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Titlebook: General-Purpose Graphics Processor Architectures; Tor M. Aamodt,Wilson Wai Lun Fung,Timothy G. Roger Book 2018 Springer Nature Switzerland

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發(fā)表于 2025-3-21 18:41:09 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱General-Purpose Graphics Processor Architectures
編輯Tor M. Aamodt,Wilson Wai Lun Fung,Timothy G. Roger
視頻videohttp://file.papertrans.cn/383/382157/382157.mp4
叢書(shū)名稱Synthesis Lectures on Computer Architecture
圖書(shū)封面Titlebook: General-Purpose Graphics Processor Architectures;  Tor M. Aamodt,Wilson Wai Lun Fung,Timothy G. Roger Book 2018 Springer Nature Switzerland
描述.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 theirhistory. 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 memor
出版日期Book 2018
版次1
doihttps://doi.org/10.1007/978-3-031-01759-9
isbn_softcover978-3-031-00631-9
isbn_ebook978-3-031-01759-9Series ISSN 1935-3235 Series E-ISSN 1935-3243
issn_series 1935-3235
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

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H.-F. Cheong,N. J. Shankar,C.-T. Chanthe growing use GPUs to develop and deploying machine learning systems [NVIDIA Corp., 2017]. Thus, the emphasis of this book is on features relevant to improving the performance and energy efficiency of non-graphics applications.
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David Zhang,Fangmei Chen,Yong Xuay not exist and so will may need to be created, perhaps by modifying the source code of existing GPU computing applications. For example, research exploring the introduction of transactional memory (TM) on GPUs required this because current GPUs do not support TM (see Section 5.3).
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David Zhang,Fangmei Chen,Yong Xunce with GPUs can follow the discussion in later chapters. We focus here on essential material, leaving more in-depth coverage to other references (e.g., [Kirk and Wen-Mei, 2016]). Many GPU computing benchmark suites exist which can be employed for architecture research. Learning how GPUs are progra
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Automatic Learning of Fuzzy Relations, of the GPU architecture. Section 5.1 explores work on how threads are scheduled in GPUs. Section 5.2 looks at alternative programming methodologies and Section 5.4 examines work on heterogeneous CPU/GPU computing.
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