Hands On Gpu Programming With Cuda
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Hands On Gpu Programming With Python And Cuda
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Author : Dr. Brian Tuomanen
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-11-27
Hands On Gpu Programming With Python And Cuda written by Dr. Brian Tuomanen and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-27 with Computers categories.
Build real-world applications with Python 2.7, CUDA 9, and CUDA 10. We suggest the use of Python 2.7 over Python 3.x, since Python 2.7 has stable support across all the libraries we use in this book. Key FeaturesExpand your background in GPU programming—PyCUDA, scikit-cuda, and NsightEffectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolverApply GPU programming to modern data science applicationsBook Description Hands-On GPU Programming with Python and CUDA hits the ground running: you’ll start by learning how to apply Amdahl’s Law, use a code profiler to identify bottlenecks in your Python code, and set up an appropriate GPU programming environment. You’ll then see how to “query” the GPU’s features and copy arrays of data to and from the GPU’s own memory. As you make your way through the book, you’ll launch code directly onto the GPU and write full blown GPU kernels and device functions in CUDA C. You’ll get to grips with profiling GPU code effectively and fully test and debug your code using Nsight IDE. Next, you’ll explore some of the more well-known NVIDIA libraries, such as cuFFT and cuBLAS. With a solid background in place, you will now apply your new-found knowledge to develop your very own GPU-based deep neural network from scratch. You’ll then explore advanced topics, such as warp shuffling, dynamic parallelism, and PTX assembly. In the final chapter, you’ll see some topics and applications related to GPU programming that you may wish to pursue, including AI, graphics, and blockchain. By the end of this book, you will be able to apply GPU programming to problems related to data science and high-performance computing. What you will learnLaunch GPU code directly from PythonWrite effective and efficient GPU kernels and device functionsUse libraries such as cuFFT, cuBLAS, and cuSolverDebug and profile your code with Nsight and Visual ProfilerApply GPU programming to datascience problemsBuild a GPU-based deep neuralnetwork from scratchExplore advanced GPU hardware features, such as warp shufflingWho this book is for Hands-On GPU Programming with Python and CUDA is for developers and data scientists who want to learn the basics of effective GPU programming to improve performance using Python code. You should have an understanding of first-year college or university-level engineering mathematics and physics, and have some experience with Python as well as in any C-based programming language such as C, C++, Go, or Java.
Hands On Gpu Programming With Cuda C And Python 3
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Author : DR. BRIAN. TUOMANEN
language : en
Publisher:
Release Date : 2020
Hands On Gpu Programming With Cuda C And Python 3 written by DR. BRIAN. TUOMANEN and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Hands On Gpu Computing With Python
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Author : Avimanyu Bandyopadhyay
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-14
Hands On Gpu Computing With Python written by Avimanyu Bandyopadhyay and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-14 with Computers categories.
Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda Accelerate Key FeaturesUnderstand effective synchronization strategies for faster processing using GPUsWrite parallel processing scripts with PyCuda and PyOpenCLLearn to use the CUDA libraries like CuDNN for deep learning on GPUsBook 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 learnUtilize Python libraries and frameworks for GPU accelerationSet up a GPU-enabled programmable machine learning environment on your system with AnacondaDeploy your machine learning system on cloud containers with illustrated examplesExplore PyCUDA and PyOpenCL and compare them with platforms such as CUDA, OpenCL and ROCm.Perform data mining tasks with machine learning models on GPUsExtend your knowledge of GPU computing in scientific applicationsWho 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.
Hands On Gpu Accelerated Computer Vision With Opencv And Cuda
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Author : Bhaumik Vaidya
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-09-26
Hands On Gpu Accelerated Computer Vision With Opencv And Cuda written by Bhaumik Vaidya and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-09-26 with Computers categories.
Discover how CUDA allows OpenCV to handle complex and rapidly growing image data processing in computer and machine vision by accessing the power of GPU Key FeaturesExplore examples to leverage the GPU processing power with OpenCV and CUDAEnhance the performance of algorithms on embedded hardware platformsDiscover C++ and Python libraries for GPU accelerationBook Description Computer vision has been revolutionizing a wide range of industries, and OpenCV is the most widely chosen tool for computer vision with its ability to work in multiple programming languages. Nowadays, in computer vision, there is a need to process large images in real time, which is difficult to handle for OpenCV on its own. This is where CUDA comes into the picture, allowing OpenCV to leverage powerful NVDIA GPUs. This book provides a detailed overview of integrating OpenCV with CUDA for practical applications. To start with, you’ll understand GPU programming with CUDA, an essential aspect for computer vision developers who have never worked with GPUs. You’ll then move on to exploring OpenCV acceleration with GPUs and CUDA by walking through some practical examples. Once you have got to grips with the core concepts, you’ll familiarize yourself with deploying OpenCV applications on NVIDIA Jetson TX1, which is popular for computer vision and deep learning applications. The last chapters of the book explain PyCUDA, a Python library that leverages the power of CUDA and GPUs for accelerations and can be used by computer vision developers who use OpenCV with Python. By the end of this book, you’ll have enhanced computer vision applications with the help of this book's hands-on approach. What you will learnUnderstand how to access GPU device properties and capabilities from CUDA programsLearn how to accelerate searching and sorting algorithmsDetect shapes such as lines and circles in imagesExplore object tracking and detection with algorithmsProcess videos using different video analysis techniques in Jetson TX1Access GPU device properties from the PyCUDA programUnderstand how kernel execution worksWho this book is for This book is a go-to guide for you if you are a developer working with OpenCV and want to learn how to process more complex image data by exploiting GPU processing. A thorough understanding of computer vision concepts and programming languages such as C++ or Python is expected.
Gpu Programming With Cuda And Tensor Cores
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Author : Martin Dunagan
language : en
Publisher: Independently Published
Release Date : 2025-09-18
Gpu Programming With Cuda And Tensor Cores written by Martin Dunagan and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-18 with Computers categories.
GPU Programming with CUDA and Tensor Cores: Harness Parallel Processing for AI, Machine Learning, and High-Performance Applications Unlock the full potential of GPU computing with this hands-on guide to CUDA programming and Tensor Core acceleration. Whether you are an engineer, researcher, data scientist, or advanced developer, this book gives you the knowledge and practical techniques to build high-performance applications that scale. Inside, you'll master the foundations of CUDA programming, learn how to manage threads, warps, and memory hierarchies, and understand how to design efficient parallel algorithms. You'll also discover how Tensor Cores accelerate matrix operations and mixed precision training, enabling breakthroughs in deep learning, AI, and scientific simulation. Key topics include: Writing efficient CUDA kernels and optimizing GPU memory usage Performing reductions, scans, and parallel matrix operations Leveraging cuBLAS, cuDNN, cuFFT, and NCCL libraries for performance Implementing real-world workloads like image classification, object detection, and large-scale scientific simulations Profiling, debugging, and scaling applications across multiple GPUs Benchmarking CPU vs GPU vs Tensor Core performance for critical insights Through detailed explanations, authentic code examples, and real-world case studies, this book bridges the gap between theory and practice giving you the tools to design, implement, and optimize GPU-powered solutions across AI, machine learning, data science, and HPC. If you are serious about mastering GPU acceleration and want to stay ahead in AI and high-performance computing, this is the essential resource you need. Take the next step in your development journey, equip yourself with the skills to build faster, smarter, and more scalable applications. Get your copy today and harness the power of CUDA and Tensor Cores!
Gpu Programming In Matlab
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Author : Nikolaos Ploskas
language : en
Publisher: Morgan Kaufmann
Release Date : 2016-08-25
Gpu Programming In Matlab written by Nikolaos Ploskas and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-08-25 with Computers categories.
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
Multicore And Gpu Programming
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Author : Gerassimos Barlas
language : en
Publisher: Morgan Kaufmann
Release Date : 2022-02-09
Multicore And Gpu Programming written by Gerassimos Barlas and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-09 with Computers categories.
Multicore and GPU Programming: An Integrated Approach, Second Edition offers broad coverage of key parallel computing tools, essential for multi-core CPU programming and many-core "massively parallel" computing. Using threads, OpenMP, MPI, CUDA and other state-of-the-art tools, the book teaches the design and development of software capable of taking advantage of modern computing platforms that incorporate CPUs, GPUs and other accelerators. Presenting material refined over more than two decades of teaching parallel computing, author Gerassimos Barlas minimizes the challenge of transitioning from sequential programming to mastering parallel platforms with multiple examples, extensive case studies, and full source code. By using this book, readers will better understand how to develop programs that run over distributed memory machines using MPI, create multi-threaded applications with either libraries or directives, write optimized applications that balance the workload between available computing resources, and profile and debug programs targeting parallel machines. - Includes comprehensive coverage of all major multi-core and many-core programming tools and platforms, including threads, OpenMP, MPI, CUDA, OpenCL and Thrust - Covers the most recent versions of the above at the time of publication - Demonstrates parallel programming design patterns and examples of how different tools and paradigms can be integrated for superior performance - Updates in the second edition include the use of the C++17 standard for all sample code, a new chapter on concurrent data structures, a new chapter on OpenCL, and the latest research on load balancing - Includes downloadable source code, examples and instructor support materials on the book's companion website
Professional Cuda C Programming
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Author : John Cheng
language : en
Publisher: John Wiley & Sons
Release Date : 2014-09-08
Professional Cuda C Programming written by John Cheng and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-09-08 with Computers categories.
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.
Mastering Cuda C Programming From Fundamentals To Advanced Gpu Computing
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Author : Toby Webber
language : en
Publisher: ChiperHub LLC
Release Date : 2025-07-31
Mastering Cuda C Programming From Fundamentals To Advanced Gpu Computing written by Toby Webber and has been published by ChiperHub LLC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-31 with Computers categories.
Unleash the Power of Parallel Processing with CUDA C++: Mastering CUDA C++ Programming Dive into the world of GPU programming and accelerate your applications to new heights! Are you ready to harness the incredible power of parallel processing? Mastering CUDA C++ Programming is your comprehensive guide to unlocking the full potential of NVIDIA GPUs for general-purpose computing. Whether you're a curious beginner or a seasoned programmer, this book will equip you with the knowledge and skills to write high-performance CUDA C++ applications. What is CUDA? CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model developed by NVIDIA. It allows you to use GPUs for general-purpose processing, accelerating applications in fields like artificial intelligence, scientific computing, and high-performance data processing. Why Choose This Book? Crystal-Clear Explanations: Complex concepts are broken down into digestible chunks, with plenty of examples and analogies to aid understanding. Even if you're new to GPU programming or parallel programming, you'll grasp the fundamentals with ease. Hands-On Learning: Solidify your understanding with practical projects that guide you through building real-world CUDA applications. You'll find CUDA examples throughout the book, illustrating key concepts and techniques. Optimization Mastery: Master advanced techniques for CUDA optimization, including CUDA streams, shared memory, and dynamic parallelism, to achieve peak performance and efficiency in your code. Real-World Applications: Explore how CUDA C++ is used in diverse fields, from deep learning and machine learning to scientific computing and image processing. We'll even delve into cutting-edge applications in AI, autonomous systems, and robotics. Future-Proof Your Skills: Get a glimpse into the future of NVIDIA CUDA and its role in emerging technologies like AI and quantum computing. What You'll Learn Fundamentals of CUDA Programming: Understand the basics of CUDA programming with CUDA C++, including GPU architecture, memory management, and thread organization. CUDA Libraries: Leverage powerful CUDA libraries like cuBLAS and cuFFT to accelerate linear algebra and signal processing tasks. Streamlining Development with Thrust: Explore the Thrust library to simplify CUDA development and write more concise and efficient code. Debugging and Profiling: Master debugging techniques and profiling tools to identify and resolve issues in your CUDA code. Advanced Optimization: Dive deep into advanced optimization techniques, including memory coalescing, warp divergence, and kernel fusion. Multi-GPU Programming: Learn how to harness the power of multiple GPUs to tackle even larger and more demanding computational challenges. A Comprehensive Guide for All Levels This book serves as both a CUDA tutorial for beginners and a comprehensive CUDA guide for experienced programmers. It's your one-stop resource for mastering CUDA C++ and unlocking the incredible power of parallel processing. Don't miss out on this opportunity to accelerate your applications and become a proficient CUDA C++ programmer! Order your copy of Mastering CUDA C++ Programming today and start your journey into the world of high-performance computing!
Cuda By Example
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Author : Jason Sanders
language : en
Publisher: Addison-Wesley Professional
Release Date : 2010-07-19
Cuda By Example written by Jason Sanders and has been published by Addison-Wesley Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-19 with Computers categories.
CUDA is a computing architecture designed to facilitate the development of parallel programs. In conjunction 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. http://developer.nvidia.com/object/cuda-by-example.html