ALICE TPC Online Tracking on GPU David Rohr for the ALICE Corporation 25.5.2010 Lisbon.

ALICE TPC Online Tracking on GPU

David Rohrfor the ALICE Corporation

25.5.2010Lisbon


IntroductionTracking AlgorithmNVIDIA CUDATracking on GPUResultsSummary

Large Hadron Collider



The ALICE Experiment



Tracking

Clusters Tracks



Real TPC pp-Event in Online Event Display



Simulated Heavy Ion Event



Tracks found in simulated Heavy Ion Event



Tracks in simulated Central Heavy Ion Event



TPC Clusters divited into Slices



The ALICE TPC



TPC Tracker and Merger



One ALICE TPC Sector(Traking is performed row by row)



Tracking Algorithm

Category of Task Name of Task Description on Task

(Initialization)

Combinatorial Part(Cellular Automation)

I: Neighbors FindingConstruct Seeds(Track Candidates)II: Evolution

Kalman Filter PartIII: Tracklet Construction

Fit SeedExtrapolate TrackletFind New Clusters

IV: Tracklet Selection Select good Tracklets

(Tracklet Output)



Step I: Neighbors Finder(Fit best straight lines)

dx+



Step II: Evolution(Keep coinciding links)



Step III: Tracklet Construction

Green: Seed Red: ExtrapolationClusters close to the extraplation point are searched



Neighbours Finder on Real Data



Evolution Step on Real Data



Tracklet Construction on Real Data



Tracklet Selection on Real Data



NVIDIA CUDA GPU



Parallel Tracklet Construction

Current Row

Tracklets are independent and can be processed simultaneouslyBecause of data locality the tracklets are processed for a common RowSingle Instruction Decoder Either parallel fitting or extrapolationRed: Initial Seed Green: Extrapolation



Initial GPU Tracker Performance (Peripheral Pb-PB)

(8 threads on Nehalem CPU with 8 virtual / 4 physical cores) Focus on Tracklet Construction when optimizing



Active GPU Threads for the First Implementation

Active GPU Threads: 19%

Colors represent Tracklet Constructor Steps:Black: IdleBlue: Track FitGreen: Track Extrapolation

x-axis: threads y-axis: time



Active GPU Threads using Dynamic Scheduling

Active GPU Threads: 62%Colors represent Tracklet Constructor Steps:Black: IdleBlue: Track FitGreen: Track Extrapolation

x-axis: threads y-axis: time



Pipelining

Initialization / Output on CPU, Tracking on GPU and DMA Transfer can overlap

Time



Final Speedup (Central Heavy Ion)

(CPU performance was doubled as a side effect)



Final Speedup in Contrast to Event Size

(PP Mode: Special variant optimized for small scale events)



Tracking Efficiency

CPU

GPU



Summary

Twofold performance increase on CPU2.5-fold performance increase compared to CPUTracking efficiency matches the CPU tracker‘s efficiencyA common source code ensures maintainabilityCPU still available during the GPU tracking

Perspective for the Future

Next NVIDIA GPU generation Fermi may result in another boostTrack Merger may be adapted to run on the GPU

ALICE TPC Online Tracking on GPU David Rohr for the ALICE Corporation 25.5.2010 Lisbon.

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