CUDA by Example

An Introduction to General-Purpose GPU Programming, Portable Documents

Author: Jason Sanders,Edward Kandrot

Publisher: Addison-Wesley Professional

ISBN: 0132180138

Category: Computers

Page: 312

View: 4015

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

Cuda by Example

An Introduction to General-purpose Gpu Programming

Author: Jason Sanders,Edward Kandrot

Publisher: Createspace Independent Publishing Platform

ISBN: 9781548845117

Category:

Page: 142

View: 8130

GPUs can be used for much more than graphics processing. As opposed to a CPU, which can only run four or five threads at once, a GPU is made up of hundreds or even thousands of individual, low-powered cores, allowing it to perform thousands of concurrent operations. Because of this, GPUs can tackle large, complex problems on a much shorter time scale than CPUs. Dive into parallel programming on NVIDIA hardware with CUDA by Chris Rose, and learn the basics of unlocking your graphics card. This updated and expanded second edition of Book provides a user-friendly introduction to the subject, Taking a clear structural framework, it guides the reader through the subject's core elements. A flowing writing style combines with the use of illustrations and diagrams throughout the text to ensure the reader understands even the most complex of concepts. This succinct and enlightening overview is a required reading for all those interested in the subject . We hope you find this book useful in shaping your future career & Business.

CUDA by Example

An Introduction to General-purpose GPU Programming

Author: Jason Sanders,Edward Kandrot

Publisher: Addison-Wesley Professional

ISBN: 9780131387683

Category: Computers

Page: 290

View: 2749

The complete guide to developing high-performance applications with CUDA - written by CUDA development team members, and supported by NVIDIA * *Breakthrough techniques for using the power of graphics processors to create highperformance general purpose applications. *Packed with realistic, C-based examples -- from basic to advanced. *Covers one of today's most highly-anticipated new technologies for software development wherever performance is crucial: finance, design automation, science, simulation, graphics, and beyond. NVIDIA graphics processors have immense computational power. With NVIDIA's breakthrough CUDA software platform, that power can be put to work in virtually any type of software development that requires exceptionally high performance, from finance to physics. Now, for the first time, two of NVIDIA's senior CUDA developers thoroughly introduce the platform, and show developers exactly how to make the most of it. CUDA C by Example is the first book on CUDA development for professional programmers - and the only book created with NVIDIA's direct involvement. Concise and practical, it focuses on presenting proven techniques and concrete example code for building high-performance parallelized CUDA programs with C. Programmers familiar with C will need no other skills or experience to get started - making high-performance programming more accessible than it's ever been before.

CUDA Programming

A Developer's Guide to Parallel Computing with GPUs

Author: Shane Cook

Publisher: Newnes

ISBN: 0124159338

Category: Computers

Page: 576

View: 3651

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 new hardware, and solving common problems. Comprehensive introduction to parallel programming with CUDA, for readers new to both Detailed instructions help readers optimize the CUDA software development kit Practical techniques illustrate working with memory, threads, algorithms, resources, and more Covers CUDA on multiple hardware platforms: Mac, Linux and Windows with several NVIDIA chipsets Each chapter includes exercises to test reader knowledge

The CUDA Handbook

A Comprehensive Guide to GPU Programming

Author: Nicholas Wilt

Publisher: Addison-Wesley

ISBN: 0133261506

Category: Computers

Page: 528

View: 5400

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 well as the guidance on how best to structure CPU/GPU data interchange and synchronization. The accompanying open source code–more than 25,000 lines of it, freely available at www.cudahandbook.com–is specifically intended to be reused and repurposed by developers. Designed to be both a comprehensive reference and a practical cookbook, the text is divided into the following three parts: Part I, Overview, gives high-level descriptions of the hardware and software that make CUDA possible. Part II, Details, provides thorough descriptions of every aspect of CUDA, including Memory Streams and events Models of execution, including the dynamic parallelism feature, new with CUDA 5.0 and SM 3.5 The streaming multiprocessors, including descriptions of all features through SM 3.5 Programming multiple GPUs Texturing The source code accompanying Part II is presented as reusable microbenchmarks and microdemos, designed to expose specific hardware characteristics or highlight specific use cases. Part III, Select Applications, details specific families of CUDA applications and key parallel algorithms, including Streaming workloads Reduction Parallel prefix sum (Scan) N-body Image Processing These algorithms cover the full range of potential CUDA applications.

CUDA Application Design and Development

Author: Rob Farber

Publisher: Elsevier

ISBN: 0123884268

Category: Computers

Page: 315

View: 4656

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.

An Introduction to Parallel Programming

Author: Peter Pacheco

Publisher: Elsevier

ISBN: 9780080921440

Category: Computers

Page: 392

View: 5706

An Introduction to Parallel Programming is the first undergraduate text to directly address compiling and running parallel programs on the new multi-core and cluster architecture. It explains how to design, debug, and evaluate the performance of distributed and shared-memory programs. The author Peter Pacheco uses a tutorial approach to show students how to develop effective parallel programs with MPI, Pthreads, and OpenMP, starting with small programming examples and building progressively to more challenging ones. The text is written for students in undergraduate parallel programming or parallel computing courses designed for the computer science major or as a service course to other departments; professionals with no background in parallel computing. Takes a tutorial approach, starting with small programming examples and building progressively to more challenging examples Focuses on designing, debugging and evaluating the performance of distributed and shared-memory programs Explains how to develop parallel programs using MPI, Pthreads, and OpenMP programming models

Professional CUDA C Programming

Author: John Cheng,Max Grossman,Ty McKercher

Publisher: John Wiley & Sons

ISBN: 1118739329

Category: Computers

Page: 528

View: 6578

Programming Massively Parallel Processors

A Hands-on Approach

Author: David B. Kirk,Wen-mei W. Hwu

Publisher: Newnes

ISBN: 0123914183

Category: Computers

Page: 514

View: 1848

Programming Massively Parallel Processors: A Hands-on Approach, Second Edition, teaches students how to program massively parallel processors. It offers a detailed discussion of various techniques for constructing parallel programs. Case studies are used to demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This guide shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. This revised edition contains more parallel programming examples, commonly-used libraries such as Thrust, and explanations of the latest tools. It also provides new coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more; increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism; and two new case studies (on MRI reconstruction and molecular visualization) that explore the latest applications of CUDA and GPUs for scientific research and high-performance computing. This book should be a valuable resource for advanced students, software engineers, programmers, and hardware engineers. New coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more Increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism Two new case studies (on MRI reconstruction and molecular visualization) explore the latest applications of CUDA and GPUs for scientific research and high-performance computing

Cuda for Engineers

An Introduction to High-performance Parallel Computing

Author: Duane Storti,Mete Yurtoglu

Publisher: Createspace Independent Publishing Platform

ISBN: 9781548588014

Category:

Page: 142

View: 6725

GPUs can be used for much more than graphics processing. As opposed to a CPU, which can only run four or five threads at once, a GPU is made up of hundreds or even thousands of individual, low-powered cores, allowing it to perform thousands of concurrent operations. Because of this, GPUs can tackle large, complex problems on a much shorter time scale than CPUs. Dive into parallel programming on NVIDIA hardware with CUDA by Chris Rose, and learn the basics of unlocking your graphics card. This updated and expanded second edition of Book provides a user-friendly introduction to the subject, Taking a clear structural framework, it guides the reader through the subject's core elements. A flowing writing style combines with the use of illustrations and diagrams throughout the text to ensure the reader understands even the most complex of concepts. This succinct and enlightening overview is a required reading for all those interested in the subject . We hope you find this book useful in shaping your future career & Business.

Heterogeneous Computing with OpenCL

Revised OpenCL 1.2 Edition

Author: Benedict Gaster,Lee Howes,David R. Kaeli,Perhaad Mistry,Dana Schaa

Publisher: Newnes

ISBN: 0124055206

Category: Computers

Page: 308

View: 9975

Heterogeneous Computing with OpenCL, Second Edition teaches OpenCL and parallel programming for complex systems that may include a variety of device architectures: multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units (APUs) such as AMD Fusion technology. It is the first textbook that presents OpenCL programming appropriate for the classroom and is intended to support a parallel programming course. Students will come away from this text with hands-on experience and significant knowledge of the syntax and use of OpenCL to address a range of fundamental parallel algorithms. Designed to work on multiple platforms and with wide industry support, OpenCL will help you more effectively program for a heterogeneous future. Written by leaders in the parallel computing and OpenCL communities, Heterogeneous Computing with OpenCL explores memory spaces, optimization techniques, graphics interoperability, extensions, and debugging and profiling. It includes detailed examples throughout, plus additional online exercises and other supporting materials that can be downloaded at http://www.heterogeneouscompute.org/?page_id=7 This book will appeal to software engineers, programmers, hardware engineers, and students/advanced students. Explains principles and strategies to learn parallel programming with OpenCL, from understanding the four abstraction models to thoroughly testing and debugging complete applications. Covers image processing, web plugins, particle simulations, video editing, performance optimization, and more. Shows how OpenCL maps to an example target architecture and explains some of the tradeoffs associated with mapping to various architectures Addresses a range of fundamental programming techniques, with multiple examples and case studies that demonstrate OpenCL extensions for a variety of hardware platforms

Multicore and Gpu Programming

An Integrated Approach

Author: Gerassimos Barlas

Publisher: Morgan Kaufmann

ISBN: 9780124171374

Category: Computers

Page: 698

View: 1534

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 with multiple examples, extensive case studies, and full source code. Using this book, you can 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 multicore machines. Comprehensive coverage of all major multicore programming tools, including threads, OpenMP, MPI, and CUDA Demonstrates parallel programming design patterns and examples of how different tools and paradigms can be integrated for superior performance Particular focus on the emerging area of divisible load theory and its impact on load balancing and distributed systems Download source code, examples, and instructor support materials on the book's companion website

OpenCL Programming Guide

Author: Aaftab Munshi,Benedict Gaster,Timothy G. Mattson

Publisher: Addison-Wesley Professional

ISBN: 9780321749642

Category: Computers

Page: 603

View: 4246

The definitive reference and programming guide for OpenCL, the new parallel programming standard for high performance and cloud environments. • •Will catch a growing wave of interest in OpenCL, following its implementations by Apple, NVIDIA, and ATI. •Designed specifically for developers: shows how to write parallel OpenCL software that takes advantage of all available processors (CPUs, GPUs, accelerators, and more) •OpenCL insiders present its API, programming language, development best practices, and many practical examples. The new OpenCL (Open Computing Language) standard and framework will make it possible to write applications that access all the programming resources available to them, including CPUs, GPUs, accelerators such as IBM's CELL, and even external processors. Already implemented by Apple (in Snow Leopard), ATI, and NVIDIA, OpenCL will be a solution for personal computers, servers, handheld/embedded devices, High Performance Computing applications, and even cloud systems. However, until now, developers who want to learn OpenCL have been required to read a specification designed for hardware and software implementers, not programmers. OpenCL Programming Guide is the first comprehensive, authoritative guide to OpenCL specifically designed for working developers. Written by leaders in the OpenCL community, this book thoroughly explains how each element of OpenCL 1.1 works, and offers complete reference material on the OpenCL API and programming language. The authors review key use cases, demonstrate how to write performant OpenCL code, and introduce essential algorithms for developers. They also present many examples, performance tips, case studies, and problems designed to help developers rapidly become effective, whatever their application or computing environment.

CUDA Fortran for Scientists and Engineers

Best Practices for Efficient CUDA Fortran Programming

Author: Gregory Ruetsch,Massimiliano Fatica

Publisher: Morgan Kaufmann

ISBN: 9780124169708

Category: Computers

Page: 323

View: 6064

CUDA Fortran for Scientists and Engineers shows how high-performance application developers can leverage the power of GPUs using Fortran, the familiar language of scientific computing and supercomputer performance benchmarking. The authors presume no prior parallel computing experience, and cover the basics along with best practices for efficient GPU computing using CUDA Fortran. To help you add CUDA Fortran to existing Fortran codes, the book explains how to understand the target GPU architecture, identify computationally intensive parts of the code, and modify the code to manage the data and parallelism and optimize performance. All of this is done in Fortran, without having to rewrite in another language. Each concept is illustrated with actual examples so you can immediately evaluate the performance of your code in comparison. Leverage the power of GPU computing with PGI's CUDA Fortran compiler Gain insights from members of the CUDA Fortran language development team Includes multi-GPU programming in CUDA Fortran, covering both peer-to-peer and message passing interface (MPI) approaches Includes full source code for all the examples and several case studies Download source code and slides from the book's companion website

GPU Computing Gems

Author: Wen-mei W. Hwu

Publisher: Elsevier

ISBN: 0123859638

Category: Computers

Page: 541

View: 1025

"Since the introduction of CUDA in 2007, more than 100 million computers with CUDA capable GPUs have been shipped to end users. GPU computing application developers can now expect their application to have a mass market. With the introduction of OpenCL in 2010, researchers can now expect to develop GPU applications that can run on hardware from multiple vendors"--

Head First HTML5 Programming

Building Web Apps with JavaScript

Author: Eric Freeman,Elisabeth Robson

Publisher: "O'Reilly Media, Inc."

ISBN: 1449331513

Category: Computers

Page: 610

View: 5167

HTML has been on a wild ride. Sure, HTML started as a mere markup language, but more recently HTML’s put on some major muscle. Now we’ve got a language tuned for building web applications with Web storage, 2D drawing, offline support, sockets and threads, and more. And to speak this language you’ve got to go beyond HTML5 markup and into the world of the DOM, events, and JavaScript APIs. Now you probably already know all about HTML markup (otherwise known as structure) and you know all aboutCSS style (presentation), but what you’ve been missing is JavaScript (behavior). If all you know about are structure and presentation, you can create some great looking pages, but they’re still just pages. When you add behavior with JavaScript, you can create an interactive experience; even better, you can create full blown web applications. Head First HTML5 Programming is your ultimate tour guide to creating web applications with HTML5 and JavaScript, and we give you everything you need to know to build them, including: how to add interactivity to your pages, how to communicate with the world of Web services, and how to use the great new APIs being developed for HTML5. Here are just some of the things you’ll learn in Head First HTML5 Programing: Learn how to make your pages truly interactive by using the power of the DOM. Finally understand how JavaScript works and take yourself from novice to well-informed in just a few chapters. Learn how JavaScript APIs fit into the HTML5 ecosystem, and how to use any API in your web pages. Use the Geolocation API to know where your users are. Bring out your inner artist with Canvas, HTML5’s new 2D drawing surface. Go beyond just plugging a video into your pages, and create custom video experiences. Learn the secret to grabbing five megabytes of storage in every user’s browser. Improve your page’s responsiveness and performance with Web workers. And much more.

GPU Parallel Program Development Using CUDA

Author: Tolga Soyata

Publisher: CRC Press

ISBN: 1498750761

Category: Mathematics

Page: 440

View: 2801

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 using CPU multi-threading in Part I. A few simple programs are used to demonstrate the concept of dividing a large task into multiple parallel sub-tasks and mapping them to CPU threads. Multiple ways of parallelizing the same task are analyzed and their pros/cons are studied in terms of both core and memory operation. Part II of the book introduces GPU massive parallelism. The same programs are parallelized on multiple Nvidia GPU platforms and the same performance analysis is repeated. Because the core and memory structures of CPUs and GPUs are different, the results differ in interesting ways. The end goal is to make programmers aware of all the good ideas, as well as the bad ideas, so readers can apply the good ideas and avoid the bad ideas in their own programs. Part III of the book provides pointer for readers who want to expand their horizons. It provides a brief introduction to popular CUDA libraries (such as cuBLAS, cuFFT, NPP, and Thrust),the OpenCL programming language, an overview of GPU programming using other programming languages and API libraries (such as Python, OpenCV, OpenGL, and Apple’s Swift and Metal,) and the deep learning library cuDNN.

Accelerating MATLAB with GPU Computing

A Primer with Examples

Author: Jung W. Suh,Youngmin Kim

Publisher: Newnes

ISBN: 0124079164

Category: Computers

Page: 258

View: 903

Beyond simulation and algorithm development, many developers increasingly use MATLAB even for product deployment in computationally heavy fields. This often demands that MATLAB codes run faster by leveraging the distributed parallelism of Graphics Processing Units (GPUs). While MATLAB successfully provides high-level functions as a simulation tool for rapid prototyping, the underlying details and knowledge needed for utilizing GPUs make MATLAB users hesitate to step into it. Accelerating MATLAB with GPUs offers a primer on bridging this gap. Starting with the basics, setting up MATLAB for CUDA (in Windows, Linux and Mac OS X) and profiling, it then guides users through advanced topics such as CUDA libraries. The authors share their experience developing algorithms using MATLAB, C++ and GPUs for huge datasets, modifying MATLAB codes to better utilize the computational power of GPUs, and integrating them into commercial software products. Throughout the book, they demonstrate many example codes that can be used as templates of C-MEX and CUDA codes for readers’ projects. Download example codes from the publisher's website: http://booksite.elsevier.com/9780124080805/ Shows how to accelerate MATLAB codes through the GPU for parallel processing, with minimal hardware knowledge Explains the related background on hardware, architecture and programming for ease of use Provides simple worked examples of MATLAB and CUDA C codes as well as templates that can be reused in real-world projects

Programming Game AI by Example

Author: Mat Buckland

Publisher: Jones & Bartlett Learning

ISBN: 9781556220784

Category: Computers

Page: 495

View: 7145

Provides an introduction to AI game techniques used in game programming.

C++ AMP

Author: Ade Miller,Kate Gregory

Publisher: Pearson Education

ISBN: 0735668191

Category: Computers

Page: 356

View: 719

Capitalize on the faster GPU processors in today’s computers with the C++ AMP code library—and bring massive parallelism to your project. With this practical book, experienced C++ developers will learn parallel programming fundamentals with C++ AMP through detailed examples, code snippets, and case studies. Learn the advantages of parallelism and get best practices for harnessing this technology in your applications. Discover how to: Gain greater code performance using graphics processing units (GPUs) Choose accelerators that enable you to write code for GPUs Apply thread tiles, tile barriers, and tile static memory Debug C++ AMP code with Microsoft Visual Studio Use profiling tools to track the performance of your code