High Energy Physics Data Management Richard P. Mount Stanford Linear Accelerator Center DOE Office...
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Transcript of High Energy Physics Data Management Richard P. Mount Stanford Linear Accelerator Center DOE Office...
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?)
– 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
The Science (2)From the Fermilab Web
• Research at Fermilab will address the grand questions of particle physics today.
– Why do particles have mass? – 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?
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)
BaBar Experiment at SLACTaking data since 1999.
Now at 1 TB/day rising rapidly
Over 1 PB in total.
Matter-antimatter asymmetry
Understanding the origins of our universe
Characteristics of HENP Experiments1980 – present
Typical data volumes: 10000n tapes(1 n 20)
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
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
EventEvent
TrackListTrackList
TrackerTracker Calor.Calor.
TrackTrackTrackTrackTrackTrack
TrackTrackTrackTrack
HitListHitList
HitHitHitHitHitHitHitHitHitHit
Dirk Düllmann/CERN
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.
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
Random-Access Storage Performance
0.000000001
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PC2100
WD200GB
STK9940B
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Latency and Speed – Random Access
Historical Trends in Storage Performance
0.000000001
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log10 (Object Size Bytes)
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MB
yte
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PC2100
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STK9940B
RAM 10 years ago
Disk 10 years ago
Tape 10 years ago
0 1 2 3 4 5 6 7 8 9 10
Latency and Speed – Random Access
Storage Characteristics – CostStorage Hosted on Network Cost per PB ($M)
net after RAID, hot spares etc.
Cost per GB/s ($M)Streaming
Random access to typically accessed objects
Cost per GB/s ($M)
Object Size
Good Memory * 750 0.001 0.018 4 bytes
Cheap Memory 250 0.0004 0.006 4 bytes
Enterprise SAN maxed out 40 0.4 8 5 kbytes
High-quality fibrechannel disk * 10 0.1 2 5 kbytes
Tolerable IDE disk 5 0.05 1 5 kbytes
Robotic tape (STK 9480C) 1 2 25 500 Mbytes
Robotic tape (STK 9940B) * 0.4 2 50 500 Mbytes
* Current SLAC choice
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)
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?
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”
Client Client Client Client Client Client
Disk Server
Disk Server
Disk Server
Disk Server
Disk Server
Disk Server
Tape Server
Tape Server
Tape Server
Tape Server
Tape Server
Generic Storage Architecture
Client Client Client Client Client Client
Disk Server
Disk Server
Disk Server
Disk Server
Disk Server
Disk Server
Tape Server
Tape Server
Tape Server
Tape Server
Tape Server
SLAC-BaBar Storage Architecture
IP Network (Cisco)
IP Network (Cisco)
120 dual/quad CPU Sun/Solaris300 TB Sun FibreChannel RAID arrays
1500 dual CPU Linux 900 single CPU Sun/Solaris
25 dual CPU Sun/Solaris40 STK 9940B6 STK 9840A6 STK Powderhornover 1 PB of data
Objectivity/DB object database + HEP-specific ROOT software
HPSS + SLAC enhancements to Objectivity and ROOT server code
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
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
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