Future of HEP Computing

"Old Farts”:

Stu Loken

Wolfgang von Rüden

David Williams

Richard Mount

Roman Tirler (will be absent)

Tom Nash

Fresh new faces chosen by panel based on the talks at the conference:

Michael Ernst

Bob Jacobsen

Abstract

In the distant past, computing in HEP was one of a few isolated examples of fields which were extremely demanding of computing and where computing was integrated in an essential way in the day to day workings of the field. HEP was thereby rather independent of computing in the broader world. Our experiments and labs built their own software, established their own networks, and at times even built specialized processing hardware.

One cannot define a moment when this situation changed. But it is clear today that HEP computing is now very much a creature of the tremendous movements that are taking place in commercially driven computing technology, the rapid spread of computing into a ubiquitous place, whether explicit or embedded, in much of the world's daily life. This panel will focus on how this ongoing revolution in the broader world of computing will impact computing within our field.

Questions

• What is (are) the main new challenges that HEP computing faces over the next decade?
  • At least one of them will be the matter of maximizing the cohesiveness and effectiveness of huge geographically dispersed & disparate collaborations. David Williams 5-10 minutes

• How long should we anticipate the extremely rapid changes in technologies to go on and what projections do you believe are valid over the next 10 years in the following areas:

• Networks - Richard Mount

• Collaboration tools, shared data bases, etc. - Stu Loken

• Software engineering, OOPS, Java - Wolfgang von Rüden

• Processor/storage curves - Roman Tirler (shown by Nash?)

• What do you anticipate to be the impact of the changes in the wide world of computing in each of these technological areas on the HEP challenges we have identified?

• What are the problems that HEP computing will need to address by its own efforts (beyond the usual integration of commercially available components) to meet the challenges we have identified?

• What will the data geographical model be?
  • Centralized data vs partially centralized date (Jürgen Knobloch)
  • Impact on network and computing iron/storage requirements
  • What is a regional center?

• Will OO DBs be lightweight enough to be used throughout data cycle?

• Java und C++
  • What will be (should be) the place for each?
    • (When) will C++ code become legacy?
      • Will Java always be slower than C++?
    • Grad students should learn both (to be more employable)?
  • (When) are learning curves worth the pay back?
  • Should physicists learn (computing) analysis skills?
  • Role of computer scientists? Consultants?
    • How do you know you have found a good one?
  • Requirements? When in process?
    • Waterfall vs iterative development?
    • Rapid prototyping?
  • Reviews and checkpoints: electronic only?

• Process?
  • Let them code first? Will they ever design?
  • Daily, weekly, monthly build?
    • (Why) are we slower than Netscape, Microsoft release cycles?

• Will complex, do-everything programs die, evolve to Component software?
  • Word, GEANT, Experiment analysis packages
  • Chosen, focused functionality
  • What is appropriate level of component granularity?

• Future (appropriate roles) (positions) of NT vs Unix vs (Java + Browser)?

• Integration of HEP computing into HEP
  • What is trend of % cost for computing in experiments?
  • Should (can, will) computing be included in TPC (total project cost)?
    • in project work break down structure (WBS)?
  • What should we be saying about computing issues as leaders in CHEP to leaders in HEP?