Geant4-FastSim - Memory footprint optimization for ML fast shower simulation


In Large Hadron Collider (LHC) experiments, at CERN in Geneva, the calorimeter is a key detector technology to measure the energy of particles. These particles interact electromagnetically and/or hadronically with the material of the calorimeter, creating cascades of secondary particles or showers. Describing the showering process relies on simulation methods that precisely describe all particle interactions with matter. A detailed and accurate simulation is based on the Geant4 toolkit. Constrained by the need for precision, the simulation is inherently slow and constitutes a bottleneck for physics analysis. Furthermore, with the upcoming high luminosity upgrade of the LHC with more complex events and a much increased trigger rate, the amount of required simulated events will increase.

Machine Learning (ML) techniques such as generative modeling are used as fast simulation alternatives to learn to generate showers in a calorimeter, i.e. simulating the calorimeter response to certain particles. The pipeline of a fast simulation solution can be categorized into five components: data preprocessing, ML model design, validation, inference and optimization. The preprocessing module allows us to derive a suitable representation of showers, to perform data cleaning, scaling and encoding. The preprocessed data is then used by the generative model for training. In order to search for the best set of hyperparameters of the model, techniques such as Automatic Machine Learning (AutoML) are used. The validation component is based on comparing different ML metrics and physics quantities between the input and generated data. After training and validation the model is converted into a format that can be used for inference in C++. This allows its application directly in the frameworks of physics experiments. The optimization component is used to further reduce the memory footprint of the model at inference time. Moreover, optimization techniques are also used at training time to reduce the number of trainable parameters.

The aim of this project is to optimize the ML pipeline of the fast simulation approach using the open-source platform Kubeflow with a focus on the memory footprint optimization of the model during inference. Furthermore, a byproduct of this project is that the student will gain expertise in cutting-edge ML techniques, and learn to use them in the context of high granularity image generation and fast simulation. Moreover, this project can serve as a baseline for future ML pipelines for all experiments at CERN.

For in-depth details regarding the entire g4fastsim project, please go to this website:

Inference Optimization pipeline

KubeFlow was the platform of choice for making a reproducible and scalable machine learning inference pipeline for the g4fastsim inference project. CERN IT Department has built which is KubeFlow service accessible to all CERN members and was used for building the pipeline.

g4fastsim is broken into 2 parts - Training and Inference. Inference Optimization Pipeline is aimed at reducing memory footprint of the ML model by performing various types of hardware-specific quantizations and graph optimizations.

The Inference pipeline is broken down into 5 main components:

  • MacroHandler
  • InputChecker
  • Inference
  • Benchmark
  • Optimization

Below image represents an overview of the approach taken to build inference optimization kubeflow pipeline:


KubeFlow Pipeline

The pipeline can be found on under the name of Geant4-Model-Optimization-Pipeline. Kubeflow_inference_optimization_pipeline

  • FullSimMacroHandler, OnnxFastSimNoEPMacroHandler - Macro Handler components which output a macro file that gets passed to the respective Par04s.
  • FullSim, OnnxFastSimNoEP - Par04s which use Full Simulation and ONNXRuntime without any execution providers respectively.
  • FullSimV/SOnnxFastSimNoEP - Benchmark component that takes in inputs from FullSim and OnnxFastSimNoEP and compares them for similarity check.

:green_book: A memory arena is a large, contiguous piece of memory that you allocate once and then use to manage memory manually by handing out parts of that memory. To understand arena in relation to memory, check out this stack overflow post


  • fullSimJsonlUrl - Url of FullSim Jsonl file which generates macro file for full sim inference
  • fastSimJsonlUrl - Url of FastSim Jsonl file which generates macro file for fast sim inference
  • jsonlSavePath - Path where JSONL file will be saved after downloading, inside the docker container
  • particleEnergy - Energy of the particle
  • particleAngle - Angle of the particle.
  • setSizeLatentVector - Dimension of the latent vector (encoded vector in a Variational Autoencoder model)
  • setSizeConditionVector - Size of the condition vector (energy, angle and geometry)
  • setModelPathName - Path of the ONNX model to be used for Fast Sim
  • setProfileFlag - Whether to perform profiling during inference using ONNXRuntime.
  • setOptimizationFlag - Whether to perform graph optimization on the model when using ONNXRuntime.
  • setDnnlFlag - Whether to use oneDNN Execution Provider as compute backend when using ONNXRuntime.
  • setOpenVINOFlag - Whether to use OpenVINO Execution Provider as compute backend when using ONNXRuntime.
  • setCudaFlag - Whether to use CUDA Execution Provider as compute backend when using ONNXRuntime.
  • setTensorrtFlag - Whether to use TensorRT Execution Provider as compute backend when using ONNXRuntime.
  • setInferenceLibrary - Whether to use ONNXRuntime or LWTNN for inference in FastSim
  • setFileName - Name of the the output .root file.
  • beamOn - Number of events to run
  • yLogScale - Whether to set a log scale on the y-axis of full and fast simulation comparison plots.
  • saveFig - Whether to save plots in Benchmark component
  • eosDirectory - Path of the EOS directory where output plots will be saved.


  • setDnnlEnableCpuMemArena - Whether to enable memory arena when using oneDNN Execution Provider.


  • setCudaDeviceId - ID of the device on which to run CUDA Execution Provider.
  • setCudaGpuMemLimit - GPU Memory Limit when using CUDA Execution Provider.
  • setCudaArenaExtendedStrategy - Strategy to use for extending memory arena. For more details, go here
  • setCudaCudnnConvAlgoSearch - Type of search done for finding which cuDNN convolution algorithm to use. For more details, go here
  • setCudaDoCopyInDefaultStream - Whether to perform data copying operation from host to device and viceversa in default stream or separate streams. For more details, check here
  • setCudaCudnnConvUseMaxWorkspace - Amount of memory to use when querying cuDNN to find the most optimal convolution algorithm. Lower value will result in sub-optimal querying and higher value will lead to higher peak memory usage. For more details, check here.


  • setTrtDeviceId - ID of device on which to run TensorRT Execution Provider
  • setTrtMaxWorksapceSize - Maximum memory usage of TensorRT Engine.
  • setTrtMaxPartitionIterations - Maximum no.of iterations allowed while partitioning the model for TensorRT
  • setTrtMinSubgraphSize - Minimum node size in a subgraph after partitioning.
  • setTrtFp16Enable - Whether to enable FP16 computation in TensorRT
  • setTrtInt8Enable - Whether to enable INT8 computation in TensorRT
  • setTrtInt8UseNativeCalibrationTable - Select what calibration table is used for non-QDQ (Quantize- DeQuantize) models in INT8 mode. If 1, native TensorRT generated calibration table is used; if 0, ONNXRUNTIME tool generated calibration table is used.
  • setTrtEngineCacheEnable - Enable TensorRT engine caching. The purpose of using engine caching is to save engine build time in the case that TensorRT may take long time to optimize and build engine.

    :warning: Each engine is created for specific settings such as model path/name, precision (FP32/FP16/INT8 etc), workspace, profiles etc, and specific GPUs and it’s not portable, so it’s essential to make sure those settings are not changing, otherwise the engine needs to be rebuilt and cached again.

    :warning: Please clean up any old engine and profile cache files (.engine and .profile) if any of the following changes:

    • Model changes (if there are any changes to the model topology, opset version, operators etc.)
    • ORT version changes (i.e. moving from ORT version 1.8 to 1.9)
    • TensorRT version changes (i.e. moving from TensorRT 7.0 to 8.0)
    • Hardware changes. (Engine and profile files are not portable and optimized for specific Nvidia hardware)
  • setTrtEngineCachePath - Specify path for TensorRT engine and profile files if setTrtEngineCacheEnable is True, or path for INT8 calibration table file if setTrtInt8Enable is True.
  • setTrtDumpSubgraphs - Dumps the subgraphs that are transformed into TRT engines in onnx format to the filesystem. This can help debugging subgraphs.


  • setOVDeviceType - Type of device to run OpenVINO Execution Provider on
  • setOVDeviceId - ID of the device to run OpenVINO Execution Provider on
  • setOVEnableVpuFastCompile - During initialization of the VPU device with compiled model, Fast-compile may be optionally enabled to speeds up the model’s compilation to VPU device specific format.
  • setOVNumOfThreads - No.of threads to use while performing computation using OpenVINO Execution Provider
  • setOVUseCompiledNetwork - It can be used to directly import pre-compiled blobs if exists or dump a pre-compiled blob at the executable path.
  • setOVEnableOpenCLThrottling - This option enables OpenCL queue throttling for GPU devices (reduces CPU utilization when using GPU).


Par04 takes in input a macro file. This macro file contains a list of commands in the order they are to be run. Normally, if we want to change a macro file we will have to change the .mac file, upload it to gitlab, EOS or someplace from where it will be downloaded and pass the URL in KubeFlow Run UI Dashboard. This will become a tedious process.

MacroHandler component is implemented to solve this issue by generating a .mac file based on the inputs from the KubeFlow Run UI.

The input to MacroHandler component is a .jsonl file containing jsonlines, where each jsonline contain 2 keys - command and value. An example of .jsonl macro file can be seen here.

.jsonl file is converted to .mac file after value modifications and passed to the respective Par04 components.

Eg: As per the pipeline image above, the output of FullSimMacroHandler component will be passed to FullSim and output of OnnxFastSimNoEPMacroHandler component will be passed to OnnxFastSimNoEP.

This component is specifically designed for KubeFlow usage

Macro file

This macro file contains Geant4 commands as well as custom commands for manipulating the simulation process. An example of the macro file can be seen below. It uses CUDA backend for performing inference in ONNXRuntime.

#  examplePar04_onnx.mac
# Detector Construction 
/Par04/detector/setDetectorInnerRadius 80 cm
/Par04/detector/setDetectorLength 2 m
/Par04/detector/setNbOfLayers 90
/Par04/detector/setAbsorber 0 G4_W 1.4 mm true
/Par04/detector/setAbsorber 1 G4_Si 0.3 mm true
## 2.325 mm of tungsten =~ 0.25 * 9.327 mm = 0.25 * R_Moliere
/Par04/mesh/setSizeOfRhoCells 2.325 mm
## 2 * 1.4 mm of tungsten =~ 0.65 X_0
/Par04/mesh/setSizeOfZCells 3.4 mm
/Par04/mesh/setNbOfRhoCells 18
/Par04/mesh/setNbOfPhiCells 50
/Par04/mesh/setNbOfZCells 45

# Initialize
/run/numberOfThreads 1

/gun/energy 10 GeV
/gun/position 0 0 0
/gun/direction 0 1 0

# Inference Setup
## dimension of the latent vector (encoded vector in a Variational Autoencoder model)
/Par04/inference/setSizeLatentVector 10
## size of the condition vector (energy, angle and geometry)  
/Par04/inference/setSizeConditionVector 4
## path to the model which is set to download by cmake
/Par04/inference/setModelPathName ./MLModels/Generator.onnx
/Par04/inference/setProfileFlag 1
/Par04/inference/setOptimizationFlag 0
/Par04/inference/setCudaFlag 1

# cuda options
/Par04/inference/cuda/setDeviceId 0
/Par04/inference/cuda/setGpuMemLimit 2147483648
/Par04/inference/cuda/setArenaExtendedStrategy kSameAsRequested
/Par04/inference/cuda/setCudnnConvAlgoSearch DEFAULT
/Par04/inference/cuda/setDoCopyInDefaultStream 1
/Par04/inference/cuda/setCudnnConvUseMaxWorkspace 1

/Par04/inference/setInferenceLibrary ONNX

## set mesh size for inference == mesh size of a full sim that
## was used for training; it coincides with readout mesh size
/Par04/inference/setSizeOfRhoCells 2.325 mm
/Par04/inference/setSizeOfZCells 3.4 mm
/Par04/inference/setNbOfRhoCells 18
/Par04/inference/setNbOfPhiCells 50
/Par04/inference/setNbOfZCells 45

# Fast Simulation
/analysis/setFileName 10GeV_100events_fastsim_onnx.root
## dynamically set readout mesh from particle direction
## needs to be the first fast sim model!
/param/ActivateModel defineMesh
## ML fast sim, configured with the inference setup /Par04/inference
/param/ActivateModel inferenceModel
/run/beamOn 100

There are additional commands which can be used to configure the settings of each execution provider. A detailed example of macro file for ONNXRuntime can be viewed here.


KubeFlow passes all the parameters as string from its run UI. In order to debug type issues a small input checker component is build which will output the value of every KubeFlow UI parameter and its value type. This an independent component of the pipeline.

This component is specifically designed for KubeFlow usage


Par04 is a C++ application which takes in as input an ONNX model, performs inference using it and outputs a .root file containing the simulated physics quantities which can be used for downstream analysis of particle showers.

Par04 currently supports 3 libraries - ONNXRuntime, LWTNN and PyTorch. The inference optimization pipeline currently being built is geared towards using ONNXRuntime. The execution providers that will be investigated for optimizing the ONNX model are -

  • Nvidia TensorRT - GPU
  • Nvidia CUDA - GPU
  • Intel oneDNN - CPU
  • Intel OpenVINO - CPU

    Support for Intel OpenVINO is currently on halt due to compatibility issues.

Output .root file

The output of Par04 is a .root file which can be opened using TSystem (C++) and uproot (python). The .root file contains the following structure:

├── energyDeposited;1
├── energyParticle;1
├── energyRatio;1
├── events;1
│   ├── CPUResMem
│   ├── CPUVirMem
│   ├── EnergyCell
│   ├── EnergyMC
│   ├── GPUMem
│   ├── phiCell
│   ├── rhoCell
│   ├── SimTime
│   └── zCell
├── hitType:1
├── longFirstMoment;1
├── longProfile;1
├── longSecondMoment;1
├── time;1
├── transFirstMoment;1
├── transProfile;1
└── transSecondMoment;1

More details on these quantities are explained in this link.

CPUResMem, CPUVirMem, GPUMem are added into the updated version of the Par04 application: CPUResMem - Resident Memory used by the inference of the Par04 application CPUVirMem - Virtual Memory used by the inference of the Par04 application GPUMem - GPU Memory used by the inference of the Par04 application More details on Resident and Virtual Memory can be found here


Par04 has a number of optional dependencies which can be added to customise it for different host hardware and usecase.

Running FastSim in Par04 will require ONNXRuntime.


  • Geant4 - A platform for simulation of the passage of particles through matter.


  • ONNXRuntime - Machine learning platform developed by microsoft which is acting as the backend for using different hardware specific execution providers.

  • CUDA - Parallel computing interface for Nvidia GPUs developed by Nvidia.

  • TensorRT - Machine learning framework developed by Nvidia for optimizing ML model deployment on Nvidia GPUs. It is built on top of CUDA.

  • oneDNN - Machine learning library which optimizes model deployment on Intel hardware. It is part of oneAPI - a cross platform performance library for deep learning applications.

  • OpenVINO - Machine Learning toolkit facilitating optimization of deep learning models from a framework and deployment on Intel Hardware.

  • ROOT - An object oriented library developed by CERN for for particle physics data analysis.
    • In Par04, ROOT is used to get host memory usage.
  • Valgrind - Valgrind is a applicationming tool for memory debugging, memory leak detection, and profiling.
    • In Par04, Valgrind is used for function call profiling of inference block.

Run Inference

To run Par04 simulation application, follow the below given steps:

  1. Clone the git repo
    git clone --recursive
  2. Build the application
    cmake -DCMAKE_BUILD_TYPE="Debug" -DCMAKE_CXX_FLAGS_DEBUG="-ggdb3" <source folder>

If you want build Par04 using dependencies, then use the DCMAKE_PREFIX_PATH flag.

cmake -DCMAKE_BUILD_TYPE="Debug" \
    -DCMAKE_PREFIX_PATH="/opt/valgrind/install;/opt/root/src/root;/opt/geant4/install;/opt/onnxruntime/install;/opt/onnxruntime/data;/usr/local/cuda;" \
    -DCMAKE_CXX_FLAGS_DEBUG="-ggdb3" \
    <source folder>
  1. To build and install the application, go into the build directory and run
    make -j`nproc`
    make install
  2. Go to the install directory and run:
    ./examplePar04 -m <macro file> -k -g True -o <output filename> -do <output directory>

:warning: Make sure to add path to dependencies in PATH and LD_LIBRARY_PATH respectively

Checkout dependencies section for more info on customising Par04 for your purpose.


  • -m - macro file path
  • -k - Whether it is kubeflow deployment or not. Adding -k will indicate True.
  • -g - Whether to enable GPU memory usage collection or not.
  • -o - Name of the output file. Make sure it ends with .root.
  • -do - Path of the directory where output file will be saved.

:warning: -o and -do will only be used when -k is set as these 2 flags are KubeFlow deployment specific. They are added to make the Par04 compatible with KubeFlow’s data passing mechanism. In order to make KubeFlow component’s reusable, KubeFlow recommends allowing the KubeFlow sdk to generate paths at compile time.

The paths generated can or cannot be present in the docker container, so, we need to handle both the cases and when path is not present, then our code should auto generate the directories as the per the path. In order to understand KubeFlow’s data passing mechanism, please refer to this guide.

If you are using Par04 as a standalone application, you can also use /analysis/setFileName in macro file to set the filename.


The benchmark component is built for comparing the output of different Par04 runs. Currently, it supports 2 output files and the plan to support 3 files is in roadmap. The current benchmark component is tailored a lot towards using in a pipeline rather than a standalone application.

:warning: If you are running as a standalone script, make sure the output .root filenames of Full Sim and Fast Sim(s) is same and both are in different folders.

├── FastSim
│   └── output.root
└── FullSim
    └── output.root

The output of the benchmark component is a set of plots which will help with comparison between Full Sim and Fast Sim(s) as well as a .json file which will contain Jensen Shannon Divergence values for different physics quantity histograms.

For more details on Jensen Shannon Divergence, refer to this link

Benchmark is a python component and requires the following dependencies:

  • scipy
  • uproot
  • numpy
  • matplotlib
  • json

Run Benchmark

  1. To run Benchmark, clone the Par04 repository
    git clone --recursive
  2. Go into the Benchmark component directory
    cd <source directory>/pipelines/model-optimization/benchmark
  3. Run
    python3 \
    --input_path_A <Path of first input directory> \
    --input_path_B <Path of second input directory> \
    --rootFilename <Filename of the output root file> \
    --truthE <Energy of the particles> \
    --truthAngle <Angle of the particles> \
    --yLogScale <Whether to set a log scale on the y-axis of full and fast simulation comparison plots.> \
    --saveFig <Whether to save the plots> \
    --eosDirectory <EOS directory path where you want to save the files> \
    --eosSaveFolderPath <EOS folder inside the EOS directory where you want to save the output> \
    --saveRootDir <Directory path inside docker container where you want the data to be saved for further passing>

:warning: If you are running Benchmark as a kubeflow component, use It performs Kerberos authentication for EOS access and then runs Every time a Kubeflow component is created it needs to authenticate itself for EOS access, it is not feasible to do it manually and takes care of that.

Before running a pipeline which requires EOS access, make sure your krb-secret is up-to-date and also run kinit CERN_ID. If you want to renew your krb-secret perform the steps mentioned here.

An example plot is given below: transProfile_1_E_10_GeV_A_90


To be build