2012 SC Companion: High Performance Computing, Networking Storage and Analysis最新文献_第4页

Poster: Exploring Design Space of a 3D Stacked Vector Cache - Designing a 3D Stacked Vector Cache using Conventional EDA Tools 海报:探索3D堆叠矢量缓存的设计空间-使用传统EDA工具设计3D堆叠矢量缓存

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.271

Ryusuke Egawa, J. Tada, Yusuke Endo, H. Takizawa, Hiroaki Kobayashi

引用次数: 0

Poster: PanDA: Next Generation Workload Management and Analysis System for Big Data 海报:PanDA:面向大数据的新一代工作量管理与分析系统

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.302

K. De, A. Klimentov, S. Panitkin, M. Titov, A. Vaniachine, T. Wenaus, D. Yu, G. Záruba

引用次数: 3

Light-Weight Data Management Solutions for Visualization and Dissemination of Massive Scientific Datasets - Position Paper 大规模科学数据集可视化和传播的轻量级数据管理解决方案-立场文件

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.157

G. Agrawal, Yunde Su

{"title":"Light-Weight Data Management Solutions for Visualization and Dissemination of Massive Scientific Datasets - Position Paper","authors":"G. Agrawal, Yunde Su","doi":"10.1109/SC.Companion.2012.157","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.157","url":null,"abstract":"Many of the `big-data' challenges today are arising from increasing computing ability, as data collected from simulations has become extremely valuable for a variety of scientific endeavors. With growing computational capabilities of parallel machines, scientific simulations are being performed at finer spatial and temporal scales, leading to a data explosion. As a specific example, the Global Cloud-Resolving Model (GCRM) currently has a grid-cell size of 4 km, and already produces 1 petabyte of data for a 10 day simulation. Future plans include simulations with a grid-cell size of 1 km, which will increase the data generation 64 folds. Finer granularity of simulation data offers both an opportunity and a challenge. On one hand, it can allow understanding of underlying phenomenon and features in a way that would not be possible with coarser granularity. On the other hand, larger datasets are extremely difficult to store, manage, disseminate, analyze, and visualize. Neither the memory capacity of parallel machines, memory access speeds, nor disk bandwidths are increasing at the same rate as computing power, contributing to the difficulty in storing, managing, and analyzing these datasets. Simulation data is often disseminated widely, through portals like the Earth System Grid (ESG), and downloaded by researchers all over the world. Such dissemination efforts are hampered by dataset size growth, as wide area data transfer bandwidths are growing at a much slower pace. Finally, while visualizing datasets, human perception is inherently limited.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"144 1","pages":"1296-1300"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78588974","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: 1

Building a Climatology of Mountain Gap Wind Jets and Related Coastal Upwelling 山口风急流及相关海岸上升流气候学的建立

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.71

S. Graves, Xiang Li, K. Keiser, Deborah K. Smith

{"title":"Building a Climatology of Mountain Gap Wind Jets and Related Coastal Upwelling","authors":"S. Graves, Xiang Li, K. Keiser, Deborah K. Smith","doi":"10.1109/SC.Companion.2012.71","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.71","url":null,"abstract":"Winds accelerating through coastal topology are capable of generating jets that often result in cold-water upwelling events in near-coast locations. In situ measurements are frequently not available in remote locations for many of the mountain gap locations globally, so to provide a record of these events for researchers, as well as military and commercial interests, this NASA-funded project is demonstrating how remotely sensed satellite data derived products, and fused model and observations, for wind and sea surface temperatures can be used to detect both wind jet and upwelling events. An algorithm was developed to automatically detect gap wind and ocean upwelling events at gulf regions of Central America using the Cross-Calibrated, Multi-Platform (CCMP) ocean surface wind product and the Optimally Interpolated Sea Surface Temperature (OISST) product. Hierarchical thresholding and region growing methods are used to extract regions of strong winds and temperature anomalies. A post processing step further links the detected events to generate time series of these events. Though developed for Central America regions, the algorithm is being extended to apply to other coastal regions so that detected event products are globally consistent. Through collaboration with the Global Hydrology Resource Center (GHRC), a NASA Distribute Active Archive Center, this project is analyzing large climate data records to generate a resulting climatology of wind jet and upwelling events at known geographic locations will be available as a resource for other researchers. Likewise, through integration of the project's analysis techniques with the GHRC's data ingest processing, the identification and notification of new or current events will likewise be openly available to research, commercial and military users. This paper provides a report on the preliminary results of applying the team's approach of identifying and capturing events for selected mountain gap jet locations.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"324 1","pages":"495-499"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76301127","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: 0

Poster: HTCaaS: A Large-Scale High-Throughput Computing by Leveraging Grids, Supercomputers and Cloud 海报:HTCaaS:利用网格、超级计算机和云的大规模高吞吐量计算

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.176

Seungwoo Rho, Seoyoung Kim, Sangwan Kim, Seokkyoo Kim, Jik-Soo Kim, Soonwook Hwang

引用次数: 12

Reducing the De-linearization of Data Placement to Improve Deduplication Performance 减少数据放置的非线性化，提高重复数据删除性能

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.110

Yujuan Tan, Zhichao Yan, D. Feng, E. Sha, Xiongzi Ge

{"title":"Reducing the De-linearization of Data Placement to Improve Deduplication Performance","authors":"Yujuan Tan, Zhichao Yan, D. Feng, E. Sha, Xiongzi Ge","doi":"10.1109/SC.Companion.2012.110","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.110","url":null,"abstract":"Data deduplication is a lossless compression technology that replaces the redundant data chunks with pointers pointing to the already-stored ones. Due to this intrinsic data elimination feature, the deduplication commodity would delinearize the data placement and force the data chunks that belong to the same data object to be divided into multiple separate parts. In our preliminary study, it is found that the de-linearization of the data placement would weaken the data spatial locality that is used for improving data read performance, deduplication throughput and efficiency in some deduplication approaches, which significantly affects the deduplication performance. In this paper, we first analyze the negative effect of the de-linearization of data placement to the data deduplication performance with some examples and experimental evidences, and then propose an effective approach to reduce the de-linearization of data placement by sacrificing little compression ratios. The experimental evaluation driven by the real world datasets shows that our proposed approach effectively reduces the de-linearization of the data placement and enhances the data spatial locality, which significantly improves the deduplication performances including deduplication throughput, deduplication efficiency and data read performance, while at the cost of little compression ratios.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"80 5 pt 1 1","pages":"796-800"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74592308","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: 6

Software-Defined Networking for Big-Data Science - Architectural Models from Campus to the WAN 面向大数据科学的软件定义网络——从校园到广域网的架构模型

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.341

I. Monga, Eric Pouyoul, C. Guok

引用次数: 62

Poster: Evaluating Asynchrony in Gibraltar RAID's GPU Reed-Solomon Coding Library 海报:评估直布罗陀RAID的GPU Reed-Solomon编码库中的异步性

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.285

Xin Zhou, A. Skjellum, M. Curry

引用次数: 4

Abstract: Preliminary Report for a High Precision Distributed Memory Parallel Eigenvalue Solver 高精度分布式存储器并行特征值求解器初步研究

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.255

Toshiyuki Imamura, S. Yamada, M. Machida

引用次数: 1

Crayons: An Azure Cloud Based Parallel System for GIS Overlay Operations 蜡笔:一个基于Azure云的GIS叠加操作并行系统

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.315

Dinesh Agarwal

引用次数: 8