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

TDPSS: A Scalable Time Domain Power System Simulator for Dynamic Security Assessment TDPSS:用于动态安全评估的可扩展时域电力系统模拟器

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.51

S. Khaitan, J. McCalley

{"title":"TDPSS: A Scalable Time Domain Power System Simulator for Dynamic Security Assessment","authors":"S. Khaitan, J. McCalley","doi":"10.1109/SC.Companion.2012.51","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.51","url":null,"abstract":"Simulation plays a very crucial role to model, study and experiment with any design innovation proposed in the power systems. Since mathematical modeling of power systems leads to tens of thousands of stiff DAEs (differential and algebraic equations), the design of power system simulators involve exercising a trade-off between the simulation speed and modeling accuracy. Lack of efficient and detailed simulators forces the designers to experiment their techniques with small test systems and hence, the results obtained from such experiments may not be representative of the results obtained using real-life power systems. In this paper, we present TDPSS, a high speed time domain power system simulator for dynamic security assessment. TDPSS has been designed using object-oriented programming framework, and thus, it is modular and extensible. By offering a variety of models of power system components and fast numerical algorithms, it provides the user with the flexibility to experiment with different design options in an efficient manner. We discuss the design of TDPSS to give insights into the simulation infrastructure and also discuss the areas where TDPSS can be extended for parallel contingency analysis. We also validate it against the commercial power system simulators, namely PSSE and DSA Tools. Further, we compare the simulation speed of TPDSS for different numerical algorithms. The results have shown that TDPSS is accurate and also outperforms the commonly used commercial simulator PSSE in terms of its computational efficiency.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"14 1","pages":"323-332"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80103585","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: 5

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