Enhancing manageability of execution and data for GPGPU computing

GPU;Multi tenancy;In situ analytics;Bandwidth sharing

Goswami, Anshuman ; Wolf, Matthew Computer Science Schwan, Karsten Vuduc, Richard Liu, Ling Kim, Hyesoon Yalamanchili, Sudhakar ; Wolf, Matthew

University:Georgia Institute of Technology

Department:Computer Science

关键词: GPU;    Multi tenancy;    In situ analytics;    Bandwidth sharing;   

Others  :  https://smartech.gatech.edu/bitstream/1853/58206/1/GOSWAMI-DISSERTATION-2017.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

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.