult to learn without extensive programming experience. Recognized CUDA authorities John Cheng, Max Grossman, and Ty McKercher guide readers through essential GPU programming skills and best practices in Professional CUDA C Programming, including: CUDA Programming Model GPU Execution Model GPU Memory model Streams, Event and Concurrency Multi-GPU Programming CUDA Domain-Specific Libraries Profiling and Performance Tuning The book makes complex CUDA concepts easy to understand for anyone with knowledge of basic software development with exercises designed to be both readable and high-performance. For the professional seeking entrance to parallel computing and the high-performance computing community, Professional CUDA C Programming is an invaluable resource, with the most current information available on the market.

## Highly Parallel Computing

Author | : George S. Almasi,Allan Gottlieb |

Publsiher | : Benjamin-Cummings Publishing Company |

Total Pages | : 689 |

Release | : 1994 |

ISBN 10 | : |

ISBN 13 | : UOM:39015009120117 |

Language | : EN, FR, DE, ES & NL |

**Highly Parallel Computing Book Review:**

This second edition includes new exercises for each chapter, a quantitative treatment of speedup, seismic migration, using a workstation network as a parallel computer, recent changes in technology, more languages, fat trees, wormhole switching, new SIMD hardware, an expanded section on CM-2, new MIMD hardware, using workstation clusters as a MIMD system, and directory based caches. Annotation copyright by Book News, Inc., Portland, OR

## Embedded Computing for High Performance

Author | : João Manuel Paiva Cardoso,José Gabriel de Figueiredo Coutinho,Pedro C. Diniz |

Publsiher | : Morgan Kaufmann |

Total Pages | : 320 |

Release | : 2017-06-13 |

ISBN 10 | : 0128041994 |

ISBN 13 | : 9780128041994 |

Language | : EN, FR, DE, ES & NL |

**Embedded Computing for High Performance Book Review:**

Embedded Computing for High Performance: Design Exploration and Customization Using High-level Compilation and Synthesis Tools provides a set of real-life example implementations that migrate traditional desktop systems to embedded systems. Working with popular hardware, including Xilinx and ARM, the book offers a comprehensive description of techniques for mapping computations expressed in programming languages such as C or MATLAB to high-performance embedded architectures consisting of multiple CPUs, GPUs, and reconfigurable hardware (FPGAs). The authors demonstrate a domain-specific language (LARA) that facilitates retargeting to multiple computing systems using the same source code. In this way, users can decouple original application code from transformed code and enhance productivity and program portability. After reading this book, engineers will understand the processes, methodologies, and best practices needed for the development of applications for high-performance embedded computing systems. Focuses on maximizing performance while managing energy consumption in embedded systems Explains how to retarget code for heterogeneous systems with GPUs and FPGAs Demonstrates a domain-specific language that facilitates migrating and retargeting existing applications to modern systems Includes downloadable slides, tools, and tutorials

## OpenCL Programming by Example

Author | : Ravishekhar Banger,Koushik Bhattacharyya |

Publsiher | : Packt Publishing Ltd |

Total Pages | : 304 |

Release | : 2013-12-23 |

ISBN 10 | : 1849692351 |

ISBN 13 | : 9781849692359 |

Language | : EN, FR, DE, ES & NL |

**OpenCL Programming by Example Book Review:**

This book follows an example-driven, simplified, and practical approach to using OpenCL for general purpose GPU programming. If you are a beginner in parallel programming and would like to quickly accelerate your algorithms using OpenCL, this book is perfect for you! You will find the diverse topics and case studies in this book interesting and informative. You will only require a good knowledge of C programming for this book, and an understanding of parallel implementations will be useful, but not necessary.

## Accelerating MATLAB Performance

Author | : Yair M. Altman |

Publsiher | : CRC Press |

Total Pages | : 785 |

Release | : 2014-12-11 |

ISBN 10 | : 1482211300 |

ISBN 13 | : 9781482211306 |

Language | : EN, FR, DE, ES & NL |

**Accelerating MATLAB Performance Book Review:**

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.

## Recent Progress in Parallel and Distributed Computing

Author | : Wen-Jyi Hwang |

Publsiher | : BoD – Books on Demand |

Total Pages | : 124 |

Release | : 2017-07-19 |

ISBN 10 | : 9535133152 |

ISBN 13 | : 9789535133155 |

Language | : EN, FR, DE, ES & NL |

**Recent Progress in Parallel and Distributed Computing Book Review:**

Parallel and distributed computing has been one of the most active areas of research in recent years. The techniques involved have found significant applications in areas as diverse as engineering, management, natural sciences, and social sciences. This book reports state-of-the-art topics and advances in this emerging field. Completely up-to-date, aspects it examines include the following: 1) Social networks; 2) Smart grids; 3) Graphic processing unit computation; 4) Distributed software development tools; 5) Analytic hierarchy process and the analytic network process