1. Applications Using the EGEE Grid Infrastructure
C. Loomis (CNRS/LAL)
Planning for Grids in SA
Meraka Institute, Pretoria, SA
12-13 May 2008

2. Contents Introduction Scientific Disciplines on the Grid
Grid Functionality
Highlighted Applications
Project Evolution
Conclusions
Grid Apps. — C. Loomis — May 2008

3. Introduction Grid: A hardware and software infrastructure… Federation
Sharing and coherent use of distributed resources.
Use of diverse resources (CPU, storage, DBs, infiniband, …).
Collaboration
Platform to bring different people with different skills together.
Mechanism to analyze, publish, and combine previous results.
Goal: Adoption and use by large number of users in wide spectrum of different scientific disciplines.
Grid Apps. — C. Loomis — May 2008

4. Scientific Disciplines
Growth and diversification of applications.
Reported apps. only  underestimate!
6/2006
2/2007
1/2008
Astron. & Astrophysics 2 8 9
Computational Chemistry 6 27 21
Earth Science 16 18
Fusion 3 4
High-Energy Physics 11 7
Life Sciences 23 39 37
Others 14
Total 62
118
117
Condensed Matter Physics
Comp. Fluid Dynamics
Computer Science/Tools
Civil Protection
Finance
Grid Apps. — C. Loomis — May 2008

5. Growing Usage ~22K CPUs in continuous use Usage doubled this last year
Grid Apps. — C. Loomis — May 2008

6. Usage by Scientific Discipline
Wide (natural) differences in total CPU utilization.
Continued heavy use in HEP and LS.
Average 20x increase in other areas!
Evidence of broad adoption of grid technology.
2006
2007
TOTAL
Astron. & Astrophysics 58
1272
1331
Comp. Chem.
347
7389
7736
Earth Science 32
255
287
Fusion 17
1369
1386
High-Energy Physics
7033 5
166110
236444
Life Sciences
9073
9666
18739
Others
2498
10112
12610
8236 1
196173
278533
Grid Apps. — C. Loomis — May 2008

7. Summary of Use Large, growing overall utilization
Long-term, habitual use of infrastructure.
Broad adoption
Growing number of Virtual Organizations ( ≈ experiments).
Uptake in broad spectrum of disciplines.
Grid Apps. — C. Loomis — May 2008

8. Functionality EGEE core middleware = gLite Job management
Gatekeeper, Workload Mgt. System
GridWay, GANGA/DIANE
Data management:
SRM, FTS, LFC
SRB, …
Metadata management
AMGA
VO management
VOMS (user and group management, authorization)
Grid Apps. — C. Loomis — May 2008

9. CERN LHC 9 km © CERN Geneva © CERN Geneva
Grid Apps. — C. Loomis — May 2008

10. ATLAS Experiment 7000 tons 20 m 40 m ATLAS Image: ATL-PHO-GEN-2002-002
Grid Apps. — C. Loomis — May 2008

11. High Energy Physics Data Rate: Data Volume:
40 MHz interaction rate
100 Hz of filtered events
1-10 megabytes per filtered event
0.1-1 gigabytes/second
Data Volume:
LHC runs 24/7.
Generates petabytes of data per year!
Plus 1-10 times that in simulated data.
Data management is the real challenge for LHC.
Recording and retrieval.
Metadata management for locating interesting data.
Chaotic analysis and large productions.
kilo- K
103
mega- M
106
giga- G
109
tera- T
1012
peta- P
1015
exa- E
1018
Grid Apps. — C. Loomis — May 2008

12. Demanding Data Flow Real data: Analyzed data: Simulated data:
CERN to others
Analyzed data:
All to all
Simulated data:
Jürgen Knobloch
Grid Apps. — C. Loomis — May 2008

13. GEOSCOPE GEOSCOPE (http://geoscope.ipgp.jussieu.fr/)
Localization of earthquakes
Determination of rupture modes of the faults
Results within a few hours of major earthquakes
date = 6 Jan. 2008
time = 5:14:17
depth = 50.9 km
magnitude = 6.1
latitude = °
longitude = °
Grid Apps. — C. Loomis — May 2008

14. Noise Determination Process complete data set on EGEE
25 years of data
28 seismological stations and data center
Impact on seismological data center design
Grid Apps. — C. Loomis — May 2008

15. Evolution Evolve from R&D projects to service infrastructure.
Continuous evolution since European DataGrid (2001).
EGEE-III starts 1 May 2008 for 2 years.
Planned switch to EGI after EGEE-III.
Focus in EGEE-III
Support, Community Building, Advanced Functionality
Grid Apps. — C. Loomis — May 2008

16. Classic Support Direct User Support VO Support
Use GGUS system; grid ticketing system.
Provide team for handling tickets.
Documentation management and generation.
VO Support
Registration.
Resource allocation.
Better tools for VO management (user mgt., communication, …).
Application Porting Support
GILDA (INFN): porting/training using t-Infrastructure
GASuC (SZTAKI): porting to production grid infrastructure
Grid Apps. — C. Loomis — May 2008

17. Community Building Build strong, self-reliant user communities.
Discipline-specific meetings
Techniques to aid each discipline (common data, tools, etc.)
Dissemination within that discipline
Topical Meetings
Discuss common problems or needs
Highlight tools/techniques to address those needs
Grid Apps. — C. Loomis — May 2008

18. Community Building Conferences = Knowledge Transfer
Present results from using grid technology.
Discuss encountered problems and solutions.
Increase interactions between users.
EGEE’07
(Budapest)
UF1
(CERN)
EGEE’06 (Geneva)
UF3
(Clermont- Ferrand)
UF2-OGF20 (Manchester)
Grid Apps. — C. Loomis — May 2008

19. Conclusions Scientists use the EGEE grid: Long-term goals:
Routinely and heavily to speed and to enhance their analyses,
To federate (share) their resources, and
To collaborate effectively.
Long-term goals:
Provide infrastructure and full range of support services.
Have grid technology routinely used in scientific community.
Build strong, self-reliant user communities.
Grid Apps. — C. Loomis — May 2008