Pad-parallel tagging
- Tag / exclude clusters which cross a pad row tangentially
- Apply the looper tagger twice with different settings
- Overall change in efficiency / clone rate / fake rate / total number of digit maxima
| raw | looper tagging | parallel pad tagging |
Efficiency (%) | 93.34 | 93.31 | 94.04 |
Clone (%) | 7.56 | 6.75 | 5.82 |
Fake (%) | 19.24 | 17.31 | 16.79 |
Digit maxima (mio.) | 25.8 | 22.6 | 16.2 |
Differential studies (for one sector)
- Started with pT and η -> Can only analyse clusters which have an assignment (obviously)
- Network mostly tags assigned clusters with very high probabilites (this is with looper tagging and pad-parallel tagging)
- Black line is a typical cut-off line for the network (0.16 in this case)
- At higher pT, the density seems to increase in favor of the network -> high-pT clusters are tagged correctly
Neural network GPU speed
- Bug in PyTorch: Conv3D layer (used for 3D network) does not properly utilize the MM GPU kernels
- However FC layers do!
- Conv3D ~0.5-1 TFLOPs ; FC layers: 10 - 20 TFLOPs
- With only FC layers: Processing ~70-80 mio. clusters / s for classification network (relatively huge: 110k trainable parameters) on MI100