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 InfrastructureItemBy 20002005 Networking core 1Gbps2.5Gbps backbone 2MbpsN*155M+2.5G Oversea link 55Mbps620M+12G HPC Peak TFLOPS 0.135.5 Linpack TFLOPS 0.054.3 Storage 2.1TB182TB Scientific Database Member institutes 21>45 Databases 180400+ Data volume 725GB15TB+

6. Resources Lenovo 6800 Superserver Storage VizWall Scientific Data (SDB) Science Digital Lib (CSDL)

7. CAS e-Science Initiative 2006-2010 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. DAS modules Data Access Interface Virtual Database Physical Database MappingBuilder DataView

17. DataView SDG Services

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

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. Chandr a Hubble MMT Smm array VLA Antartica submmMagellan 6.5m Whipple -ray SIRTF Oak Ridge 1.2m C O VO World Wide Telescope

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

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

28. Avian Bird Flu in Gangcha, Qinghai Province, May 2005

30. 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)

31. Bird-flu basic databases Standards –Bird-flu basic databases 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

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

33. IAPProgram Global Natural Hazards and Disaster Reduction IAP Program Global Natural Hazards and Disaster Reduction

34. 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 intl collaborative research

35. Global Natural Hazards 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

36. 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

37. Thank you!