Updates on NN perfromance

• CF clusterizer, 21.6 mio. clusters

• Classification (trained on MC) + CF regression, 16 mio. clusters

• Classification (trained on real data, native clusters) + CF regression, 18.5 mio. clusters

• Next subjects for study
  • Improve regression network (probably connected to training data)
  • Use one of the classification networks, vary cuts and observe impact on tracking efficiency
  • Automatize the working point search (potentially via a hyperparameter optimization strategy)

Updates on NN speed & implementation

• With ROCm 6.2, ONNXRuntime compiles now for gfx906 (MI50) & gfx908 (MI100) and also with support of float16
• Successfully imported and evaluated NN's on standalone (in O2) task with ~19 mio. clusters/s (MI50) and ~25 mio. clusters/s (MI100)
  • MI100 should be ~8x faster than MI50 for FP16 -> We are "scheduling"-bound!
• session->Run() can only currently run with max. 3910 tensors of size 346 (= 7x7x7 + 3) in FP16: Potentially limited by GPU memory page-size
  • Increased perfromance by spawning multiple sessions (~30) and using multithreading to load the GPU

• CPU implementation (top is ONNX, bottom is standard reco)

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