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ALICE HLT High Speed Tracking and Vertexing

ALICE HLT High Speed Tracking and Vertexing. Sergey Gorbunov 1,2. 1 Frankfurt Institute for Advanced Studies, University of Frankfurt, Germany 2 Kirchhoff Institute for Physics, University of Heidelberg, Germany. Real-Time 2010 Conference Lisboa, May 25, 2010.

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ALICE HLT High Speed Tracking and Vertexing

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  1. ALICE HLT High SpeedTracking and Vertexing Sergey Gorbunov1,2 1Frankfurt Institute for Advanced Studies, University of Frankfurt, Germany 2Kirchhoff Institute for Physics, University of Heidelberg, Germany Real-Time 2010 Conference Lisboa, May 25, 2010

  2. HLT event reconstruction scheme (main trackers) The TPC Sector Tracker is the most complicated algorithm: • combinatorial search • fit mathematics • the reconstruction time is crucial Sergey Gorbunov, FIAS

  3. TPC sector HLT TPC Sector Tracker: The Cellular Automaton method 2. Evolution Neighbours finder: 3. Other steps • fit, search for missed hits, and the final track selection • For each TPC cluster it finds two (up&down) neighbours which compose the best line • non-reciprocal links removed • one-to-one linked clusters are compose track segments row k+1 row k row k-1 Sergey Gorbunov, FIAS

  4. HLT TPC Tracker performance (Sector Tracker + Global Merger) on MC 99.94% 99.86% pp, HLT pp, Offline 9.30% 9.06% 0.21% 0.19% 95.84% 98.15% central PbPb, HLT central PbPb, Offline 13.22% 12.13% 1.66% 1.40% Sergey Gorbunov, FIAS

  5. HLT TPC Tracker performance (Sector Tracker + Global Merger) on MC Performance on Monte Carlo MC, 14 TeV pp events: HLT Tracker : Time = 19.6 ms Eff = 99.86% Ghost = 0.19% Clone = 9.06% Offline Tracker : Time = 66.0 ms Eff = 99.94% Ghost = 0.21% Clone = 9.30% ~ linear time dependence: MC, 5 TeV Central PbPb events: HLT Tracker : Time = 17.6 s Eff = 98.15% Ghost = 1.66% Clone = 13.22% Offline Tracker : Time = 160.1 s Eff = 95.84% Ghost = 1.40% Clone = 12.13% Sergey Gorbunov, FIAS

  6. Real PP Event in the HLT (2009 data) tracks vertex-fitted tracks primary vertex Sergey Gorbunov, FIAS

  7. HLT V0’s: PP run 00010480 Monitoring of V0 physics on-line • HLT V0 finder • Gamma, Ks, Lambda analysis Sergey Gorbunov, FIAS

  8. Use of parallel hardware: GPU devices NVIDIA GeForce GTX 280: • 30x8 general propose processors; pure calculations can be ~100 times faster than CPU • very parallel: || execution of branches, || memory access • CUDA language - a little extension of C++ • fast access to the small portion of data (16k) at the time; no memory cache • single precision floating point • ONLY parallel calculations Sergey Gorbunov, FIAS

  9. Running the sector tracker on the GPU cluster at Frankfurt University • speed-up: 10.5x GPU CPU • same code • same result CPU GPU Sergey Gorbunov, FIAS

  10. HLT Tracker - Summary • HLT Tracker - Summary: • The ALICE HLT tracker shows good performance and speed. • It is able to use the GPU hardware, showing ~10 times speed-up with comparison to CPU. • The HLT was running well in 2009 performing the full on-line event reconstruction, which includes monitoring of the events, the vertex position, and v0 physics. • In work: • Installing the GPU hardware. • Further speed-up of the tracker. • Speed-up of the rest of the HLT reconstruction software for heavy ions (clusterfinders, vertex finders, v0, ITS tracker, …). Sergey Gorbunov, FIAS

  11. HLT vertexer: the Silicon Pixel Detector High-Speed Vertexing in HLT Silicon Pixel Detector (SPD) • The innermost ALICE detector • Two layers of silicon at ~4cm and ~8cm • Pixel measurements (XYZ) The SPD detector can provide stand-alone event vertex, which is useful for on-line monitoring of the ALICE interaction point. Sergey Gorbunov, FIAS

  12. HLT SPD vertexer: tracks SPD tracks: • all combinations of inner + outer pixels • straight trajectories: the magnetic field is not taken into account outer layer inner layer pixels Sergey Gorbunov, FIAS

  13. HLT SPD vertexer: track selection Selection of tracks: cut in XY, search in Z Calculate DCA point for each track. Remove tracks with DCA > R cut. Store Z of the DCA point in an array. Find the highest peak in the Z-array, select tracks which produce the peak. R cut DCA point vertex guess Sergey Gorbunov, FIAS

  14. HLT SPD vertexer: vertex fit The 3D vertex is fitted as a closest point to all the selected tracks. Sergey Gorbunov, FIAS

  15. HLT SPD vertexer: complete algorithm Complete algorithm: Guess the vertex Select tracks for the vertex fit Fit the vertex Iterate from step 2. several times Sergey Gorbunov, FIAS

  16. HLT SPD Vertexer performance on MC HLT SPD Vertexer performance • 1000 14TeV pp MC events • In order to check possible bias to the origin, the MC vertex is set to (.2,.2,0) Resolutions: • X,Y = 269 um • Z = 164 um • No offsets Speed: 3500 events / s Sergey Gorbunov, FIAS

  17. HLT SPD Vertexer: real data 2009 On-line SPD vertexer on the HLT event display, run 101498 Sergey Gorbunov, FIAS

  18. Summary HLT Vertexer – Summary: • On-line SPD vertexer has been developed for the ALICE HLT. • It is fast and shows good resolutions on Monte-Carlo. • The vertexer is used for on-line monitoring of the ALICE interaction point since the first collisions in 2009. Sergey Gorbunov, FIAS

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