Independent publication, 2024. — 380 р. — (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...
4th Edition. — Morgan Kaufmann\Elsevier, 2023. — 555 p. — ISBN: 978-0-323-91231-0. Programming Massively Parallel Processors: A Hands-on Approach shows both student and professional 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 process,...
2-е изд. — М.: МГУ им. М.В. Ломоносова, 2015. — 336 с.: ил. — (Суперкомпьютерное образование). — ISBN: 978-5-19-011058-6. Данная книга представляет собой подробное практическое руководство по разработке приложений с использованием технологии NVIDIA CUDA версии 4. В первой части последовательно излагаются основы программной модели CUDA применительно к языкам C и Fortran,...
Author not specified. — The MathWorks, Inc., 2023. — 714 p. Contents 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
Author not specified. — The MathWorks, Inc., 2022. — 850 p. Apps Functions Classes Objects Tools The MatLAB Coder app generates C or C++ code from MatLAB code. You can generate: • C or C++ source code, static libraries, dynamically linked libraries, and executables that you can integrate into existing C or C++ applications outside of MatLAB. • MEX functions for accelerated...
Author not specified. — The MathWorks, Inc., 2022. — 596 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 a HPC facility. As a result, CUDA is increasingly important in...
Author 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
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...
Author 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
2-е издание. — Москва: МГУ, 2015. — 336 с. — ISBN 978-5-19-011058-6. Данная книга представляет собой подробное практическое руководство по разработке приложений с использованием технологии NVIDIA CUDA версии 4. В первой части последовательно излагаются основы программной модели CUDA применительно к языкам C и Fortran, сведения о типах памяти GPU и методы эффективного...
Author not specified. — The MathWorks, Inc., 2020. — 464 p. Functions Supported for GPU Code Generation. Kernel Creation from MatLAB Code. Kernel Creation from Simulink Models. Troubleshooting. Deep Learning. Targeting Embedded GPU Devices.
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...