This document summarizes a virtual server implementation project. It discusses setting up a virtual server environment to save power compared to running multiple physical servers. It includes calculations showing the power savings and comparisons of running 5 virtual versus physical servers. It also lists the hardware and software needed for the project, including a standalone server, UPS, storage, and virtualization software. A project plan is outlined with tasks like planning, installation, testing, and closing out the project over about 7 days. The total projected budget is $17,590 including costs for hardware, software tools, and internal staff labor.
Intel Galileo acts as a chess client: read the chess figures position (NFC tags) and send the positions into the cloud. An Intel Edison reads the positions, calculates the best move (with Stockfish) and write the result back to the cloud. The Intel Galileo reads the bestmove result and shows it.
Intel Galileo acts as a chess client: read the chess figures position (NFC tags) and send the positions into the cloud. An Intel Edison reads the positions, calculates the best move (with Stockfish) and write the result back to the cloud. The Intel Galileo reads the bestmove result and shows it.
Getting The Most Out of VR | Sinjin BainJessica Tams
Delivered at Casual Connect Europe 2016
Virtual Reality and Augmented (or Mixed) Reality have unique and distinct capabilities from other interactive platforms that require new approaches to development and content creation and management. We will explore some innovative approaches to runtime performance, collaborative development, data and live operations to get the most out of your games in development.
Protecting Real-Time GPU Kernels in Integrated CPU-GPU SoC PlatformsHeechul Yun
Presentation slides of the following paper at ECRTS'18.
Waqar Ali, Heechul Yun. "Protecting Real-Time GPU Kernels on Integrated CPU-GPU SoC Platforms." Euromicro Conference on Real-Time Systems (ECRTS), 2018
How to Burn Multi-GPUs using CUDA stress test memoNaoto MATSUMOTO
How to Burn Multi-GPUs using CUDA stress test memo (2017/05/20)
SAKURA Internet, Inc. / SAKURA Internet Research Center.
Senior Researcher / Naoto MATSUMOTO
Presentació a càrrec d'Adrián Macía, cap de Càlcul Científic del CSUC, duta a terme a la "3a Jornada de formació sobre l'ús del servei de càlcul" celebrada el 29 d'octubre de 2020 en format virtual.
De gemiddelde GPU bevat tegenwoordig meer PK's dan de CPU. Naar aanleiding hiervan komen er steeds meer mogelijkheden om computationele problemen te verplaatsen van de CPU naar de GPU. Deze presentatie zal een inleiding zijn hoe je dit in Java kunt doen met behulp van Jogamp JoCL. Aan de hand van enkele simpele problemen wordt aangetoond wanneer een GPU beter ingezet kan worden dan een CPU en vice versa. Dit is ook een van de speerpunten in Java 9 (Project Sumatra) wat o.a. JoCL als inspiratie gebruikt.
An experiment that has been conducted to research whether heat will have a positive or negative effect on a GPU (Graphics Processing Unit). This experiment was conducted to show how the head effects the performance of the GPU and provides insight to the production of air cooling systems and how to create them to be more efficient.
Slides for a talk @ try! Swift Tokyo 2018
[Abstract] Metal is an API that provides access to the GPU. Apple announced it's 10x times faster than OpenGL. In this session, I'll explain the basics of Metal, then compare the performance of graphics rendering with UIImageView.
Even if you don't use the API directly, your app is implicitly benefitting from Metal. This comparison to a familiar class will lead you to be conscious of the GPU layer that we usually miss.
【概要】
MetalはGPUへのアクセスを提供するAPIで、OpenGLより10倍速いという謳い文句で登場しました。本セッションではMetalの基礎を解説しつつ、iOSにおけるグラフィックス描画性能をUIImageViewと比較してみます。
MetalのAPIを直接利用する機会がなくても、Metalはあなたのアプリの裏で暗躍しています。身近なクラスとの比較を通じて、普段我々が意識することのないGPUのレイヤで何が起きているのか、目を向けてみるきっかけになればと思います。
Future of computing is boring (and that is exciting!) alekn
We see a trend where computing becomes a metered utility similar to how the electric grid evolved. Initially electricity was generated locally but economies of scale (and standardization) made it more efficient and economical to have utility companies managing the electric grid. Similar developments can be seen in computing where scientific grids paved the way for commercial cloud computing offerings. However, in our opinion, that evolution is far from finished and in this paper we bring forward the remaining challenges and propose a vision for the future of computing. In particular we focus on diverging trends in the costs of computing and developer time, which suggests that future computing architectures will need to optimize for developer time.
Keywords—cloud computing, future, economics, cost
Despite the increase of deep learning practitioners and researchers, many of them do not use GPUs, this may lead to long training/evaluation cycles and non-practical research.
In his talk, Lior shares how to get started with GPUs and some of the best practices that helped him during research and work. The talk is for everyone who works with machine learning (deep learning experience is NOT mandatory!), It covers the very basics of how GPU works, CUDA drivers, IDE configuration, training, inference, and multi-GPU training.
LAS16-307: Benchmarking Schedutil in AndroidLinaro
LAS16-307: Benchmarking Schedutil in Android
Speakers: Steve Muckle
Date: September 28, 2016
★ Session Description ★
Being able to see the performance and power impacts of changes in a real world environment such as Android is a prerequisite to doing meaningful development on scheduler-guided frequency (or many other sensitive subsystems). The first half of this session will review setting up the tools to automate testing for performance and power in Android. The second half will cover the results of using these tests to compare the schedutil and interactive governors.
★ Resources ★
Etherpad: pad.linaro.org/p/las16-307
Presentations & Videos: http://connect.linaro.org/resource/las16/las16-307/
★ Event Details ★
Linaro Connect Las Vegas 2016 – #LAS16
September 26-30, 2016
http://www.linaro.org
http://connect.linaro.org
SIGUCCS 2013 ACM presentation.
Energy overhead of the graphical user interface in server operating systems. Heather Brotherton, J. Eric Dietz, John McGrory, and Fredrick Mtenzi. 2013. In Proceedings of the 2013 ACM annual conference on Special interest group on university and college computing services (SIGUCCS '13). ACM, New York, NY, USA, 65-68. DOI=10.1145/2504776.2504781 http://doi.acm.org/10.1145/2504776.2504781
VMworld 2013: Quantifying the Business Value of VMware Horizon View VMworld
VMworld 2013
Aivars Apsite, Metro Health
Ridwan Huq, VMware
Learn more about VMworld and register at http://www.vmworld.com/index.jspa?src=socmed-vmworld-slideshare
Getting The Most Out of VR | Sinjin BainJessica Tams
Delivered at Casual Connect Europe 2016
Virtual Reality and Augmented (or Mixed) Reality have unique and distinct capabilities from other interactive platforms that require new approaches to development and content creation and management. We will explore some innovative approaches to runtime performance, collaborative development, data and live operations to get the most out of your games in development.
Protecting Real-Time GPU Kernels in Integrated CPU-GPU SoC PlatformsHeechul Yun
Presentation slides of the following paper at ECRTS'18.
Waqar Ali, Heechul Yun. "Protecting Real-Time GPU Kernels on Integrated CPU-GPU SoC Platforms." Euromicro Conference on Real-Time Systems (ECRTS), 2018
How to Burn Multi-GPUs using CUDA stress test memoNaoto MATSUMOTO
How to Burn Multi-GPUs using CUDA stress test memo (2017/05/20)
SAKURA Internet, Inc. / SAKURA Internet Research Center.
Senior Researcher / Naoto MATSUMOTO
Presentació a càrrec d'Adrián Macía, cap de Càlcul Científic del CSUC, duta a terme a la "3a Jornada de formació sobre l'ús del servei de càlcul" celebrada el 29 d'octubre de 2020 en format virtual.
De gemiddelde GPU bevat tegenwoordig meer PK's dan de CPU. Naar aanleiding hiervan komen er steeds meer mogelijkheden om computationele problemen te verplaatsen van de CPU naar de GPU. Deze presentatie zal een inleiding zijn hoe je dit in Java kunt doen met behulp van Jogamp JoCL. Aan de hand van enkele simpele problemen wordt aangetoond wanneer een GPU beter ingezet kan worden dan een CPU en vice versa. Dit is ook een van de speerpunten in Java 9 (Project Sumatra) wat o.a. JoCL als inspiratie gebruikt.
An experiment that has been conducted to research whether heat will have a positive or negative effect on a GPU (Graphics Processing Unit). This experiment was conducted to show how the head effects the performance of the GPU and provides insight to the production of air cooling systems and how to create them to be more efficient.
Slides for a talk @ try! Swift Tokyo 2018
[Abstract] Metal is an API that provides access to the GPU. Apple announced it's 10x times faster than OpenGL. In this session, I'll explain the basics of Metal, then compare the performance of graphics rendering with UIImageView.
Even if you don't use the API directly, your app is implicitly benefitting from Metal. This comparison to a familiar class will lead you to be conscious of the GPU layer that we usually miss.
【概要】
MetalはGPUへのアクセスを提供するAPIで、OpenGLより10倍速いという謳い文句で登場しました。本セッションではMetalの基礎を解説しつつ、iOSにおけるグラフィックス描画性能をUIImageViewと比較してみます。
MetalのAPIを直接利用する機会がなくても、Metalはあなたのアプリの裏で暗躍しています。身近なクラスとの比較を通じて、普段我々が意識することのないGPUのレイヤで何が起きているのか、目を向けてみるきっかけになればと思います。
Future of computing is boring (and that is exciting!) alekn
We see a trend where computing becomes a metered utility similar to how the electric grid evolved. Initially electricity was generated locally but economies of scale (and standardization) made it more efficient and economical to have utility companies managing the electric grid. Similar developments can be seen in computing where scientific grids paved the way for commercial cloud computing offerings. However, in our opinion, that evolution is far from finished and in this paper we bring forward the remaining challenges and propose a vision for the future of computing. In particular we focus on diverging trends in the costs of computing and developer time, which suggests that future computing architectures will need to optimize for developer time.
Keywords—cloud computing, future, economics, cost
Despite the increase of deep learning practitioners and researchers, many of them do not use GPUs, this may lead to long training/evaluation cycles and non-practical research.
In his talk, Lior shares how to get started with GPUs and some of the best practices that helped him during research and work. The talk is for everyone who works with machine learning (deep learning experience is NOT mandatory!), It covers the very basics of how GPU works, CUDA drivers, IDE configuration, training, inference, and multi-GPU training.
LAS16-307: Benchmarking Schedutil in AndroidLinaro
LAS16-307: Benchmarking Schedutil in Android
Speakers: Steve Muckle
Date: September 28, 2016
★ Session Description ★
Being able to see the performance and power impacts of changes in a real world environment such as Android is a prerequisite to doing meaningful development on scheduler-guided frequency (or many other sensitive subsystems). The first half of this session will review setting up the tools to automate testing for performance and power in Android. The second half will cover the results of using these tests to compare the schedutil and interactive governors.
★ Resources ★
Etherpad: pad.linaro.org/p/las16-307
Presentations & Videos: http://connect.linaro.org/resource/las16/las16-307/
★ Event Details ★
Linaro Connect Las Vegas 2016 – #LAS16
September 26-30, 2016
http://www.linaro.org
http://connect.linaro.org
SIGUCCS 2013 ACM presentation.
Energy overhead of the graphical user interface in server operating systems. Heather Brotherton, J. Eric Dietz, John McGrory, and Fredrick Mtenzi. 2013. In Proceedings of the 2013 ACM annual conference on Special interest group on university and college computing services (SIGUCCS '13). ACM, New York, NY, USA, 65-68. DOI=10.1145/2504776.2504781 http://doi.acm.org/10.1145/2504776.2504781
VMworld 2013: Quantifying the Business Value of VMware Horizon View VMworld
VMworld 2013
Aivars Apsite, Metro Health
Ridwan Huq, VMware
Learn more about VMworld and register at http://www.vmworld.com/index.jspa?src=socmed-vmworld-slideshare
A brief technical overview about GPU power consumption and performance, with references to the latest architecture developed by Nvidia: Maxwell and Tegra X1.
Co-Author: Pietro Piscione (https://www.linkedin.com/pub/pietro-piscione/84/b37/926)
An introduction to the Design of Warehouse-Scale ComputersAlessio Villardita
A brief overview of the main factors involved in the design of Warehouse-Scale Computers (WSC), from the hardware, to the cooling system to the overall plant energy efficiency, always keeping in mind the costs of such a big architecture.
Co-Author: Pietro Piscione (https://www.linkedin.com/pub/pietro-piscione/84/b37/926)
A work based on:
"The Datacenter as a Computer, An Introduction to the Design of Warehouse-Scale Machines, Second Edition"
by
Luiz André Barroso
Jimmy Clidaras
Urs Hölzle
2013.11.14 Big Data Workshop Michael BrowneNUI Galway
Michael Browne from the Irish Centre for High End Computing presented this overview of Big Data and Computer Architecture during the Big Data Workshop hosted by the Social Sciences Computing Hub at the Whitaker Institute on the 14th November 2013
17. Hardware List $8,990 Total equipment cost for the project $700 $700 HP T1500 XR UPS G2 1 $200 $200 17” LCD Monitor 1 $15 $15 Halogen light(USB) 1 $1,875 $1,875 SSD Flash memory(64GB) 1 $200 $100 External HDD 320G(USB) 2 $6,000 $6,000 Standalone Server (HP Prolian ML 370 G5) CPU: Quad Core Processor 2 GHz RAM: 2G 1 Total cost Unit cost Description Quantity Equipment needed Timing
18. Software List $5,591 Total software cost for the project $38 $38 Power saving application 1 $0 $0 Recording power consumption software 1 $1,583 $1,583 Windows Server 2003(5CAL) 1 $0 $0 Ubuntu Server 1 $570 $570 Windows2000 Advanced Server(5CAL) 1 $3,400 $3,400 VMware GSX 1 Total cost Unit cost Description Quantity Software needed Timing
19. MS Project 1day? Audit 15 7DAY Total 0.5 days Closeout Report 14 1.5 days? Closing 0.3 days Sketch of Change 13 0.5 days Change Control 12 0.8 days Control 1 day? Training 11 0.5 days Test 10 1 day? Installation 9 2.5 days? Executing 0.3 days Service 8 0.3 days Software 7 0.3 days Hardware 6 0.2 days Sketch 5 0.1 days MS Project 4 0.5 days WBS 3 1.7 days Planning 0.2 days Feasibility study 2 0.3 days Project Charter 1 0.5 days Initiate Duration Task_Name ID