1. HLT - data compression vs event rejection

2. 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-p T  - jet events)

3. 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

4. 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.

5. 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

6. TPC event (only about 1% is shown)

7. Regions-of-interest and partial readout Example: selection of TPC sector and  -slice based on TRD track candidate

8. Data compression: Entropy coder Variable Length Coding short codes for long codes for frequent values infrequent values Results: NA49: compressed event size = 72% ALICE: = 65% ( Arne Wiebalck, diploma thesis, Heidelberg) Probability distribution of 8-bit TPC data

9. 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) code book compare

10. 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:

11. 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 ( , ,p T )-slices  ROI –high precision tracking for selected track candidates  jets, dielectrons,...

12. 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

13. Fast cluster finder (1) timing: 5ms per padrow

14. Fast cluster finder (2)

15. Fast cluster finder (3) Efficiency Offline efficiency

16. Fast vertex finder Resolution Timing result: 19 msec on ALPHA (667 MHz)

17. Fast track finder Tracking efficiency

18. Fast track finder Timing results

19. Hough transform (1) Data flow

20. Hough transform (2)  -slices

21. Hough transform (3) Transformation and maxima search

22. Level-3 system architecture TPC sector #1 TPC sector #36 TRDITSXYZ local processing subsector/sector global processing I (2x18 sectors) global processing II (detector merging) global processing III (event reconstruction) ROI data compr. event rejection monitoring Level-3 trigger momentum filter

23. TPC on-line tracking Assumptions: Bergen fast tracker DEC Alpha 667 MHz Fast cluster finder excluding cluster deconvolution Note: 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 ms Note- 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