OO Software and Data Handling in AMS

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OO Software and Data Handling in AMS
Vitali Choutko, Alexei Klimentov
MIT, ETHZ

• Computing in High Energy and Nuclear Physics
• Beijing , September 3-7 , 2001

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Outline

• AMS particle physics experiment on the
  international space station
• Data flow and AMS ground centers
• Software development
• Conditions and Tag Database
• Data Processing

• AMS Detector
• STS91 precursor flight
• AMS ISS mission

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AMS a particle physics experiment in space
PHYSICS GOALS
Accurate, high statistics measurements of
charged, cosmic ray spectra in space gt 0.1GV

• The study of dark matter (90 ?)

Nuclei and e- spectra measurement

• Determination of the existence or absence of
  antimatter in the Universe

Look for negative nuclei

• The study of the origin and composition of
  cosmic rays

Measure isotopes D, He, Li, Be
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Precursor flight
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10 events recorded Trigger rates 0.1-1kHz DAQ
lifetime 90
Results
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• Anti-matter search
• He / He 1.1x 10
• Charged Cosmic Ray spectra
• Pr, D, e- , He, N
• Geomagnetic effects on CR
• under/over geomagnetic cutoff components

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Magnet Nd2Fe14B TOF trigger, velocity and
Z Si Tracker charge sign, rigidity, Z Aerogel
Threshold Cerenkov velocity Anticounters
reject multi particle events
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AMS on ISS , 3 years in space
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Separate e- from p,p
up to 300 GeV
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He, He, B, C

e-,? up to 1000 GeV
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ISS to Remote AMS Centers Data Flow
White Sand, NM facility
AMS
Real-time Dump data
Real-time, Dump, White Sands
LOR playback
Payload Operations Control Center
MSFC, Al
Monitoring science data
Real-time dump
Payload Data Service system
HS Monitoring Science Flight ancillary data
Real-time Data HS
ACOP
External Communications
Short Term
GSE
Stored data
Long Term
NearReal-time
Science Operations Center
File transfer
High Rate Frame MUX
playback
NASA Ground Infrastructure
Telescience centers
Remote AMS Sites
ISS
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RT data Commanding Monitoring NRT Analysis
POIC_at_MSFC AL
POCC
POCC
HOSC Web Server and xterm
XTerm
commands
Monitoring, HS data Flight Ancillary data AMS
science data (selected)
cmds archive
TReK WS
Science Operations Center
Science Operations Center
voiceloop
TReK WS
Video distribution
External Communications
PC Farm
GSE
GSE
NRT Data Processing Primary storage
Archiving Distribution Science Analysis
AMS Data, NASA data, metadata
Buffer data Retransmit To SOC
Production Farm
GSE
MC production
D S A e T r A v e r
AMS Remote center
Analysis Facilities
Data Server
MC production Data mirror archiving
Analysis Facilities
AMS Station
AMS Station
AMS Station
AMS Ground Centers
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AMS SW development

• Been started mid 1996
• basic decisions
• new code C only (though we had a large part of
  legacy SW written on Fortran)
• Existing libraries (CERNLIB, Geant, etc)
  incorporated via C/Fortran interface (R.Burow)
• transient and persistent classes are separated
  with implementing of copy member functions
• Decide to use Root and HBOOK for histogramming
  and data visualization

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AMS SW development (contd)

• Use different persistency solutions for various
  type of data

• Flat files for the raw data
• Ntuples and Root files for ESD
• Relational Database (Oracle) tables for file
  catalogues
• Relational Database (Oracle) Objectivity up to
  Sep 1998

• Event Tags
• Calibration data
• Slow control data
• NASA ancillary data
• Various catalogues (processing history, etc)

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Tag Storage with Oracle RDBMS

• Tag is an unsigned 32 bit integer containing
  16, 1 to 5 bit long parameters such as charge,
  momentum sign, ß,
• Model
• Query retrieve tags with 3 parameters satisfied
  to the given limits (query taken from the real
  analysis chain)
• Data stored on Raid array connected to AS4100
  (quad-CPU rated at 600MHz, 2GB RAM)

• Flat files 2400 files, one file per DAQ run,
  tags are stored as an array of unsigned int.
• RootN - 10 files, each file with 240 trees,
  one tree per DAQ run with single branch (tag) per
  tree
• RootS - 10 files, each file with 240
  trees, one tree per DAQ run , having 16
  branches,
• every parameter stored in a
  dedicated branch
• OracleN - table with 10 partitions and 1
  column, mapping tag to a column
• OracleI - table with 10 partitions and 1
  column with 16 bitmap indices, mapping tag to a
  column
• OracleS table with 10 partitions and 16
  columns, every parameter mapping to a column

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Oracle RDBMS to store AMS tags
1)
1) 500 sec to build indices for 100M tags
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Design of the Conditions Database

• Collection of Time Dependent Values (TDVs)
• Primary access keys name, id, validity interval
• Secondary key insert time
• Major Components table of names and ids,
  default TDVs, TDVs
• Applications
• Loading data into
  database
• Fetching conditions
  during event reconstruction
• Management utilities (TDV
  browser)

• Name, id
• Validity begin, validity end time
• Insert time
• Array of unsigned integers (size 100 byte 8
  Mbyte)

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AMS Conditions Database

• Initially Objectivity, then flat files, now
  Oracle
• Performance test for

• TOF temperature (many short records)
• Tracker pedestals (small amount of large records)

(a) BLOB array is stored inside the table, (b) -
outside
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Oracle RDBMS to store Tags and TDVs

• Currently 8 Gbyte is stored in the Conditions DB
  (115 different TDV types)
• 100 million event tags are stored in Tag DB
• Oracle RDBMS performance and functionality
  satisfy AMS requirements. Using of bitmap indices
  for tags improves query time dramatically.
• The current implementation works with distributed
  CORBA technology. It allowes to reduce the number
  of database clients and machine loading.

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Nominal Tables
Hosts, Interfaces Producers, Servers
II
I
VI

• I submit 1st server
• II cold start
• III read active tables (available hosts,
  number of servers, producers, jobs/host)
• IV submit servers
• V get runinfo (runs to be processed, ESD
  output path)
• VI submit producers (LILO, LIRO,RIRO)
• Notify servers

server
server
producers
III
ESD

Active Tables Hosts, Interfaces, Producers,
Servers
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ESD
Raw data
server
server
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VI
Conditions DB
server
server

server
Tag DB
server
producers
server
ESD
Catalogues
ESD
ESD
Oracle RDBMS
Raw data
AMS Production
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AMS Production Highlights

• Stable running for more than 1 month
• Average efficiency 95 (98 without Oracle)
• Processes communication and control via Corba
• LSF for process submission
• Run Oracle server on AS4100 Alpha and Oracle
  clients on Linux.
• Oracle RDBMS

• Tag DB with 100M entries
• Conditions DB with 100K entries
• Bookkeeping

• Production status
• Runs history
• File catalogues