ALICE HLT High Speed Tracking and Vertexing

ALICE HLT High SpeedTracking and Vertexing

Real-Time 2010 Conference Real-Time 2010 Conference Lisboa, May 25, 2010Lisboa, May 25, 2010

Sergey GorbunovSergey Gorbunov 1,21,2

11 Frankfurt Institute for Advanced Studies, University of Frankfurt, GermanyFrankfurt Institute for Advanced Studies, University of Frankfurt, Germany22 Kirchhoff Institute for Physics, University of Heidelberg, GermanyKirchhoff Institute for Physics, University of Heidelberg, Germany

Real-Time 2010 Sergey Gorbunov, FIAS 2/18

HLT event reconstruction scheme (main trackers)HLT event reconstruction scheme (main trackers)

The TPC Sector Tracker is the most complicated algorithm:

combinatorial search fit mathematics the reconstruction time is crucial


HLT TPC Sector Tracker: The Cellular Automaton methodHLT 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


HLT TPC Tracker performance (Sector Tracker + Global Merger) on MCHLT 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%


HLT TPC Tracker performance (Sector Tracker + Global Merger) on MCHLT TPC Tracker performance (Sector Tracker + Global Merger) on MC

HLT Tracker :

Time = 17.6 sEff = 98.15%Ghost = 1.66%Clone = 13.22%

HLT Tracker :

Time = 19.6 msEff = 99.86%Ghost = 0.19%Clone = 9.06%

Offline Tracker :

Time = 160.1 s Eff = 95.84%Ghost = 1.40% Clone = 12.13%

Offline Tracker :

Time = 66.0 msEff = 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:


Real PP Event in the HLT (2009 data)Real PP Event in the HLT (2009 data)

primary vertex

vertex-fitted tracks

tracks


HLT V0’s: PP run 00010480HLT V0’s: PP run 00010480

Monitoring of V0 physics on-line

HLT V0 finder

Gamma, Ks, Lambda analysis


Use of parallel hardware: GPU devicesUse of parallel hardware: GPU devices

NVIDIA GeForce GTX 280: 30x8 general propose processors; pure calculations can be ~100 timesfaster 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


Running the sector tracker on the GPU cluster at Frankfurt UniversityRunning the sector tracker on the GPU cluster at Frankfurt University

CPU GPU

• speed-up: 10.5x

• same code• same result

CPU GPU


HLT Tracker - SummaryHLT Tracker - Summary

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: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, …).


HLT vertexer: the Silicon Pixel DetectorHLT vertexer: the Silicon Pixel Detector

Silicon Pixel Detector (SPD) 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 The SPD detector can provide stand-alone

event vertex, which is useful for on-lineevent vertex, which is useful for on-line

monitoring of the ALICE interaction point. monitoring of the ALICE interaction point.

High-Speed Vertexing in HLT


HLT SPD vertexer: tracksHLT 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: SPD tracks:


HLT SPD vertexer: track selectionHLT 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 Selection of tracks: cut in XY, search in Z


HLT SPD vertexer: vertex fitHLT SPD vertexer: vertex fit

The 3D vertex is fitted as a closest point to all the selected tracks.The 3D vertex is fitted as a closest point to all the selected tracks.


HLT SPD vertexer: complete algorithmHLT 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: Complete algorithm:


HLT SPD Vertexer performance on MCHLT SPD Vertexer performance on MC

HLT SPD Vertexer performanceHLT 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: Resolutions:

• X,Y = 269 um• Z = 164 um• No offsets

Speed: Speed:

3500 events / s


HLT SPD Vertexer: real data 2009HLT SPD Vertexer: real data 2009

On-line SPD vertexer on the HLT event display, run 101498On-line SPD vertexer on the HLT event display, run 101498


SummarySummary

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.

HLT Vertexer – Summary:HLT Vertexer – Summary:

Documents

ALICE HLT High Speed Tracking and Vertexing