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Author: Jon Peddie
Publisher: Springer Nature
ISBN: 3031135814
Category : Computers
Languages : en
Pages : 355
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Book Description
This is the second book in a three-part series that traces the development of the GPU, which is defined as a single chip with an integrated transform and lighting (T&L) capability. This feature previously was found in workstations as a stand-alone chip that only performed geometry functions. Enabled by Moore’s law, the first era of GPUs began in the late 1990s. Silicon Graphics (SGI) introduced T&L first in 1996 with the Nintendo 64 chipset with integrated T&L but didn’t follow through. ArtX developed a chipset with integrated T&L but didn’t bring it to market until November 1999. The need to integrate the transform and lighting functions in the graphics controller was well understood and strongly desired by dozens of companies. Nvidia was the first to produce a PC consumer level single chip with T&L in October 1999. All in all, fifteen companies came close, they had designs and experience, but one thing or another got in their way to prevent them succeeding. All the forces and technology were converging; the GPU was ready to emerge. Several of the companies involved did produce an integrated GPU, but not until early 2000. This is the account of those companies, the GPU and the environment needed to support it. The GPU has become ubiquitous and can be found in every platform that involves a computer and a user interface.
Author: Jon Peddie
Publisher: Springer Nature
ISBN: 3031135814
Category : Computers
Languages : en
Pages : 355
Get Book Here
Book Description
This is the second book in a three-part series that traces the development of the GPU, which is defined as a single chip with an integrated transform and lighting (T&L) capability. This feature previously was found in workstations as a stand-alone chip that only performed geometry functions. Enabled by Moore’s law, the first era of GPUs began in the late 1990s. Silicon Graphics (SGI) introduced T&L first in 1996 with the Nintendo 64 chipset with integrated T&L but didn’t follow through. ArtX developed a chipset with integrated T&L but didn’t bring it to market until November 1999. The need to integrate the transform and lighting functions in the graphics controller was well understood and strongly desired by dozens of companies. Nvidia was the first to produce a PC consumer level single chip with T&L in October 1999. All in all, fifteen companies came close, they had designs and experience, but one thing or another got in their way to prevent them succeeding. All the forces and technology were converging; the GPU was ready to emerge. Several of the companies involved did produce an integrated GPU, but not until early 2000. This is the account of those companies, the GPU and the environment needed to support it. The GPU has become ubiquitous and can be found in every platform that involves a computer and a user interface.
Author: Jon Peddie
Publisher: Springer Nature
ISBN: 3031109686
Category : Computers
Languages : en
Pages : 424
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Book Description
This is the first book in a three-part series that traces the development of the GPU. Initially developed for games the GPU can now be found in cars, supercomputers, watches, game consoles and more. GPU concepts go back to the 1970s when computer graphics was developed for computer-aided design of automobiles and airplanes. Early computer graphics systems were adopted by the film industry and simulators for airplanes and high energy physics—exploding nuclear bombs in computers instead of the atmosphere. A GPU has an integrated transform and lighting engine, but these were not available until the end of the 1990s. Heroic and historic companies expanded the development and capabilities of the graphics controller in pursuit of the ultimate device, a fully integrated self-contained GPU. Fifteen companies worked on building the first fully integrated GPU, some succeeded in the console, and Northbridge segments, and Nvidia was the first to offer a fully integrated GPU for the PC. Today the GPU can be found in every platform that involves a computer and a user interface.
Author: Jon Peddie
Publisher: Springer Nature
ISBN: 3031140478
Category : Computers
Languages : en
Pages : 434
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Book Description
This third book in the three-part series on the History of the GPU covers the second to sixth eras of the GPU, which can be found in anything that has a display or screen. The GPU is now part of supercomputers, PCs, Smartphones and tablets, wearables, game consoles and handhelds, TVs, and every type of vehicle including boats and planes. In the early 2000s the number of GPU suppliers consolidated to three whereas now, the number has expanded to almost 20. In 2022 the GPU market was worth over $250 billion with over 2.2 billion GPUs being sold just in PCs, and more than 10 billion in smartphones. Understanding the power and history of these devices is not only a fascinating tale, but one that will aid your understanding of some of the developments in consumer electronics, computers, new automobiles, and your fitness watch.
Author: Fernando Moral-Andrés
Publisher: Springer Nature
ISBN: 3031576756
Category :
Languages : en
Pages : 481
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Book Description
Author: Raphael Couturier
Publisher: CRC Press
ISBN: 1466571624
Category : Mathematics
Languages : en
Pages : 500
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Book Description
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 methods necessary for designing or porting your scientific application on GPUs. It will improve your knowledge about image processing, numerical applications, methodology to design efficient applications, optimization methods, and much more. Everything You Need to Design/Port Your Scientific Application on GPUs The first part of the book introduces the GPUs and Nvidia’s CUDA programming model, currently the most widespread environment for designing GPU applications. The second part focuses on significant image processing applications on GPUs. The third part presents general methodologies for software development on GPUs and the fourth part describes the use of GPUs for addressing several optimization problems. The fifth part covers many numerical applications, including obstacle problems, fluid simulation, and atomic physics models. The last part illustrates agent-based simulations, pseudorandom number generation, and the solution of large sparse linear systems for integer factorization. Some of the codes presented in the book are available online.
Author: Jon Peddie
Publisher: Springer Science & Business Media
ISBN: 1447149327
Category : Computers
Languages : en
Pages : 485
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Book Description
If you have ever looked at a fantastic adventure or science fiction movie, or an amazingly complex and rich computer game, or a TV commercial where cars or gas pumps or biscuits behaved liked people and wondered, “How do they do that?”, then you’ve experienced the magic of 3D worlds generated by a computer. 3D in computers began as a way to represent automotive designs and illustrate the construction of molecules. 3D graphics use evolved to visualizations of simulated data and artistic representations of imaginary worlds. In order to overcome the processing limitations of the computer, graphics had to exploit the characteristics of the eye and brain, and develop visual tricks to simulate realism. The goal is to create graphics images that will overcome the visual cues that cause disbelief and tell the viewer this is not real. Thousands of people over thousands of years have developed the building blocks and made the discoveries in mathematics and science to make such 3D magic possible, and The History of Visual Magic in Computers is dedicated to all of them and tells a little of their story. It traces the earliest understanding of 3D and then foundational mathematics to explain and construct 3D; from mechanical computers up to today’s tablets. Several of the amazing computer graphics algorithms and tricks came of periods where eruptions of new ideas and techniques seem to occur all at once. Applications emerged as the fundamentals of how to draw lines and create realistic images were better understood, leading to hardware 3D controllers that drive the display all the way to stereovision and virtual reality.
Author: Avimanyu Bandyopadhyay
Publisher:
ISBN: 9781789341072
Category : Computers
Languages : en
Pages : 452
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Book Description
Explore GPU-enabled programmable environment for machine learning, scientific applications, and gaming using PuCUDA, PyOpenGL, and Anaconda Accelerate Key Features Understand effective synchronization strategies for faster processing using GPUs Write parallel processing scripts with PyCuda and PyOpenCL Learn to use the CUDA libraries like CuDNN for deep learning on GPUs Book 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 learn Utilize Python libraries and frameworks for GPU acceleration Set up a GPU-enabled programmable machine learning environment on your system with Anaconda Deploy your machine learning system on cloud containers with illustrated examples Explore PyCUDA and PyOpenCL and compare them with platforms such as CUDA, OpenCL and ROCm. Perform data mining tasks with machine learning models on GPUs Extend your knowledge of GPU computing in scientific applications Who 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.
Author: Frank N. Magill
Publisher: Routledge
ISBN: 1134264623
Category : History
Languages : en
Pages : 767
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Book Description
First Published in 2004. Volume II provides the hard facts and the history behind the headlines; significant 20th-century events in the evolution of all aspects of business and commerce are described in chronologically-arranged articles. The text of each article is divided into two sections: Summary of the Event describes the event itself and the circumstances leading up to it, and Impact of the Event analyzes the influence of the event on the evolution of business practice or on a major industry in both the short and long terms. Each article concludes with a fully annotated Bibliography.
Author: Natalia Kryvinska
Publisher: Springer Nature
ISBN: 3030621510
Category : Technology & Engineering
Languages : en
Pages : 587
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Book Description
This book provides solutions to manage information competently in order to increase its business usage. The information/knowledge business is a highly-dynamic evolving industry, and the novel methodologies and practices for the business information processing, as well as application of mathematical models to the business analytics and efficient management, are the most essential for the decision-making and further development of this field. Consequently, in this series subline first volume, the authors study challenges and opportunities, as well as embrace different aspects of business information processing for an efficient enterprise management. The authors cover also methods and techniques, as well as strategies for the efficient business information processing for management. Besides, the authors analyse strategies for lowering business information/data loss, while improving customer satisfaction and maintenance levels. The major goal is to analyse the key aspects of managerial implications on the informational business on the continuous basis.
Author: Jason Sanders
Publisher: Addison-Wesley Professional
ISBN: 0132180138
Category : Computers
Languages : en
Pages : 524
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Book Description
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
