PPT-Efficient Memory Virtualization

Author : evadeshell | Published Date : 2020-06-23

Reducing Dimensionality of Nested Page Walks Jayneel Gandhi Arkaprava Basu Mark D Hill Michael M Swift MICRO47 Executive Summary Problem TLB misses in

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Efficient Memory Virtualization: Transcript

Reducing Dimensionality of Nested Page Walks Jayneel Gandhi Arkaprava Basu Mark D Hill Michael M Swift MICRO47 Executive Summary Problem TLB misses in virtual machines. Abdullah . Almurayh. . MSCS . Graduate . Candidate. Committee members: . Dr. Edward Chow (Advisor) . Dr. . . Chuan. . Yue. Dr. . Albert . Glock. Summer2011. Master Thesis Proposal. MCEVE. Outline. Introduction. Based on materials from:. Introduction to Virtual Machines by Carl . Waldspurger. Understanding Intel® Virtualization Technology (VT) by N. B. . Sahgal. and D. Rodgers. Intel Virtualization Technology Roadmap and VT-. FAWN. :. Workloads and Implications. Vijay . Vasudevan. , David Andersen, Michael . Kaminsky. *, Lawrence Tan, . Jason Franklin. , . Iulian. . Moraru. Carnegie Mellon University, *Intel Labs Pittsburgh. : Retrofitting Protection of Virtual Machines in Multi-tenant Cloud with Nested Virtualization. Fengzhe. Zhang,. . Jin Chen, . Haibo. Chen, . Binyu. . Zang. 2011-10-24. System Research Group. Parallel Processing Institute. U . Wisc. and HP Labs. ISCA’13. Architecture Reading Club Summer'13. 1. Key points. Big memory workloads . Memcached. , databases, graph analysis . Analysis shows. TLB misses can account for . upto. : Retrofitting Protection of Virtual Machines in Multi-tenant Cloud with Nested Virtualization. Fengzhe. Zhang,. . Jin Chen, . Haibo. Chen, . Binyu. . Zang. 2011-10-24. System Research Group. Parallel Processing Institute. Reducing Dimensionality of Nested Page Walks . Jayneel . Gandhi. , . Arkaprava. . Basu. ,. Mark D. . Hill, Michael . M. . Swift. MICRO-47 . Executive Summary. Problem: TLB misses in virtual machines. Matt McSpirit. Senior Product Marketing Manager. Microsoft Corporation. @mattmcspirit. VIR317-R. What will we cover?. Why Hyper-V?. Why System Center?. Why Microsoft Cloud?. Summary. Resources. Trends – Changing Market Landscape. Liang . Liang, . Rong. Chen. , . Haibo. Chen, . Yubin. Xia, . KwanJong. . Park, . Binyu. . Zang. , . Haibing. Guan. Institute of Parallel and Distributed . Systems. Shanghai Jiao Tong University, China. Vera Asodi. VMware. What is Virtualization?. Virtualization is a technology that allows you to transform hardware into software. Virtualization allows you to run multiple operating systems simultaneously on a single computer. FAWN. :. Workloads and Implications. Vijay . Vasudevan. , David Andersen, Michael . Kaminsky. *, Lawrence Tan, . Jason Franklin. , . Iulian. . Moraru. Carnegie Mellon University, *Intel Labs Pittsburgh. Jaeung Han¹, Jeongseob Ahn¹, . Changdae. Kim. ¹, Youngjin Kwon¹, . Young-. ri. Choi², and Jaehyuk Huh¹. ¹ KAIST. (Korea Advanced Institute of Science and Technology). ² KISTI. (Korea Institute of Science and Technology Information). Samuli Laine Tero Karras. NVIDIA Research. “Bitmaps” vs “Vectors”. 2. Bitmap 2D image. Vector 2D image. Why Not “Bitmaps” in 3D?. 3. ?. “Bitmap” 3D object. Vector 3D object. Voxel datasets often consist of volume data . Gyrokinetic. Particle-to-Grid Interpolation for Multicore Processors. Kamesh. . Madduri. , . Samuel Williams. , . Stephane. . Ethier. , . Leonid . Oliker. , John . Shalf. , Erich . Strohmaier. , Katherine Yelick .

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