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| Author | : Matt Pharr |
| Publisher | : Addison-Wesley Professional |
| Total Pages | : 814 |
| Release | : 2005 |
| Genre | : Computers |
| ISBN | : 9780321335593 |
More useful techniques, tips, and tricks for harnessing the power of the new generation of powerful GPUs.
| Author | : Fouad Sabry |
| Publisher | : One Billion Knowledgeable |
| Total Pages | : 430 |
| Release | : 2022-07-10 |
| Genre | : Technology & Engineering |
| ISBN | : |
What Is General Purpose Computing On Graphics Processing Units The term "general-purpose computing on graphics processing units" (also known as "general-purpose computing on GPUs") refers to the practice of employing a graphics processing unit (GPU), which ordinarily performs computation only for the purpose of computer graphics, to carry out computation in programs that are typically performed by the central processing unit (CPU). The already parallel nature of graphics processing may be further parallelized by using numerous video cards in a single computer or a large number of graphics processors. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: General-purpose computing on graphics processing units Chapter 2: Supercomputer Chapter 3: Flynn's taxonomy Chapter 4: Graphics processing unit Chapter 5: Physics processing unit Chapter 6: Hardware acceleration Chapter 7: Stream processing Chapter 8: BrookGPU Chapter 9: CUDA Chapter 10: Close to Metal Chapter 11: Larrabee (microarchitecture) Chapter 12: AMD FireStream Chapter 13: OpenCL Chapter 14: OptiX Chapter 15: Fermi (microarchitecture) Chapter 16: Pascal (microarchitecture) Chapter 17: Single instruction, multiple threads Chapter 18: Multidimensional DSP with GPU Acceleration Chapter 19: Compute kernel Chapter 20: AI accelerator Chapter 21: ROCm (II) Answering the public top questions about general purpose computing on graphics processing units. (III) Real world examples for the usage of general purpose computing on graphics processing units in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of general purpose computing on graphics processing units' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of general purpose computing on graphics processing units.
| Author | : Tor M. Aamodt |
| Publisher | : Springer Nature |
| Total Pages | : 122 |
| Release | : 2022-05-31 |
| Genre | : Technology & Engineering |
| ISBN | : 3031017595 |
Originally developed to support video games, graphics processor units (GPUs) are now increasingly used for general-purpose (non-graphics) applications ranging from machine learning to mining of cryptographic currencies. GPUs can achieve improved performance and efficiency versus central processing units (CPUs) by dedicating a larger fraction of hardware resources to computation. In addition, their general-purpose programmability makes contemporary GPUs appealing to software developers in comparison to domain-specific accelerators. This book provides an introduction to those interested in studying the architecture of GPUs that support general-purpose computing. It collects together information currently only found among a wide range of disparate sources. The authors led development of the GPGPU-Sim simulator widely used in academic research on GPU architectures. The first chapter of this book describes the basic hardware structure of GPUs and provides a brief overview of their history. Chapter 2 provides a summary of GPU programming models relevant to the rest of the book. Chapter 3 explores the architecture of GPU compute cores. Chapter 4 explores the architecture of the GPU memory system. After describing the architecture of existing systems, Chapters 3 and 4 provide an overview of related research. Chapter 5 summarizes cross-cutting research impacting both the compute core and memory system. This book should provide a valuable resource for those wishing to understand the architecture of graphics processor units (GPUs) used for acceleration of general-purpose applications and to those who want to obtain an introduction to the rapidly growing body of research exploring how to improve the architecture of these GPUs.
| Author | : Jason Sanders |
| Publisher | : Addison-Wesley Professional |
| Total Pages | : 524 |
| Release | : 2010-07-19 |
| Genre | : Computers |
| ISBN | : 0132180138 |
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
| Author | : Raphael Couturier |
| Publisher | : CRC Press |
| Total Pages | : 496 |
| Release | : 2013-11-21 |
| Genre | : Computers |
| ISBN | : 1466571640 |
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
| Author | : Hyesoon Kim |
| Publisher | : Springer Nature |
| Total Pages | : 88 |
| Release | : 2022-05-31 |
| Genre | : Technology & Engineering |
| ISBN | : 3031017374 |
General-purpose graphics processing units (GPGPU) have emerged as an important class of shared memory parallel processing architectures, with widespread deployment in every computer class from high-end supercomputers to embedded mobile platforms. Relative to more traditional multicore systems of today, GPGPUs have distinctly higher degrees of hardware multithreading (hundreds of hardware thread contexts vs. tens), a return to wide vector units (several tens vs. 1-10), memory architectures that deliver higher peak memory bandwidth (hundreds of gigabytes per second vs. tens), and smaller caches/scratchpad memories (less than 1 megabyte vs. 1-10 megabytes). In this book, we provide a high-level overview of current GPGPU architectures and programming models. We review the principles that are used in previous shared memory parallel platforms, focusing on recent results in both the theory and practice of parallel algorithms, and suggest a connection to GPGPU platforms. We aim to provide hints to architects about understanding algorithm aspect to GPGPU. We also provide detailed performance analysis and guide optimizations from high-level algorithms to low-level instruction level optimizations. As a case study, we use n-body particle simulations known as the fast multipole method (FMM) as an example. We also briefly survey the state-of-the-art in GPU performance analysis tools and techniques. Table of Contents: GPU Design, Programming, and Trends / Performance Principles / From Principles to Practice: Analysis and Tuning / Using Detailed Performance Analysis to Guide Optimization
| Author | : Raphael Couturier |
| Publisher | : CRC Press |
| Total Pages | : 500 |
| Release | : 2013-11-21 |
| Genre | : Mathematics |
| ISBN | : 1466571624 |
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 | : Robert Robey |
| Publisher | : Simon and Schuster |
| Total Pages | : 702 |
| Release | : 2021-08-24 |
| Genre | : Computers |
| ISBN | : 1638350388 |
Parallel and High Performance Computing offers techniques guaranteed to boost your code’s effectiveness. Summary Complex calculations, like training deep learning models or running large-scale simulations, can take an extremely long time. Efficient parallel programming can save hours—or even days—of computing time. Parallel and High Performance Computing shows you how to deliver faster run-times, greater scalability, and increased energy efficiency to your programs by mastering parallel techniques for multicore processor and GPU hardware. About the technology Write fast, powerful, energy efficient programs that scale to tackle huge volumes of data. Using parallel programming, your code spreads data processing tasks across multiple CPUs for radically better performance. With a little help, you can create software that maximizes both speed and efficiency. About the book Parallel and High Performance Computing offers techniques guaranteed to boost your code’s effectiveness. You’ll learn to evaluate hardware architectures and work with industry standard tools such as OpenMP and MPI. You’ll master the data structures and algorithms best suited for high performance computing and learn techniques that save energy on handheld devices. You’ll even run a massive tsunami simulation across a bank of GPUs. What's inside Planning a new parallel project Understanding differences in CPU and GPU architecture Addressing underperforming kernels and loops Managing applications with batch scheduling About the reader For experienced programmers proficient with a high-performance computing language like C, C++, or Fortran. About the author Robert Robey works at Los Alamos National Laboratory and has been active in the field of parallel computing for over 30 years. Yuliana Zamora is currently a PhD student and Siebel Scholar at the University of Chicago, and has lectured on programming modern hardware at numerous national conferences. Table of Contents PART 1 INTRODUCTION TO PARALLEL COMPUTING 1 Why parallel computing? 2 Planning for parallelization 3 Performance limits and profiling 4 Data design and performance models 5 Parallel algorithms and patterns PART 2 CPU: THE PARALLEL WORKHORSE 6 Vectorization: FLOPs for free 7 OpenMP that performs 8 MPI: The parallel backbone PART 3 GPUS: BUILT TO ACCELERATE 9 GPU architectures and concepts 10 GPU programming model 11 Directive-based GPU programming 12 GPU languages: Getting down to basics 13 GPU profiling and tools PART 4 HIGH PERFORMANCE COMPUTING ECOSYSTEMS 14 Affinity: Truce with the kernel 15 Batch schedulers: Bringing order to chaos 16 File operations for a parallel world 17 Tools and resources for better code
| Author | : Simon Marlow |
| Publisher | : "O'Reilly Media, Inc." |
| Total Pages | : 322 |
| Release | : 2013-07-12 |
| Genre | : Computers |
| ISBN | : 1449335926 |
If you have a working knowledge of Haskell, this hands-on book shows you how to use the language’s many APIs and frameworks for writing both parallel and concurrent programs. You’ll learn how parallelism exploits multicore processors to speed up computation-heavy programs, and how concurrency enables you to write programs with threads for multiple interactions. Author Simon Marlow walks you through the process with lots of code examples that you can run, experiment with, and extend. Divided into separate sections on Parallel and Concurrent Haskell, this book also includes exercises to help you become familiar with the concepts presented: Express parallelism in Haskell with the Eval monad and Evaluation Strategies Parallelize ordinary Haskell code with the Par monad Build parallel array-based computations, using the Repa library Use the Accelerate library to run computations directly on the GPU Work with basic interfaces for writing concurrent code Build trees of threads for larger and more complex programs Learn how to build high-speed concurrent network servers Write distributed programs that run on multiple machines in a network
| Author | : Conor Helmick |
| Publisher | : |
| Total Pages | : 45 |
| Release | : 2022 |
| Genre | : Deep learning (Machine learning) |
| ISBN | : |
Graphics processing units (GPUs) contain a significant number of cores relative to central processing units (CPUs), allowing them to handle high levels of parallelization in multithreading. A general-purpose GPU (GPGPU) is a GPU that has its threads and memory repurposed on a software level to leverage the multithreading made possible by the GPU’s hardware, and thus is an extremely strong platform for intense computing – there is no hardware difference between GPUs and GPGPUs. Deep learning is one such example of intense computing that is best implemented on a GPGPU, as its hardware structure of a grid of blocks, each containing processing threads, can handle the immense number of necessary calculations in parallel. A convolutional neural network (CNN) created for financial data analysis shows this advantage in the runtime of the training and testing of a neural network.
