LINQ to HPC: Developing Big Data Applications on Windows HPC ServerSaptak Sen
This document discusses large-scale data processing enabled by new technologies. It notes that large data volumes from 100s of TBs to 10s of PBs can now be processed at low cost using distributed parallel frameworks like MapReduce. New data sources include sensors, devices, and unstructured data like text and images. These new technologies enable analyzing this data to answer questions and gain new insights about product popularity, best ads to serve, and detecting fraud.
LINQ to HPC: Developing Big Data Applications on Windows HPC ServerSaptak Sen
This document discusses large-scale data processing enabled by new technologies. It notes that large data volumes from 100s of TBs to 10s of PBs can now be processed at low cost using distributed parallel frameworks like MapReduce. New data sources include sensors, devices, and unstructured data like text and images. These new technologies enable analyzing this data to answer questions and gain new insights about product popularity, best ads to serve, and detecting fraud.
HPC (High Performance Computing) utilizes parallel processing for applications like actuarial sciences, financial modeling, and genetics research. A hybrid HPC-Azure configuration uses on-premise compute nodes and Azure compute nodes for additional capacity. The head node distributes work units to the compute node clusters. Powershell and the Windows Azure Management Library can automate starting and stopping Azure nodes to control costs in this hybrid configuration.
A Sensing Coverage Analysis of a Route Control Method for Vehicular Crowd Sen...Osamu Masutani
The document proposes and evaluates route control methods for vehicular crowd sensing to maximize sensing coverage of a city. It presents three key ideas: (1) modifying vehicle routes to pass through areas of high sensing demand, (2) reserving routes to avoid traffic concentration, and (3) using predictive reservations for longer routes. The methodology and evaluation show that these methods can enhance coverage without significantly increasing travel time, especially for static and uniform demands. Future work includes optimization techniques and more realistic simulations.
Windows Server 2016 で作るシンプルなハイパーコンバージドインフラ (Microsoft TechSummit 2016)Takamasa Maejima
2016年11月に開催された Microsoft TechSummit 2016 での、Windows Server 2016 ストレージ機能 (SDS) を活用したハイパーコンバージドインフラ (HCI) に関するセッションスライドです。
[イベント名] Microsoft TechSummit 2016
[開催日] 2016年11月1日
[セッションID] CDP-002
[セッションタイトル] Windows Server 2016 で作るシンプルなハイパーコンバージドインフラ
Similar to 破「Windows azureでhpc 」わんくま大阪2013年12月 (20)
7. Windows Azure の最新ネタを・・
HdInsight (Microsoft Hadoop)のサービスイン (12月)
PowerPivot、Power View、およびその他の Microsoft BI
ツールで Hadoop データを分析可能。
Oracle DB 仕込みの仮想マシンの提供(10月)
Windows Server 2012 R2版仮想マシンの提供(10月)
Visual Studio Online(Preview)☆
42. MVPになると・・・(特典)
① Visual Studio Ultimate with MSDN または
Visual Studio Premium with MSDN のライセンスが付与
② MVPとの交流会(日本及びUS)
・MVP Open Day (品川で日本人対象)
・MVP Global Summit (US本国にて世界大会)
③ その他 多くの特典
58. Optiplex 9020 USFF、Precision T1700 SFF、PowerEdge VRTXの構成について比較。
ラック搭載時の重量及び消費電力など
9020 USFF T1700 SFF VRTX
1台あた
りのス
ペック
CPUタイプ
Core i7-4770
1ソケット4コア
Intel Xeon E3-1245
1ソケット4コア
Intel Xeon E5-2680
(2ソケット16コア)
×4ブレード
寸法/重量
23.7 cm x 6.5 cm x
24.0 cm / 3.3 kg
29.0 cm x 9.26 cm x
31.2 cm / 5.3kg
21.9 cm x 48.2 cm x
73.0 cm / 68.7 kg
消費電力 200W 140W 2269W
台数 21 21 1
総コア数 84コア 84コア 64コア
総重量 69.3 kg 111.3kg 68.7 kg
総消費電力 4200W 2940W 2269W
前頁検討例=36U搭載可能台数で比較。