N.-Y.: Wrox .2014. - 528 p. 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. It not only teaches readers the fundamentals of...
Morgan Kaufmann, 2012. — 600 p. — ISBN10: 0124159338, ISBN13: 978-0124159334. 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...
Morgan Kaufmann, 2011. - 886 p. "GPU computing is becoming an outstanding field in high performance computing. Due to its easiness, the CUDA approach enables programmers to take advantage of GPU-acceleration very quickly. My research in complex science as well as applications in high frequency trading benefited significantly from GPU computing. " - Dr. Tobias Preis, ETH Zurich,...
Packt Publishing, 2013. — 304 p. — ISBN: 1849692343, ISBN13: 9781849692342 Learn about all of the OpenCL Architecture and major APIs. Learn OpenCL programming with simple examples from Image Processing, Pattern Recognition and - Statistics with detailed code explanation. Explore several aspects of optimization techniques, with code examples to guide you through the process....
Addison-Wesley Professional, 2011. - 648 p. - ISBN: 0321749642 Using the new OpenCL (Open Computing Language) standard, you can write applications that access all available programming resources: CPUs, GPUs, and other processors such as DSPs and the Cell/B.E. processor. Already implemented by Apple, AMD, Intel, IBM, NVIDIA, and other leaders, OpenCL has outstanding potential...
Packt Publishing, 2013. — 303 p. — ISBN-10: 1849694524, ISBN-13: ISBN 978-1-84969-452-0. OpenCL (Open Computing Language) is the first royalty-free standard for cross platform, parallel programming of modern processors found in personal computers, servers, mobiles, and embedded devices. OpenCL greatly improves speed and responsiveness for a wide spectrum of applications in...
2nd Edition. — Morgan Kaufmann, 2012 — 514 p. — ISBN10: 0124159923, ISBN13: 978-0124159921. This best-selling guide to CUDA and GPU parallel programming has been revised with more parallel programming examples, commonly-used libraries, and explanations of the latest tools. With these improvements, the book retains its concise, intuitive, practical approach based on years of...
New York: Chapman and Hall/CRC, 2018. — 477 p. GPU Parallel Program Development using CUDA teaches GPU programming by showing the differences among different families of GPUs. This approach prepares the reader for the next generation and future generations of GPUs. The book emphasizes concepts that will remain relevant for a long time, rather than concepts that are...
Morgan Kaufmann, 2013. — 338 p. — ISBN: 9780124169708. Key Features Leverage the power of GPU computing with PGI’s CUDA Fortran compiler. Gain insights from members of the CUDA Fortran language development team. Includes multi-GPU programming in CUDA Fortran, covering both peer-to-peer and message passing interface (MPI) approaches. Includes full source code for all the...
Manning Publications, 2011. — 458 p. — ISBN: 9781617290176. OpenCL in Action is a thorough, hands-on presentation of OpenCL, with an eye toward showing developers how to build high-performance applications of their own. It begins by presenting the core concepts behind OpenCL, including vector computing, parallel programming, and multi-threaded operations, and then guides you...
IOS Press BV, 2012. — 312 p. — ISBN: 1614990298. In 2011 many computer users were exploring the opportunities and the benefits of the massive parallelism offered by heterogeneous computing. In 2000 the Khronos Group, a not-for-profit industry consortium, was founded to create standard open APIs for parallel computing, graphics and dynamic media. Among them has been OpenCL, an...
Morgan Kaufmann, 2010. — 279 p. In Praise of Programming Massively Parallel Processors: A Hands-on Approach "Parallel programming is about performance, for otherwise you’d write a sequential program. For those interested in learning or teaching the topic, a problem is where to find truly parallel hardware that can be dedicated to the task, for it is difficult to see interesting...
Morgan Kaufmann, 2011. — 336 p. — ISBN: 978-0123884268. 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...
Applications of GPU Computing Series. — Morgan Kaufmann, 2011. — 560 p. — ISBN: 0123859638. This is the second volume of Morgan Kaufmann's GPU Computing Gems, offering an all-new set of insights, ideas, and practical "hands-on" skills from researchers and developers worldwide. Each chapter gives you a window into the work being performed across a variety of application domains,...
Addison-Wesley Professional, 2015. — 352 p. CUDA for Engineers gives you direct, hands-on engagement with personal, high-performance parallel computing, enabling you to do computations on a gaming-level PC that would have required a supercomputer just a few years ago. The authors introduce the essentials of CUDA C programming clearly and concisely, quickly guiding you from...
2nd Revised edition. — Morgan Kaufmann, 2012. — 308 p. — ISBN10: 0124058949, ISBN13: 978-0124058941. "Heterogeneous Computing with OpenCL" teaches OpenCL and parallel programming for complex systems that may include a variety of device architectures: multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units (APUs) such as AMD Fusion technology. Designed to work...
3rd Edition. — Morgan Kaufmann, 2017. — 552 p. — ISBN: 978-0-12-811986-0. This book, Third Edition shows both student and professional alike the basic concepts of parallel programming and GPU architecture, exploring, in detail, various techniques for constructing parallel programs. Case studies demonstrate the development process, detailing computational thinking and ending...
Morrisville, Syncfusion Inc, 2014. — 119 p. CUDA stands for Compute Unified Device Architecture. It is a suite of technologies for programming NVIDIA graphics cards and computer hardware. CUDA C is an extension to C or C++; there are also extensions to other languages like FORTRAN, Python, and C#. CUDA is the official GPGPU architecture developed by NVIDIA. It is a mature...
3d edition. — Morgan Kaufmann. 2015. — 313 p. Heterogeneous Computing with OpenCL 2.0 teaches OpenCL and parallel programming for complex systems that may include a variety of device architectures: multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units (APUs). This fully-revised edition includes the latest enhancements in OpenCL 2.0 including: Shared virtual...
Morgan Kaufmann, 2017. — 752 p. — ISBN: 978-0-12-803738-6. This book focuses on research and practices in GPU based systems. The topics treated cover a range of issues, ranging from hardware and architectural issues, to high level issues, such as application systems, parallel programming, middleware, and power and energy issues. Divided into six parts, this edited volume...
NVIDIA Corporation, 2007. - 143 p. This document is organized into the following chapters: contains a general introduction to CUDA. outlines the programming model. describes its hardware implementation. describes the CUDA API and runtime. gives some guidance on how to achieve maximum performance. illustrates the previous chapters by walking through the code of some simple...
New York: Morgan Kaufmann, 2016. — 316 p. Parallel Programming with OpenACC is a modern, practical guide to implementing dependable computing systems. The book explains how anyone can use OpenACC to quickly ramp-up application performance using high-level code directives called pragmas. The OpenACC directive-based programming model is designed to provide a simple, yet powerful,...
Morgan Kaufmann, 2011. — 296 p. — ISBN: 0123877660. Heterogeneous Computing with OpenCL teaches OpenCL and parallel programming for complex systems that may include a variety of device architectures: multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units (APUs) such as AMD Fusion technology. Designed to work on multiple platforms and with wide industry...
Fixstars Corporation, 2010. — 246 p. — ASIN: B003H4QYHE. The OpenCL Programming Book starts with the basics of parallelization, then covers the main concepts and terminology, also teaching how to set up a development environment for OpenCL, while concluding with a walkthrough of the source code of an implementation of the fast Fourier transform (FFT) and Mersenne twister...
Adams, Fraser. Accelerating Pattern Matching with OpenCL . This book describes in detail an approach to pattern matching that uses a parallel variant of the well known Aho-Corasick algorithm implemented using OpenCL to enable heterogeneous acceleration across a range of devices. The book however primarily focusses on high-end GPU devices as they offer very good price/...
Coruna: Universidade da Coruna, 2014. — 222 p. GPU computing supposed a major step forward, bringing high performance computing to commodity hardware. Feature-rich parallel languages like CUDA and OpenCL reduced the programming complexity. However, to fully take advantage of their computing power, specialized parallel algorithms are required. Moreover, the complex GPU memory...
4th Edition. — Morgan Kaufmann\Elsevier, 2023. — 555 p. — ISBN: 978-0-323-91231-0. Programming Massively Parallel Processors: A Hands-on Approach shows both students and professionals alike the basic concepts of parallel programming and GPU architecture. Various techniques for constructing parallel programs are explored in detail. Case studies demonstrate the development...
Independent publication, 2024. — 380 p. — (GPU Mastery Series: Unlocking CUDA's Power using pyCUDA). — ASIN: B0DG48Y2N5. Book Description: Dive into the world of parallel computing with our comprehensive guide on GPU Programming using CUDA. Designed to empower developers, researchers, and enthusiasts, this tutorial unlocks the full potential of GPU acceleration using Python...
Cambridge University Press, 2022. — 474 p. — ISBN: 978-1-108-47953-0. CUDA is now the dominant language used for programming GPUs, one of the most exciting hardware developments of recent decades. With CUDA, you can use a desktop PC for work that would have previously required a large cluster of PCs or access to an HPC facility. As a result, CUDA is increasingly important in...
NVidia, 2019. — 86 p. This Best Practices Guide is a manual to help developers obtain the best performance from NVIDIA CUDA GPUs. It presents established parallelization and optimization techniques and explains coding metaphors and idioms that can greatly simplify programming for CUDA-capable GPU architectures. While the contents can be used as a reference manual, you should be...
Authors not specified. — The MathWorks, Inc., 2021. — 476 p. Functions Supported for GPU Code Generation. Kernel Creation from MatLAB Code. Kernel Creation from Simulink Models. Troubleshooting. Deep Learning. Targeting Embedded GPU Devices.
Authors not specified. — The MathWorks, Inc., 2022. — 566 p. Functions Supported for GPU Code Generation. Kernel Creation from MatLAB Code. Kernel Creation from Simulink Models. Deep Learning. Targeting Embedded GPU Devices. Troubleshooting. Troubleshooting CUDA Errors.
Authors not specified. — The MathWorks, Inc., 2023. — 714 p. Functions Supported for GPU Code Generation. Kernel Creation from MatLAB Code. Kernel Creation from Simulink Models. Deep Learning. Targeting Embedded GPU Devices. Troubleshooting. Troubleshooting CUDA Errors.
Boca Raton: CRC Press, 2025. — 385 p. The WebGPU Sourcebook: High-Performance Graphics and Machine Learning in the Browser explains how to code web applications that access the client’s graphics processor unit, or GPU. This makes it possible to render graphics in a browser at high speed and perform computationally-intensive tasks such as machine learning. By taking advantage of...
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