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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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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Memory System,l graphics applications interact with several memory spaces such as texture, constant, and render surfaces. While access to these memory spaces is available in GPGPU programming APIs like CUDA, we will focus on memory spaces employed in GPGPU programming in this chapter and in particular the microarchitecture support required to implement them.

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Modeling of Smooth Muscle ActivationIn this and the following chapter we will examine the architecture and microarchitecture of modern GPUs. We divide our discussion of GPU architecture into two parts: (1) examining the SIMT cores that implement computation in this chapter and then (2) looking at the memory system in the next chapter.

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978-3-031-00631-9Springer Nature Switzerland AG 2018

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General-Purpose Graphics Processor Architectures978-3-031-01759-9Series ISSN 1935-3235 Series E-ISSN 1935-3243

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Introduction,n video games. Today GPUs are found everywhere from inside smartphones, laptops, datacenters, and all the way to supercomputers. Indeed, an analysis of the Apple A8 application processor shows that it devotes more die area to its integrated GPU than to central processor unit (CPU) cores [A8H]. The d

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Programming Model,nce 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