1. Online Track Reconstruction in the CBM Experiment I. Kisel, I. Kulakov, I. Rostovtseva, M. Zyzak (for the CBM Collaboration) I. Kisel, I. Kulakov, I. Rostovtseva, M. Zyzak (for the CBM Collaboration) @gsi.de E-mail: M.Zyzak @gsi.de Deutsche Physikalische Gesellschaft e.V. Münster 11 Tracking Challenge  Fixed-target heavy-ion experiment  10 7 collisions/s  1000 charged particles/collision  Non-homogeneous magnetic field  Track reconstruction and displaced vertex search required in the first trigger level Track Finder w.r.t. Detector Inefficiency Detector efficiency, % 1009795908580 x, μm1213 14 15 y, μm576061656973 t x,mrad0.350.360.370.380.400.42 t y,mrad0.600.61 0.630.640.66 p, %1.221.251.281.341.411.48 The algorithm is stable Slight efficiency degradation with detector efficiency decreasing Resolution of track parameters becomes slightly worse because of the smaller number of hits Scalability of the Track Finder 2 CPUs Intel X5550, 4 cores per CPU, HT, 2.7 GHz 4 CPUs AMD E6164HE, 12 cores per CPU, 1.7 GHz (in collaboration with Julien Leduc/CERN openlab) Strong many-core scalability for large groups of minimum bias events is observed. ConclusionsConclusions For track finding a CA based algorithm is used. The algorithm is fast and efficient. The algorithm is robust with respect to the detector inefficiency. The algorithm shows strong many-core scalability. The investigation of 4D reconstruction has been started. Deterministic Annealing Filter 1 Hit displacement unshifted5 σ hit 10 σ hit 20 σ hit MVD10.4 20.7 STS10.3 20.4 3 0.70.80.5 4 43.985.098.7 50.51.6 0.8 60.6 7 80.1 Task: reduce an influence of attached distorted or noise hits on the reconstructed track parameters. Percentage of rejected hits depending on the distance from the shifted hit on the 4 th STS station to its Monte-Carlo position has been measured. A weight is introduced to each hit Algorithm is iterative With each iteration estimation of the hits weight is improved Based on SIMD KF track fit benchmark 2 4D Reconstruction for the CBM Experiment CBM will have: Free streaming data 4D measurements (x, y, z, t) Track reconstruction prior event recognition First idealized 4D STS reconstruction with CA track finder has been investigated. Discrete time have been used. The same efficiency Slight increase of the processing time with larger size of the time slices Will be further investigated within the CA track finder. 1 R. Frühwirth and A. Strandlie, Track Fitting with ambiguities and noise: a study of elastic tracking and nonlinear filters. Comp. Phys. Comm. 120 (1999) 197-214. 2 S. Gorbunov, U. Kebschull, I. Kisel, V. Lindenstruth and W.F.J. Müller, Fast SIMDized Kalman filter based track fit, Comp. Phys. Comm. 178 (2008) 374-383 Track Reconstruction Cellular Automaton (CA) based track finder algorithm Kalman filter track fit Highly optimized code –Single precision calculations –Magnetic field approximation –Reconstruction in several iterations Highly parallelized code –Data level (SIMD instructions, 4 single-precision floating point calculations in parallel) –Task level (ITBB, parallelization between cores) 0. Hits 1. Segments 1 2 3 4 2. Counters 3. Track Candidates 4. Tracks Detector layers Hits Cellular Automaton: 1.Build short track segments 2.Connect according to the track model 3.Tree structures appear, collect segments into track candidates 4.Select the best track candidates Cellular Automaton advantages: Local w.r.t. data Intrinsically parallel Extremely simple Very fast Perfect for many-core CPU/GPU Track Reconstruction Efficiency Efficiency and ratios, % Reference set97.8 All set87.6 Clone0.8 Ghost12.8 Tracks/ev733 Time/ev, s1.4 All set: p ≥ 0.1 GeV/c Reference set: p ≥ 1 GeV/c Ghost: purity < 70% Reconstructable track: Number of consecutive MC points ≥ 4 Computer with two Xeon X5550 processors at 2.7 GHz and 8 MB L3, 1 core is used. Au+Au central events at 25 AGeV, 8 STS and 2 MVD stations. Au+Au central events at 25 AGeV, 8 STS stations.