Hands On Gpu Programming With Cuda C And Python 3

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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 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.

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.

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.

Hands On Gpu Programming With Cuda

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Author : Jaegeun Han
language : en
Publisher:
Release Date : 2019-09-27

Hands On Gpu Programming With Cuda written by Jaegeun Han and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-27 with Computers categories.

Explore different GPU programming methods using libraries and directives, such as OpenACC, with extension to languages such as C, C++, and Python Key Features Learn parallel programming principles and practices and performance analysis in GPU computing Get to grips with distributed multi GPU programming and other approaches to GPU programming Understand how GPU acceleration in deep learning models can improve their performance Book Description Compute Unified Device Architecture (CUDA) is NVIDIA's GPU computing platform and application programming interface. It's designed to work with programming languages such as C, C++, and Python. With CUDA, you can leverage a GPU's parallel computing power for a range of high-performance computing applications in the fields of science, healthcare, and deep learning. Learn CUDA Programming will help you learn GPU parallel programming and understand its modern applications. In this book, you'll discover CUDA programming approaches for modern GPU architectures. You'll not only be guided through GPU features, tools, and APIs, you'll also learn how to analyze performance with sample parallel programming algorithms. This book will help you optimize the performance of your apps by giving insights into CUDA programming platforms with various libraries, compiler directives (OpenACC), and other languages. As you progress, you'll learn how additional computing power can be generated using multiple GPUs in a box or in multiple boxes. Finally, you'll explore how CUDA accelerates deep learning algorithms, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). By the end of this CUDA book, you'll be equipped with the skills you need to integrate the power of GPU computing in your applications. What you will learn Understand general GPU operations and programming patterns in CUDA Uncover the difference between GPU programming and CPU programming Analyze GPU application performance and implement optimization strategies Explore GPU programming, profiling, and debugging tools Grasp parallel programming algorithms and how to implement them Scale GPU-accelerated applications with multi-GPU and multi-nodes Delve into GPU programming platforms with accelerated libraries, Python, and OpenACC Gain insights into deep learning accelerators in CNNs and RNNs using GPUs Who this book is for This beginner-level book is for programmers who want to delve into parallel computing, become part of the high-performance computing community and build modern applications. Basic C and C++ programming experience is assumed. For deep learning enthusiasts, this book covers Python InterOps, DL libraries, and practical examples on performance estimation.

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

Practical Gpu Programming

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Author : Maris Fenlor
language : en
Publisher: GitforGits
Release Date : 2025-02-20

Practical Gpu Programming written by Maris Fenlor and has been published by GitforGits this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Computers categories.

If you're a Python pro looking to get the most out of your code with GPUs, then Practical GPU Programming is the right book for you. This book will walk you through the basics of GPU architectures, show you hands-on parallel programming techniques, and give you the know-how to confidently speed up real workloads in data processing, analytics, and engineering. The first thing you'll do is set up the environment, install CUDA, and get a handle on using Python libraries like PyCUDA and CuPy. You'll then dive into memory management, kernel execution, and parallel patterns like reductions and histogram computations. Then, we'll dive into sorting and search techniques, but with a focus on how GPU acceleration transforms business data processing. We'll also put a strong emphasis on linear algebra to show you how to supercharge classic vector and matrix operations with cuBLAS and CuPy. Plus, with batched computations, efficient broadcasting, custom kernels, and mixed-library workflows, you can tackle both standard and advanced problems with ease. Throughout, we evaluate numerical accuracy and performance side by side, so you can understand both the strengths and limitations of GPU-based solutions. The book covers nearly every essential skill and modern toolkit for practical GPU programming, but it's not going to turn you into a master overnight. Key Learnings Boost processing speed and efficiency for data-intensive tasks. Use CuPy and PyCUDA to write and execute custom CUDA kernels. Maximize GPU occupancy and throughput efficiency by using optimal thread block and grid configuration. Reduce global memory bottlenecks in kernels by using shared memory and coalesced access patterns. Perform dynamic kernel compilation to ensure tailored performance. Use CuPy to carry out custom, high-speed elementwise GPU operations and expressions. Implement bitonic and radix sort algorithms for large or batch integer datasets. Execute parallel linear search kernels to detect patterns rapidly. Scale matrix operations using Batched GEMM and high-level cuBLAS routines. Table of Content Introduction to GPU Fundamentals Setting up GPU Programming Environment Basic Data Transfers and Memory Types Simple Parallel Patterns Introduction to Kernel Optimization Working with PyCUDA and CuPy Features Practical Sorting and Search Linear Algebra Essentials on GPU

Mastering Gpu Programming With C And Cuda

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Author : Ethan Cole
language : en
Publisher: Independently Published
Release Date : 2025-11-26

Mastering Gpu Programming With C And Cuda written by Ethan Cole and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-11-26 with Computers categories.

Master the full power of GPU acceleration and take your C++ applications to performance levels that CPUs alone simply can't reach. Mastering GPU Programming with C++ and CUDA is a practical, modern, and results-driven guide for developers who want to build lightning-fast parallel applications, optimize CUDA workloads, and even deliver GPU-powered libraries that can be used directly from Python. Whether you're accelerating simulations, processing massive datasets, building real-time applications, or exploring high-performance computing, this book shows you exactly how to harness NVIDIA GPUs with confidence-and produce software that truly scales. Unlock the Real Performance Potential of GPUs Starting from the core principles of parallelism and GPU architecture, you'll learn how to think like a GPU programmer. Step-by-step, you'll write your first kernels, optimize memory usage, scale across multiple GPUs, and leverage advanced features like CUDA streams and asynchronous execution. Build Skills That Translate to Real-World Impact Through clear explanations, hands-on examples, and professional optimization techniques, you will learn how to: Develop high-performance parallel applications in C++ and CUDA Optimize kernels, memory transfers, and GPU resource usage Analyze and eliminate bottlenecks with profiling tools Scale workloads using multi-GPU and distributed models Package GPU-powered code into reusable C++ libraries Expose CUDA libraries to Python using PyBind11 for broader adoption Go Beyond Tutorials-Build Production-Ready Solutions This isn't just a collection of code snippets. You'll explore real engineering practices: Modern C++ patterns for safer GPU resource management API design and modular GPU library architecture Deployment strategies for research, enterprise, and cloud environments Best practices for maintainability, testing, and cross-platform builds Expand Your Reach with Python Integration With Python now dominating AI, data science, and research, the book teaches you how to connect your GPU-accelerated C++ code to Python-turning your work into reusable, scalable, and widely adoptable tools. Who This Book Is For C++ developers breaking into GPU computing Engineers and researchers building high-performance applications Professionals working in AI, simulation, finance, analytics, robotics, gaming, and scientific computing Anyone who wants to unlock more speed, scalability, and efficiency from their software No prior CUDA experience is required just solid C++ fundamentals and the desire to push performance boundaries. Why This Book Stands Out Unlike generic CUDA resources, this book: Covers fundamentals and advanced optimization Bridges C++ and Python for maximum versatility Focuses on reusable, production-grade techniques Prepares you for the fast-evolving GPU landscape, cloud platforms, and next-gen hardware

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!