CERN Summer Student Project
by Alex Burlacu
[Full topic of the project here]
CERN Summer Student Project
by Alex Burlacu
Benchmarking TMVA package against TensorFlow on event-by-event inference
performance on multi-layered perceptrons for HEP
CERN Summer Student Project
by Alex Burlacu
TL;DR: TMVA DeepNets vs Tensorflow DeepNets
What is TMVA?
What are the use cases?
Provides Machine Learning capabilities to ROOT users
... also Feature Selection and data manipulation
Is part of the ROOT package
Low-latency inference
(As) Real-time (as possible) inference
For high energy physics
Why this project matters?
Define TMVA's Deep Learning Roadmap
Benchmark Setting
Intel i7-7820X CPU @ 3.60GHz (8 PCores/16 LCores)
32 GB DDR4 RAM
Non-optimized TF v1.9rc1 and Keras
MKL Optimized TF v1.8 and Keras
TF v1.8 C++ API
TMVA from ROOT 6.14
Inference Overhead
Inference Overhead
TMVA is 1-2 orders of magnitude faster on small batches and nets
Dependency of inference time on model size (TMVA / TF C++ inf. time)
Dependency of inference time on model size (TMVA / TF C++ inf. time)
TMVA MLPs work best with 1-4 layers and narrow layers
Dependency of inference time on batch size
2 Layers, 128 Neurons/Layer
Dependency of inference time on batch size
3 Layers, 256 Neurons/Layer
For Batch Size > 32 TMVA isn't the best choice
Thank you!
Questions?
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