1. 05/14/04Larry Dennis, FSU1 Scale of Hall D Computing CEBAF provides us with a tremendous scientific opportunity for understanding one of the fundamental forces of nature. 100 MB/s 1 GB/s

2. 05/14/04Larry Dennis, FSU1 Requirements Summary

3. 05/14/04Larry Dennis, FSU1 Some comparisons: Hall D vs. other HENP Not just an issue of equipment. These experiments all have the support of large dedicated computing groups within the experiments well defined computing models 100200 0002001001 - 3000Hall D ~240 (CLAS) 80002510-22120JLAB– current ~500300BaBar ~500300 D0/CDF Run II ~3007000>2040200STAR ~500 (?)100 000100 300 US Atlas (Tier 1) ~1800500 0005001002 000 (total)CMS PeopleCPU SI95/year Disk Cache TB Data rates MB/s Data Volumes (tape) TB/year

4. 05/14/04Larry Dennis, FSU1 Hall D Computing Tasks First Pass Analysis Data Mining Physics Analysis Partial Wave Analysis Physics Analysis Acquisition Monitoring Slow Controls Data Archival Planning Simulation Publication Calibrations

5. 05/14/04Larry Dennis, FSU1 Hall D Software We must accomplish all of the tasks other experiments must also accomplish. Data Acquisition, Slow Controls, Calibration, Reconstruction, Simulation, Physics Analysis, etc. Need to make a dramatic improvement in what we can do with limited resources.

6. 05/14/04Larry Dennis, FSU1 Goals for the Computing Environment 1. The experiment must be easy to conduct (coded for software people  system must automated sufficiently so that only two people are required to run experiment). 2. Everyone can participate in solving experimental problems – no matter where they are located. 3. Calibration effort can keep up with data acquisition. 4. Offline analysis can more than keep up with the online acquisition (factor of 2?). continued…

7. 05/14/04Larry Dennis, FSU1 Goals for the Computing Environment 1. Simulations can more than keep up with the online acquisition (factor of 10?). 2. Production of tracks/clusters from raw data and simulations can be planned, conducted, monitored, validated and used by a group. 3. Production of tracks/clusters from raw data and simulations can be conducted automatically with group monitoring. 4. Subsequent analysis can be done automatically, with group participation if individuals so choose.

8. 05/14/04Larry Dennis, FSU1 Meeting the Hall D Computational Challenges Moore’s law: Computer performance increases by a factor of 2 every 18 months. Gilder’s Law: Network bandwidth triples every 12 months. Solving the information management problems requires people working on the software and developing a workable computing environment. Dennis’ Law: Neither Moore’s Law nor Gilder’s Law will solve our computing problems.

9. 05/14/04Larry Dennis, FSU1 Limiting Resources? A. Money. B. Data Acquisition. C. CPU Speed. D. Network Speed. E. Storage Capacity. F. People. Which of the following resources is (are) likely to be the rate limiting resource(s)?

10. 05/14/04Larry Dennis, FSU1 How Can We Significantly Advance Computing Technology Provide an extremely efficient working environment. Focus on making physicists more efficient. Workflow and organization. Getting them the information they need when they need it. Automate, Automate, Automate! Do we need bleeding-edge software engineering? Do we need the most optimized codes? We need to try to make the case for more efficient performance later on by software innovations now.

11. 05/14/04Larry Dennis, FSU1 Critical Role for Computing in Hall D The quality of Hall D science depends critically upon the collaboration’s ability to conduct it’s computing tasks.

12. 05/14/04Larry Dennis, FSU1 Efficient Information Access is Key to HallD Success Data Acquisition  Raw Data,  Experimental Conditions CalibrationsSimulationsData ReductionPhysics AnalysisPWA Information From Researchers Hall D Experimental Information