CUDA Application Design and Development

Machine generated contents note: 1. How to think in CUDA 2. Tools to build, debug and profile 3. The GPU performance envelope 4. The CUDA memory subsystems 5. Exploiting the CUDA execution grid 6. MultiGPU applications and scaling 7. Numerical CUDA, libraries and high-level language bindings 8. Mixing CUDA with rendering 9. High Performance Machine Learning 10. Scientific Visualization 11. Multimedia with OpenCV 12. Ultra Low-power Devices: Tegra.

Produk Detail:

  • Author : Rob Farber
  • Publisher : Elsevier
  • Pages : 315 pages
  • ISBN : 0123884268
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKCUDA Application Design and Development

CUDA Application Design and Development

CUDA Application Design and Development
  • Author : Rob Farber
  • Publisher : Elsevier
  • Release : 18 May 2021
GET THIS BOOKCUDA Application Design and Development

Machine generated contents note: 1. How to think in CUDA 2. Tools to build, debug and profile 3. The GPU performance envelope 4. The CUDA memory subsystems 5. Exploiting the CUDA execution grid 6. MultiGPU applications and scaling 7. Numerical CUDA, libraries and high-level language bindings 8. Mixing CUDA with rendering 9. High Performance Machine Learning 10. Scientific Visualization 11. Multimedia with OpenCV 12. Ultra Low-power Devices: Tegra.

CUDA Programming

CUDA Programming
  • Author : Shane Cook
  • Publisher : Newnes
  • Release : 18 May 2021
GET THIS BOOKCUDA Programming

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 into CUDA installation. Chapters on core concepts including threads, blocks, grids, and memory focus on both parallel and CUDA-specific issues. Later, the book demonstrates CUDA in practice for optimizing applications, adjusting to

CUDA by Example

CUDA by Example
  • Author : Jason Sanders,Edward Kandrot
  • Publisher : Addison-Wesley Professional
  • Release : 19 July 2010
GET THIS BOOKCUDA by Example

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

Designing Scientific Applications on GPUs

Designing Scientific Applications on GPUs
  • Author : Raphael Couturier
  • Publisher : CRC Press
  • Release : 21 November 2013
GET THIS BOOKDesigning Scientific Applications on GPUs

Many of today’s complex scientific applications now require a vast amount of computational power. General purpose graphics processing units (GPGPUs) enable researchers in a variety of fields to benefit from the computational power of all the cores available inside graphics cards. Understand the Benefits of Using GPUs for Many Scientific Applications Designing Scientific Applications on GPUs shows you how to use GPUs for applications in diverse scientific fields, from physics and mathematics to computer science. The book explains the

GPU Parallel Program Development Using CUDA

GPU Parallel Program Development Using CUDA
  • Author : Tolga Soyata
  • Publisher : CRC Press
  • Release : 19 January 2018
GET THIS BOOKGPU Parallel Program Development Using CUDA

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 platform-specific. At the same time, the book also provides platform-dependent explanations that are as valuable as generalized GPU concepts. The book consists of three separate parts; it starts by explaining parallelism

Multicore and GPU Programming

Multicore and GPU Programming
  • Author : Gerassimos Barlas
  • Publisher : Elsevier
  • Release : 16 December 2014
GET THIS BOOKMulticore and GPU Programming

Multicore and GPU Programming offers broad coverage of the key parallel computing skillsets: multicore CPU programming and manycore "massively parallel" computing. Using threads, OpenMP, MPI, and CUDA, it teaches the design and development of software capable of taking advantage of today’s computing platforms incorporating CPU and GPU hardware and explains how to transition from sequential programming to a parallel computing paradigm. Presenting material refined over more than a decade of teaching parallel computing, author Gerassimos Barlas minimizes the challenge

The CUDA Handbook

The CUDA Handbook
  • Author : Nicholas Wilt
  • Publisher : Addison-Wesley
  • Release : 11 June 2013
GET THIS BOOKThe CUDA Handbook

The CUDA Handbook begins where CUDA by Example (Addison-Wesley, 2011) leaves off, discussing CUDA hardware and software in greater detail and covering both CUDA 5.0 and Kepler. Every CUDA developer, from the casual to the most sophisticated, will find something here of interest and immediate usefulness. Newer CUDA developers will see how the hardware processes commands and how the driver checks progress; more experienced CUDA developers will appreciate the expert coverage of topics such as the driver API and context migration, as

Parallel Programming with OpenACC

Parallel Programming with OpenACC
  • Author : Rob Farber
  • Publisher : Newnes
  • Release : 14 October 2016
GET THIS BOOKParallel Programming with OpenACC

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, approach to accelerators without significant programming effort. Author Rob Farber, working with a team of expert contributors, demonstrates how to turn existing applications into portable GPU accelerated programs that demonstrate immediate speedups. The book

Professional CUDA C Programming

Professional CUDA C Programming
  • Author : John Cheng,Max Grossman,Ty McKercher
  • Publisher : John Wiley & Sons
  • Release : 09 September 2014
GET THIS BOOKProfessional CUDA C Programming

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

Hands-On GPU Programming with Python and CUDA

Hands-On GPU Programming with Python and CUDA
  • Author : Dr. Brian Tuomanen
  • Publisher : Packt Publishing Ltd
  • Release : 27 November 2018
GET THIS BOOKHands-On GPU Programming with Python and CUDA

Build real-world applications by writing effective GPU code, CUDA kernels, and device functions with the latest features of Python 3.7, CUDA 9 and CUDA 10 Key Features Expand your background in GPU programming—PyCUDA, scikit-cuda, and Nsight Effectively use CUDA libraries such as cuBLAS, cuFFT, and cuSolver Apply GPU programming to modern data science applications Book 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

Programming Massively Parallel Processors

Programming Massively Parallel Processors
  • Author : David B. Kirk,Wen-mei W. Hwu
  • Publisher : Elsevier
  • Release : 22 February 2010
GET THIS BOOKProgramming Massively Parallel Processors

Programming Massively Parallel Processors discusses 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, which begins with computational thinking and ends with effective and efficient parallel programs. This book describes computational thinking techniques that will enable students to think about problems in ways that are amenable to high-performance parallel computing. It utilizes CUDA (Compute Unified Device Architecture), NVIDIA's software development tool created specifically for

Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

Hands-On GPU-Accelerated Computer Vision with OpenCV and CUDA
  • Author : Bhaumik Vaidya
  • Publisher : Packt Publishing Ltd
  • Release : 26 September 2018
GET THIS BOOKHands-On GPU-Accelerated Computer Vision with OpenCV and CUDA

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 Features Explore examples to leverage the GPU processing power with OpenCV and CUDA Enhance the performance of algorithms on embedded hardware platforms Discover C++ and Python libraries for GPU acceleration Book Description Computer vision has been revolutionizing a wide range of industries, and OpenCV is the most widely chosen tool for computer vision with

CUDA for Engineers

CUDA for Engineers
  • Author : Duane Storti,Mete Yurtoglu
  • Publisher : Addison-Wesley Professional
  • Release : 02 November 2015
GET THIS BOOKCUDA for Engineers

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 running sample programs to building your own code. Throughout, you’ll learn from complete examples you can build, run, and modify, complemented by additional projects that deepen your understanding. All projects

GPU Computing Gems Emerald Edition

GPU Computing Gems Emerald Edition
  • Author : Anonim
  • Publisher : Elsevier
  • Release : 13 January 2011
GET THIS BOOKGPU Computing Gems Emerald Edition

GPU Computing Gems Emerald Edition offers practical techniques in parallel computing using graphics processing units (GPUs) to enhance scientific research. The first volume in Morgan Kaufmann's Applications of GPU Computing Series, this book offers the latest insights and research in computer vision, electronic design automation, and emerging data-intensive applications. It also covers life sciences, medical imaging, ray tracing and rendering, scientific simulation, signal and audio processing, statistical modeling, video and image processing. This book is intended to help those who

Heterogeneous Computing Architectures

Heterogeneous Computing Architectures
  • Author : Olivier Terzo,Karim Djemame,Alberto Scionti,Clara Pezuela
  • Publisher : CRC Press
  • Release : 10 September 2019
GET THIS BOOKHeterogeneous Computing Architectures

Heterogeneous Computing Architectures: Challenges and Vision provides an updated vision of the state-of-the-art of heterogeneous computing systems, covering all the aspects related to their design: from the architecture and programming models to hardware/software integration and orchestration to real-time and security requirements. The transitions from multicore processors, GPU computing, and Cloud computing are not separate trends, but aspects of a single trend-mainstream; computers from desktop to smartphones are being permanently transformed into heterogeneous supercomputer clusters. The reader will get an