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...
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...
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., 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.
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...
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...
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.
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...
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...
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/...
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...
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...
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...
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...
Addison-Wesley Professional, 2017. — 320 p. — ISBN13: 978-0134694283. Scientists and technical professionals can use OpenACC to leverage the immense power of modern GPUs without the complexity traditionally associated with programming them. OpenACC for Programmers is one of the first comprehensive and practical overviews of OpenACC for massively parallel programming. This book...
Addison-Wesley Professional, 2017. — 320 p. — ISBN13: 978-0134694283. Scientists and technical professionals can use OpenACC to leverage the immense power of modern GPUs without the complexity traditionally associated with programming them. OpenACC for Programmers is one of the first comprehensive and practical overviews of OpenACC for massively parallel programming. This book...
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...
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,...