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Cellular Automaton Method for TPC Slice Tracker in HLT Reconstruction

This study presents a Cellular Automaton method for TPC slice tracker reconstruction in the ALICE HLT system. The method involves a step-by-step evolution process to efficiently reconstruct tracks from clustered data, showcasing parallelism and performance boosting techniques. The developed tracker can handle various types of data efficiently. Investigations are ongoing to improve performance further by utilizing parallel hardware.

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Cellular Automaton Method for TPC Slice Tracker in HLT Reconstruction

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  1. TPC reconstruction in the HLT S. Gorbunov1 and I. Kisel2,3 ( for the ALICE Collaboration ) 1Kirchhoff Institute for Physics, University of Heidelberg, Germany 2 Gesellschaft für Schwerionenforschung mbH, Darmstadt, Germany 3Laboratory of Information Technologies, JINR, Dubna, Russia ALICE offline week CERN, October 23, 2008

  2. TPC reconstruction scheme TPC slice 35 TPC slice 1 TPC slice 0 Cluster finder Cluster finder Cluster finder … clusters clusters clusters Slice tracker Slice tracker Slice tracker slice tracks slice tracks slice tracks TPC Global merger TPC tracks Sergey Gorbunov, KIP

  3. Tracking with the Cellular Automaton method: principles birth survival death • “Game of life” - origin of the Cellular Automatons: • A dead cell with exactly three live neighbors becomes a live cell (birth) • A live cell with two or three live neighbors stays alive (survival) • In all other cases, a cell dies or remains dead (overcrowding or loneliness) • Evolution of all the cells proceeds in parallel, generation by generation • The Cellular Automaton method for tracking, principles: • Construction of global thing (track) using only local operations (hit-to-hit linking) • Try to keep combinatorics at the local level • Try to perform calculations in parallel • Gradual complication of the calculations with complication of processed data Sergey Gorbunov, KIP

  4. Cellular Automaton - step 1: Search for neighbours For each TPC cluster find two (up&down) neighbours which compose the best line TPC clusters row 2 • Search for neighbours: • local search for each TPC cluster • cluster-wise parallelism • less combinatorics left for the further processing TPC row row 1 x y z row 0 Sergey Gorbunov, KIP

  5. Cellular Automaton - step 2: Evolution Keep only one-to-one links row 3 • Evolution step: • no combinatorics • cluster-wise parallelism row 2 row 1 x y z row 0 Sergey Gorbunov, KIP

  6. Cellular Automaton - step 3: Creation of track segments Track Endpoint Clusters => Endpoints Endpoint • Creation of the track segments: • each sequence of the neighboring clusters is composed to the track segment. • track segment-wise parallelism • no search, no combinatorics • fitting mathematics • only endpoints are left for the further reconstruction Belongs to row, array sorted in Y Endpoint object Somewhere in memory Track object Endpoint object Sergey Gorbunov, KIP

  7. Cellular Automaton - step 4: Merging of segments Empty link [dead] Merge endpoints One way link [dead] One-to-one link [alive] • Merging segment endpoints - evolution step: • local search for the best neighbor in the closest rows • endpoint-wise parallelism • competition between links ( track length, closeness of the neighbour ) • endpoints are linked one-to-one • fitting mathematics (2 check) Sergey Gorbunov, KIP

  8. Cellular Automaton: example of event reconstruction Step 0: TPC clusters Step 1: Search for neighbours Step 2: Creation of track segments Sergey Gorbunov, KIP

  9. Cellular Automaton: example of event reconstruction Step 4: Merging of segments Result: Reconstructed tracks Sergey Gorbunov, KIP

  10. Cellular Automaton: TPC slice tracker performance Soft track with 7 turns Reconstruction time for the whole TPC (36 slices) : Construction of cells … 0.5 ms Merging of cells ……... 0.3 ms Creation of segments . 0.3 ms Fit of segments ……… 0.1 ms Merging of segments . 1.8 ms Reconstruction performance: All Set (Hits >10, P > .05) Reference Set (All Set + P>1.) Extra Set (All Set + P<1.) Eff Clone Eff Clone Eff Clone Ghost Time [ms] 94.7 29.6 97.9 5.9 94.5 30.6 2.9 3.3 Sergey Gorbunov, KIP

  11. Full HLT TPC tracker performance (Slice tracker + Global merger) Reconstruction performance for the full HLT TPC tracker called from off-line ( AliTPCComparison macro ) [ done by Cvetan Cheshkov ] Sergey Gorbunov, KIP

  12. Cellular Automaton: use of parallel hardware • Hardware possibility for the parallel calculations: • SIMD CPU instructions • multi-threading • multi-core CPU • special hardware (Cell processor) CBM Sergey Gorbunov, KIP

  13. Summary and plans • Summary: • The Cellular Automaton tracker for ALICE HLT has been developed • The tracker shows good performance and speed • It reconstructs all kinds of data: Physics, Cosmics, Calibration events; with and w/o magnetic field. • Running on-line in HLT • Current work: • Investigation of parallel hardware • Speed up of the global merger • Tuning for Pb-Pb collisions p-p event Pb-Pb event in work Sergey Gorbunov, KIP

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