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Alice Weekly Meeting: Software for Hardware Accelerators / PDP-SRC
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Europe/Zurich
ALICE GPU Meeting
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Speaker: Christian Sonnabend (CERN, Heidelberg University (DE))
Framework
- Added GPU timer to ONNX inference for profiling
- Added deconvolution flags to NN inference for exact matching with GPU CF
Physics
- Cluster attachment efficiency vs. fake rate for different network inputs and thresholds
- Attachment efficiency = (correctly attached cls NN / total cls) * (correctly attached cls NN / correctly attached cls GPU CF)
- Network outperforms GPU CF under all thresholds and input sizes. Choice for threshold is determined by number of correctly attached clusters in the next plot
- Significant benefits from using 3D networks
- Number of correctly attached clusters
- Threshold choice for classification network:
- <= 0.01: Almost no loss in number of correctly attached clusters
- >0.01 && <0.1: Maximum loss of 5% correctly attached clusters, but can lead to 18% savings in total clusters (see next plot)
- Number of total clusters
- CoG (pad) resolution as a function of occupancy for different network sizes (2 to 5 hidden layers; 16, 32, 64, 128 neurons per layer)
- More layers work better
- Network with L5 and N128 -> Not great performance, reason: Overtraining! Immediately visible in the logs. Validation loss goes up while training loss goes down / remains constant.
-> Improvement for the future: Save network at best training loss, best validation loss and network after all epochs are done.
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