Books: 8


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 more » 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 « less
Professional CUDA Programming in C provides down to earth coverage of the complex topic of parallel computing, a topic increasingly essential in every day computing. This entry-level programming book for professionals turns complex subjects into easy-to-comprehend concepts and easy-to-follows steps. more » It not only teaches readers the fundamentals of parallel programming with GPUs, but also helps readers think in parallel. The book covers major CUDA features with the main focus on GPU programming skills and best practices that delivers outstanding performance. « less
A Primer with Examples
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 more » 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: * 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 « less
A Comprehensive Guide to GPU Programming
The CUDA Handbook begins where CUDA by Example (Addison-Wesley, 2011) leaves off, discussing CUDA hardware and software in greater detail and covering both CUDA 5.0 and Kepler. Every CUDA developer, from the casual to the most sophisticated, will find something here of interest and immediate usefulness. more » Newer CUDA developers will see how the hardware processes commands and how the driver checks progress; more experienced CUDA developers will appreciate the expert coverage of topics such as the driver API and context migration, as well as the guidance on how best to structure CPU/GPU data interchange and synchronization. « less
A Developer's Guide to Parallel Computing with GPUs
If you need to learn CUDA but don't have experience with parallel computing, CUDA Programming: A Developer's Introductionoffers 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 more » 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 « less
As the computer industry retools to leverage massively parallel graphics processing units (GPUs), this book is designed to meet the needs of working software developers who need to understand GPU programming with CUDA and increase efficiency in their projects. CUDA Application Design and Development more » starts with an introduction to parallel computing concepts for readers with no previous parallel experience, and focuses on issues of immediate importance to working software developers: achieving high performance, maintaining competitiveness, analyzing CUDA benefits versus costs, and determining application lifespan. The book then details the thought behind CUDA and teaches how to create, analyze, and debug CUDA applications. Throughout, the focus is on software engineering issues: how to use CUDA in the context of existing application code, with existing compilers, languages, software tools, and industry-standard API libraries. Using an approach refined in a series of well-received articles at Dr Dobb's Journal, author Rob Farber takes the reader step-by-step from fundamentals to implementation, moving from language theory to practical coding. * Includes multiple examples building from simple to more complex applications in four key areas: machine learning, visualization, vision recognition, and mobile computing * Addresses the foundational issues for CUDA development: multi-threaded programming and the different memory hierarchy * Includes teaching chapters designed to give a full understanding of CUDA tools, techniques and structure. * Presents CUDA techniques in the context of the hardware they are implemented on as well as other styles of programming that will help readers bridge into the new material « less
GPU Computing Gems, Jade Edition describes successful application experiences in GPU computing and the techniques that contributed to that success. Divided into five sections, the book explains how GPU execution is achieved with algorithm implementation techniques and approaches to data structure layout. more » More specifically, it considers three general requirements: high level of parallelism, coherent memory access by threads within warps, and coherent control flow within warps. This book begins with an overview of parallel algorithms and data structures. The first few chapters focus on accelerating database searches, how to leverage the Fermi GPU architecture to further accelerate prefix operations, and GPU implementation of hash tables. The reader is then systematically walked through the fundamental optimization steps when implementing a bandwidth-limited algorithm, GPU-based libraries of numerical algorithms and software products for numerical analysis with dedicated GPU support, and the adoption of GPU computing techniques in production engineering simulation codes. The next chapters discuss 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. The book also proposes an alternative approach that balances computation regardless of node degree variance. This book will be useful to application developers in a wide range of application areas. * 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 « less
“This book is required reading for anyone working with accelerator-based computing systems.” –From the Foreword by Jack Dongarra, University of Tennessee and Oak Ridge National Laboratory CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction more » with a comprehensive software platform, the CUDA Architecture enables programmers to draw on the immense power of graphics processing units (GPUs) when building high-performance applications. GPUs, of course, have long been available for demanding graphics and game applications. CUDA now brings this valuable resource to programmers working on applications in other domains, including science, engineering, and finance. No knowledge of graphics programming is required–just the ability to program in a modestly extended version of C.   CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology. The authors introduce each area of CUDA development through working examples. After a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C, the book details the techniques and trade-offs associated with each key CUDA feature. You’ll discover when to use each CUDA C extension and how to write CUDA software that delivers truly outstanding performance.   Major topics covered include * Parallel programming * Thread cooperation * Constant memory and events * Texture memory * Graphics interoperability * Atomics * Streams * CUDA C on multiple GPUs * Advanced atomics * Additional CUDA resources All the CUDA software tools you’ll need are freely available for download from NVIDIA. « less