2012 SC Companion: High Performance Computing, Networking Storage and Analysis最新文献_第9页

Integrate Military with Distributed Cloud Computing and Secure Virtualization 将军事与分布式云计算和安全虚拟化相结合

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.145

J. M. Reddy, J. Monika

{"title":"Integrate Military with Distributed Cloud Computing and Secure Virtualization","authors":"J. M. Reddy, J. Monika","doi":"10.1109/SC.Companion.2012.145","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.145","url":null,"abstract":"Cloud computing is known as a novel information technology (IT) concept, which involves facilitated and rapid access to networks, servers, data saving media, applications and services via Internet with minimum hardware requirements. Use of information systems and technologies at the battlefield is not new. Information superiority is a force multiplier and is crucial to mission success. Distributed cloud computing in the Military systems are operational today. In the near future extensive use of military clouds at the battlefield is predicted. Integrating cloud computing logic to military applications will increase the flexibility, cost-effectiveness, efficiency and accessibility capabilities. In this paper, distributed cloud computing concepts are defined. Cloud computing supported battlefield applications are analyzed. The effects of cloud computing systems on the information domain in future warfare are discussed. Battlefield opportunities and novelties which might be introduced by distributed cloud computing systems are researched. The role of military clouds in future warfare is proposed in this paper. It was concluded that military clouds will be indispensible components of the future battlefield. Military clouds have the potential of increasing situational awareness at the battlefield and facilitating the settlement of information superiority.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"86 1","pages":"1200-1206"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82205997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

In-Situ Feature Tracking and Visualization of a Temporal Mixing Layer 时间混合层的原位特征跟踪与可视化

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.335

E. Duque, Daniel E. Hiepler, S. Legensky, C. Stone

引用次数: 1

Achieving design targets by stochastic car crash simulations 通过随机碰撞模拟实现设计目标

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.350

T. Yasuki

引用次数: 0

Oh, $#*@! Exascale! The Effect of Emerging Architectures on Scientific Discovery 哦,$ # * @ !Exascale !新兴架构对科学发现的影响

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.38

K. Moreland

引用次数: 13

A Hybrid Scheduling Approach for Scalable Heterogeneous Hadoop Systems 可扩展异构Hadoop系统的混合调度方法

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.155

Aysan Rasooli Oskooei, D. Down

引用次数: 76

Executing Optimized Irregular Applications Using Task Graphs within Existing Parallel Models 在现有并行模型中使用任务图执行优化的不规则应用程序

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.43

Christopher D. Krieger, M. Strout, J. Roelofs, A. Bajwa

引用次数: 13

Neural Circuit Simulation of Hodgkin-Huxley Type Neurons Toward Peta Scale Computers 面向Peta级计算机的霍奇金-赫胥黎型神经元的神经回路模拟

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.314

Daisuke Miyamoto, T. Kazawa, R. Kanzaki

引用次数: 6

Improved Real-Time Computation Engine for a Dispatcher Training Center of the European Transmission Network 欧洲输电网调度员培训中心改进的实时计算引擎

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.52

B. Haut, Francois Bouchez, F. Villella

{"title":"Improved Real-Time Computation Engine for a Dispatcher Training Center of the European Transmission Network","authors":"B. Haut, Francois Bouchez, F. Villella","doi":"10.1109/SC.Companion.2012.52","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.52","url":null,"abstract":"Dispatcher Training Simulators (DTS) are fundamental tools used by Transmission System Operators (TSO's) and Distribution System Operators (DSO's) around the world to train their dispatchers on frequent or uncommon situations. DTS widely used are generally based on simplified models for the system dynamics (static modelling or very simplified dynamics) and are limited to small/medium systems due to constraints in computational performance. Taking into account fast dynamics on large system is a real challenge for the simulation engine of a DTS. Indeed, in order to represent effectively the reaction of the power system, the simulation must be carried out very close to real-time. The PEGASE project addressed this challenge on the European Transmission Network (ETN). Many different algorithms were investigated and several have been implemented in a prototype based on FAST, a full-dynamic DTS simulation engine developed by Tractebel Engineering S.A. and integrated in the Energy Management System (EMS) of several TSO's. This paper describes the considered algorithmic improvements and presents numerical results (both in terms of accuracy and efficiency) obtained on a representation of the whole ETN.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"54 1","pages":"333-340"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77781127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Improving Data Analysis Performance for High-Performance Computing with Integrating Statistical Metadata in Scientific Datasets 集成科学数据集中的统计元数据，提高高性能计算的数据分析性能

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.156

Jialin Liu, Yong Chen

{"title":"Improving Data Analysis Performance for High-Performance Computing with Integrating Statistical Metadata in Scientific Datasets","authors":"Jialin Liu, Yong Chen","doi":"10.1109/SC.Companion.2012.156","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.156","url":null,"abstract":"Scientific datasets and libraries, such as HDF5, ADIOS, and NetCDF, have been used widely in many data intensive applications. These libraries have their special file formats and I/O functions to provide efficient access to large datasets. When the data size keeps increasing, these high level I/O libraries face new challenges. Recent studies have started to utilize database techniques such as indexing and subsetting, and data reorganization to manage the increasing datasets. In this work, we present a new approach to boost the data analysis performance, namely Fast Analysis with Statistical Metadata (FASM), via data subsetting and integrating a small amount of statistics into the original datasets. The added statistical information illustrates the data shape and provides knowledge of the data distribution; therefore the original I/O libraries can utilize these statistical metadata to perform fast queries and analyses. The proposed FASM approach is currently evaluated with the PnetCDF on Lustre file systems, but can also be implemented with other scientific libraries. The FASM can potentially lead to a new dataset design and can have an impact on big data analysis.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"50 1","pages":"1292-1295"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87796451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Poster: MPACK 0.7.0: Multiple Precision Version of BLAS and LAPACK 海报:MPACK 0.7.0: BLAS和LAPACK的多精度版本

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.183

Maho Nakata

引用次数: 4