1 / 37

Grid-enabled Research Activities in CAS

Grid-enabled Research Activities in CAS. Kai Nan Computer Network Information Center (CNIC) Chinese Academy of Sciences (CAS) Shanghai, 21 Feb 2006. Outline. I. Background CAS Informatization Program 2001-2005 CAS e-Science Initiative 2006-2010 II. Grid-enabled Research Activities

leyna
Download Presentation

Grid-enabled Research Activities in CAS

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Grid-enabled Research Activities in CAS Kai Nan Computer Network Information Center (CNIC)Chinese Academy of Sciences (CAS) Shanghai, 21 Feb 2006

  2. Outline • I. Background • CAS Informatization Program 2001-2005 • CAS e-Science Initiative 2006-2010 • II. Grid-enabled Research Activities • Middleware • Applications • III. Collaborations with EU

  3. Vision of CAS Informatization • e-Science + ARP →Digital CAS • e-Science represents Informatization of Research Activities • ARP (Academia Resource Planning) represents Informatization of Administrative Activities for Research

  4. CAS Informatization Program(2001-2005) • Major Projects • emphasis on Upgrade of Infrastructure

  5. Progress

  6. Resources • Lenovo 6800Superserver • Storage • VizWall • Scientific Data (SDB) • Science DigitalLib (CSDL)

  7. CAS e-Science Initiative2006-2010 • e-Science would be applications-driven • focus on implementation of e-Science Virtual Labs, the way for scientists to use • infrastructure may need refactoring

  8. e-Science Virtual Labs • “Virtual Labs” • special meanings in the e-Science context • the key position in our e-Science framework • the core component to make e-Science a reality

  9. vLabs Requirements • Infrastructure may be (almost) ready, but e-Science is not yet. • so many existing resources in place, but just a few could be brought into full play even now, with an advanced infrastructure ready. • bottleneck may be the gap between products by computer experts and end users of domain scientists • much more effort than expected to bridge this gap • Virtual Lab is proposed to be • a basic unit of research activity in the e-Science environment • the right user interface between scientists and their e-Science environment

  10. vLabs Goals • With Virtual Labs, • all kinds of resources could be integrated into a single access point; • customized and flexible services would be provided according to the specific requirements of different domains in an easier way than ever before; • multidisciplinary, multi-site and multi-organization collaboration could be carried out on a routine basis.

  11. Grid Middleware

  12. Scientific Database (SDB)& Scientific Data Grid (SDG) 45 institutes participated 503 databases 16.6 TB 236-CPU Superserver (1TF) 20TB Disk Array 50TB Tape Library VizWall & Access Grid

  13. Requirements and SDG • How to FIND the data I want from hundreds or thousands of databases • How to ACCESS large-scale, distributed and heterogeneous scientific data uniformly and conveniently • How to make sure all this goes always in a SECURE and proper way

  14. SDG Software Architecture

  15. Data Access Service (DAS) • Uniform Access Interface (read-only) • Rich metadata • Easy publish on web • flexible configuration and extensibility

  16. DataView Data Access Interface Virtual Database MappingBuilder Physical Database DAS modules

  17. SDG Services 中国古代天象记录（日食）数据库DataView服务 检索词：日食 天象 年号年代：康熙

  18. grid-enabled Applications

  19. e-Science applications • High Energy Physics • Astronomy • Biology • Natural Resources • Disaster Reduction • …

  20. YBJ-ARGO/AS • Italy,Japan-China cosmic ray observatories in Tibet. • 200TB raw data per year. • Data transferred to IHEP and processed with 400 CPUs. • Rec. data accessible by collaborators.

  21. YBJ-ARGO • Established a 8Mb/s link from Tibet to Beijing, by CNIC of CAS. To be upgraded to 155Mb/s soon. Stopped bringing tapes half year agao. • Building a computing system based on LCG,collaboration of IHEP of CAS, CNIC of CAS, INFN of Italia , EU-China Grid application under EU FP6 project

  22. GLORIAD

  23. LCG Tier-1/2 • to build a LCG Tier-1/2 node in China • Institute of High Energy Physics of CAS • CNIC providing support and working together with IHEP

  24. LCG2 production site @CNIC http://goc.grid.sinica.edu.tw/gstat/BEIJING-CNIC-LCG2-IA64/ Monitoring Info on BEIJING-CNIC-LCG2-IA64

  25. Chandra Whipple g-ray Oak Ridge 1.2m CO MMT SIRTF Hubble VLA Smm array Antartica submm Magellan 6.5m VO＝World Wide Telescope

  26. China Virtual Observatory at SDG Portal Grid Services Catalog Data Services Application Tools

  27. Avian Bird Flu Alarming & Predicating SystemBy: Institute of Microbiology, CAS Institute of Zoology, CAS Institute of Virology, CAS CNIC, CAS

  28. Avian Bird Flu in Gangcha, Qinghai Province, May 2005 上千支鱼鸥、棕鸥、斑头雁死亡

  29. Tasks • Integrate bird-flu basic databases from multiple institutes • Field survey on bird-flu • Establish bioinformatics comprehensive analysis system for bird-flu • Establish bird-flu alarming and predicting system • Establish international cooperative work environment • Establish information publishing system (web)

  30. Bird-flu basic databases • Standards • Bird-flu basic database’s model and data standard • Metadata specification and description language of bird-flu information • Data resources • Bird-flu virus resource database • Bird-flu virus inherent resource database • Bird-flu history database • Bird-flu dynamic monitoring database • Bird-flu host database • Bird-flu information database • Bird-flu international DNA database • Bird-flu international research progress database

  31. Model Database Model verification Model Storage Host data Model Evaluation System Survey data Virus data avian trade routes Distribute Model Survey on source Winter Survey Data SDB Predicting Technical architecture

  32. IAPProgram “Global NaturalHazards and Disaster Reduction”

  33. East Asia Resource Environment Collaborative Research Network • a network connecting a dozen of institutes and stations from China, Russia and Mongolia • a series of data products which integrate many relevant databases in this area and support application research • a platform for int’l collaborative research

  34. Global NaturalHazards and Disaster Reduction • issues in disaster reduction • Development of mechanism of major natural disaster • Prediction of major natural disaster; • Assessment of major natural disaster; • Pre-warning and emergency response of major natural disaster • Regional integrated research on major natural disaster • Database Construction & Application on “Natural Disaster Mitigation” • Disaster simulation

  35. Collaborations with EU • Ongoing • EUChinaGrid: Interconnection and Interoperability of Grids between Europe & China • Infrastructure is being better • Look forward to • further more on MIDDLEWARE & APPLICATIONS

  36. Thank you!

More Related