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High Energy Physics Data Management

High Energy Physics Data Management. Richard P. Mount Stanford Linear Accelerator Center DOE Office of Science Data Management Workshop, SLAC March 16-18, 2004. The Science (1). Understand the nature of the Universe (experimental cosmology?)

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High Energy Physics Data Management

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  1. High Energy Physics Data Management Richard P. Mount Stanford Linear Accelerator Center DOE Office of Science Data Management Workshop, SLAC March 16-18, 2004

  2. The Science (1) • Understand the nature of the Universe (experimental cosmology?) • BaBar at SLAC (1999 on): measuring the matter-antimatter asymmetry • CMS and Atlas at CERN (2007 on): understanding the origin of mass and other cosmic problems

  3. The Science (2)From the Fermilab Web • Research at Fermilab will address the grand questions of particle physics today. • Why do particles havemass? • Does neutrino mass come from a different source? • What is the true nature of quarks and leptons? Why are there three generations of elementary particles? • What are the truly fundamental forces? • How do we incorporate quantum gravity into particle physics? • What are the differences between matter and antimatter? • What are the dark particles that bind the universe together? • What is the dark energy that drives the universe apart? • Are there hidden dimensions beyond the ones we know? • Are we part of a multidimensional megaverse? • What is the universe made of? • How does the universe work?

  4. Experimental HENP • Large (500 – 2000 physicist) international collaborations • 5 – 10 years accelerator and detector construction • 10 – 20 years data-taking and analysis • Countable number of experiments: • Alice, Atlas, BaBar, Belle, CDF, CLEO, CMS, D0, LHCb, PHENIX, STAR … • BaBar at SLAC • Measuring matter-antimatter asymmetry (why we exist?) • 500 Physicists • Data taking since 1999 • More data than any other experiment (but likely to overtaken by CDF, D0 and STAR soon and will be overtaken by Alice, Atlas and CMS later)

  5. Hydrogen Bubble Chamber Photograph 1970 CERN Photo

  6. UA1 Experiment, CERN 1982: Discovery of the W Boson (Nobel Prize 1983) CERN Photo

  7. BaBar Experiment at SLAC Taking data since 1999. Now at 1 TB/day rising rapidly Over 1 PB in total. Matter-antimatter asymmetry Understanding the origins of our universe

  8. CMS Experiment: “Find the Higgs”~10 PB/year by 2010

  9. Characteristics of HENP Experiments1980 – present Large, complex  Large, (approaching worldwide)detectors collaborations: 500 – 2000 physicists Long (10 – 20 year) timescales God does play dice High statistics (large volumes of data) needed for precise physics  Typical data volumes: 10000n tapes (1  n  20)

  10. Event Tracker Calor. TrackList HitList Track Hit Track Hit Track Hit Track Hit Track Hit HEP Data Models • HEP data models are complex! • Typically hundreds of structure types (classes) • Many relations between them • Different access patterns • Most experiments now rely on OO technology • OO applications deal with networks of objects • Pointers (or references) are used to describe relations Dirk Düllmann/CERN

  11. Today’s HENP Data Management Challenges • Sparse access to objects in petabyte databases: • Natural object size 100 bytes – 10 kbytes • Disk (and tape) non-streaming performance dominated by latency • Approach - current: • Instantiate richer database subsets for each analysis application • Approaches – possible • Abandon tapes (use tapes only for backup, not for data-access) • Hash data over physical disks • Queue and reorder all disk access requests • Keep the hottest objects in (tens of terabytes of) memory • etc.

  12. Today’s HENP Data Management Challenges • Millions of Real or Virtual Datasets: • BaBar has a petabyte database and over 60 million “collections”. (lists of objects in the database that somebody found relevant) • Analysis groups or individuals create new collections of new and/or old objects • It is nearly impossible to make optimal use of existing collections and objects

  13. Latency and Speed – Random Access

  14. Latency and Speed – Random Access

  15. Storage Characteristics – Cost * Current SLAC choice

  16. Storage-Cost Notes • Memory costs per TB are calculated: Cost of memory + host system • Memory costs per GB/s are calculated: (Cost of typical memory + host system)/(GB/s of memory in this system) • Disk costs per TB are calculated: Cost of disk + server system • Disk costs per GB/s are calculated: (Cost of typical disk + server system)/(GB/s of this system) • Tape costs per TB are calculated: Cost of media only • Tape costs per GB/s are calculated: (Cost of typical server+drives+robotics only)/(GB/s of this server+drives+robotics)

  17. Storage Issues • Tapes: • Still cheaper than disk for low I/O rates • Disk becomes cheaper at, for example, 300MB/s per petabyte for random-accessed 500 MB files • Will SLAC every buy new tape silos?

  18. Storage Issues • Disks: • Random access performance is lousy, independent of cost unless objects are megabytes or more • Google people say: “If you were as smart as us you could have fun building reliable storage out of cheap junk” • My Systems Group says: “Accounting for TCO, we are buying the right stuff”

  19. Generic Storage Architecture Disk Server Disk Server Disk Server Disk Server Disk Server Disk Server Tape Server Tape Server Tape Server Tape Server Tape Server Client Client Client Client Client Client

  20. SLAC-BaBar Storage Architecture Objectivity/DB object database + HEP-specific ROOT software Disk Server Disk Server Disk Server Disk Server Disk Server Disk Server Tape Server Tape Server Tape Server Tape Server Tape Server Client Client Client Client Client Client 1500 dual CPU Linux 900 single CPU Sun/Solaris IP Network (Cisco) 120 dual/quad CPU Sun/Solaris300 TB Sun FibreChannel RAID arrays IP Network (Cisco) HPSS + SLAC enhancements to Objectivity and ROOT server code 25 dual CPU Sun/Solaris40 STK 9940B6 STK 9840A6 STK Powderhornover 1 PB of data

  21. Quantitatively (1) • Volume of data per experiment: • Today: 1 petabyte • 2009: 10 petabytes • Bandwidths: • Today: ~1 Gbyte/s (read) • 2009 (wish): ~1 Tbyte/s (read) • Access patterns: • Sparse iteration, 5kbyte objects • 2009 (wish): sparse iteration/random, 100 byte objects

  22. Quantitatively (2) • File systems: • Fundamental unit is an object (100 – 5000 bytes) • Files are WORM containers, of arbitrary size, for objects • File systems should be scalable, reliable, secure and standard • Transport and remote replication: • Today: A data volume equivalent to ~100% of all data is replicated, more-or-less painfully, on another continent • 2009 (wish): painless worldwide replication and replica management • Metadata management: • Today: a significant data-management problem (e.g 60 million collections) • 2009 (wish): miracles

  23. Quantitatively (3) • Heterogeneity and data transformation: • Today: not considered an issue … 99.9% of the data are only accessible to and intelligible by the members of a collaboration • Tomorrow: we live in terror of being forced to make data public (because it is unintelligible and so the user-support costs would be devastating) • Ontology, Annotation, Provenance: • Today: we think we know what provenance means • 2009 (wish): • Know the provenance of every object • Create new objects and collections making optimal use of all pre-existing objects

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