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    Enhancing manageability of execution and data for GPGPU computing

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    GOSWAMI-DISSERTATION-2017.pdf (2.849Mb)
    Date
    2016-12-13
    Author
    Goswami, Anshuman
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    Abstract
    GPGPUs are useful for many types of compute-intensive workloads from scientific simulations to cloud-focused applications like machine learning and graph analytics. However, unlike CPUs they do not allow for software-controlled sharing of resources. This leads to underutilization, unfair use and reduced programmability. This thesis looks at three different areas, 1) in situ analysis in scientific workflows, 2) multi tenancy in cloud computing environments, and 3) network sharing between evolving distributed GPU frameworks. The thesis presents four distinct software-scheduling based constructs to handle problems in each of these spaces.
    URI
    http://hdl.handle.net/1853/58206
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    • College of Computing Theses and Dissertations [1191]
    • Georgia Tech Theses and Dissertations [23877]

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