hlt - data compression vs event rejection. assumptions need for an online rudimentary event...
TRANSCRIPT
Assumptions
• Need for an online rudimentary event reconstruction for monitoring
• Detector readout rate (i.e. TPC) >> DAQ bandwidth mass storage bandwidth
• Some physics observables require running detectors at maximum rate
(e.g. quarkonium spectroscopy:
TPC/TRD dielectrons; jets in p+p: TPC tracking)
• Online combination of different detectors can increase selectivity of triggers
(e.g. jet quenching: PHOS/TPC high-pT - jet
events)
Data volume and event rate
TPC detector
data volume = 300 Mbyte/event
data rate = 200 Hz
front-end electronics
DAQ – event building
Level-3 system
permanent storage system
bandwidth
60 Gbyte/sec
15 Gbyte/sec
< 1.2 Gbyte/sec
< 2 Gbyte/sec
HLT tasks
• Online (sub)-event reconstruction– optimization and monitoring of detector
performance
– monitoring of trigger selectivity
– fast check of physics program
• Data rate reduction– data volume reduction
• regions-of-interest and partial readout
• data compression
– event rate reduction• (sub)-event reconstruction and event rejection
• p+p program– pile-up removal
– charged particle jet trigger, etc.
Data rate reduction
• Volume reduction– regions-of-interest and partial
readout– data compression
• entropy coder
• vector quantization
• TPC-data modeling
• Rate reduction– (sub)-event reconstruction and event
rejection before event building
Regions-of-interest and partial readout
• Example: selection of TPC sector and -slice based on TRD track candidate
Data compression:Entropy coder
Variable Length Codingshort codes for long codes forfrequent values infrequent values
Results: NA49: compressed event size = 72% ALICE: = 65%
(Arne Wiebalck, diploma thesis, Heidelberg)
Probability distribution of 8-bit TPC data
Data compression:Vector quantization
• Sequence of ADC-values on a pad = vector:
• Vector quantization = transformation of vectors into codebook entries
• Quantization error:
Results: NA49: compressed event size = 29 %ALICE: = 48%-64%(Arne Wiebalck, diploma thesis, Heidelberg)
codebook
compare
Data compression: TPC-data modeling
• Fast local pattern recognition:
Result: NA49: compressed event size = 7 %
analytical cluster model
quantization of deviations from track and cluster
model
local track parameters
comparison to raw data
simple local track model (e.g. helix) track parameters
• Track and cluster modeling:
Fast pattern recognition
Essential part of Level-3 system
– crude complete event reconstruction
monitoring
– redundant local tracklet finder for cluster evaluation
efficient data compression
– selection of (,,pT)-slices
ROI
– high precision tracking for selected track candidates jets, dielectrons, ...
Fast pattern recognition
• Sequential approach– cluster finder, vertex finder and track
follower• STAR code adapted to ALICE TPC
– reconstruction efficiency
– timing results
• Iterative feature extraction– tracklet finder on raw data and cluster
evaluation• Hough transform
Level-3 system architecture
TPCsector
#1
TPCsector#36
TRD ITS XYZ
local processingsubsector/sector
global processing I(2x18 sectors)
global processing II(detector merging)
global processing III(event reconstruction)
ROI
data compr.
event rejection
monitoring
Lev
el-3
trig
ger
momentumfilter
TPC on-line trackingAssumptions:• Bergen fast tracker• DEC Alpha 667 MHz • Fast cluster finder excluding cluster deconvolutionNote: This cluster finder is sub optimal for the inner sectors and additional work is required here. However in order to get some estimate the computation requirements were based on the outer pad rows. It should be noted that the possibly necessary deconvolution in the inner padrows may require comparably more CPU cycles.
TPC L3 Tracking estimate:• Cluster finder on pad row of the outer sector
5 ms• tracking of all (monte carlo) space points for one TPC sector
600 msNote - this data may not include realistic noise - tracking to first order is linear with the number of tracks provided there are few overlaps - assuming one ideal processor below• Cluster finder on one sector (145 padrows)
725 ms• Process complete sector
1,325 s• Process complete TPC
47,7 s• Running at maximum TPC rate (200 Hz), January 2000 9540 CPUs• Assuming 20% overhead
11500 CPUs (parallel computation, network transfer, inner sector additional overhead, sector merging etc.)• Moores Law (60%/a) @ 2006 – 1a commission x10,5
1095 CPUs