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

2/18 Real-Time 2010 Sergey Gorbunov, FIAS HLT event reconstruction scheme (main trackers) The TPC Sector Tracker is the most complicated algorithm:  combinatorial search  fit mathematics  the reconstruction time is crucial

3/18 Real-Time 2010 Sergey Gorbunov, FIAS HLT TPC Sector Tracker: The Cellular Automaton method row k+1 row k row k-1 TPC sector 1.Neighbours finder: For each TPC cluster it finds two (up&down) neighbours which compose the best line 2. Evolution non-reciprocal links removed one-to-one linked clusters are compose track segments 3. Other steps fit, search for missed hits, and the final track selection

4/18 Real-Time 2010 Sergey Gorbunov, FIAS HLT TPC Tracker performance (Sector Tracker + Global Merger) on MC pp, HLT pp, Offline central PbPb, HLT central PbPb, Offline 99.86% 9.06% 0.19% 98.15% 13.22% 1.66% 95.84% 12.13% 1.40% 99.94% 9.30% 0.21%

5/18 Real-Time 2010 Sergey Gorbunov, FIAS HLT TPC Tracker performance (Sector Tracker + Global Merger) on MC HLT Tracker : Time = 17.6 s Eff = 98.15% Ghost = 1.66% Clone = 13.22% HLT Tracker : Time = 19.6 ms Eff = 99.86% Ghost = 0.19% Clone = 9.06% Offline Tracker : Time = s Eff = 95.84% Ghost = 1.40% Clone = 12.13% Offline Tracker : Time = 66.0 ms Eff = 99.94% Ghost = 0.21% Clone = 9.30% MC, 14 TeV pp events: MC, 5 TeV Central PbPb events: Performance on Monte Carlo ~ linear time dependence:

6/18 Real-Time 2010 Sergey Gorbunov, FIAS Real PP Event in the HLT (2009 data) primary vertex vertex-fitted tracks tracks

7/18 Real-Time 2010 Sergey Gorbunov, FIAS HLT V0’s: PP run Monitoring of V0 physics on-line HLT V0 finder Gamma, Ks, Lambda analysis

8/18 Real-Time 2010 Sergey Gorbunov, FIAS 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

9/18 Real-Time 2010 Sergey Gorbunov, FIAS Running the sector tracker on the GPU cluster at Frankfurt University CPU GPU speed-up: 10.5x same code same result CPU GPU

10/18 Real-Time 2010 Sergey Gorbunov, FIAS 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, …).

11/18 Real-Time 2010 Sergey Gorbunov, FIAS HLT vertexer: the Silicon Pixel Detector 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. High-Speed Vertexing in HLT

12/18 Real-Time 2010 Sergey Gorbunov, FIAS HLT SPD vertexer: tracks all combinations of inner + outer pixels straight trajectories: the magnetic field is not taken into account outer layer inner layer pixels SPD tracks:

13/18 Real-Time 2010 Sergey Gorbunov, FIAS HLT SPD vertexer: track selection 1.Calculate DCA point for each track. 2.Remove tracks with DCA > R cut. 3.Store Z of the DCA point in an array. 4.Find the highest peak in the Z-array, select tracks which produce the peak. vertex guess R cut DCA point Selection of tracks: cut in XY, search in Z

14/18 Real-Time 2010 Sergey Gorbunov, FIAS HLT SPD vertexer: vertex fit The 3D vertex is fitted as a closest point to all the selected tracks.

15/18 Real-Time 2010 Sergey Gorbunov, FIAS HLT SPD vertexer: complete algorithm 1.Guess the vertex 2.Select tracks for the vertex fit 3.Fit the vertex 4.Iterate from step 2. several times Complete algorithm:

16/18 Real-Time 2010 Sergey Gorbunov, FIAS HLT SPD Vertexer performance on MC HLT SPD Vertexer performance TeV 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

17/18 Real-Time 2010 Sergey Gorbunov, FIAS HLT SPD Vertexer: real data 2009 On-line SPD vertexer on the HLT event display, run

18/18 Real-Time 2010 Sergey Gorbunov, FIAS 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 HLT Vertexer – Summary: