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acts-project/traccc: Demonstrator tracking chain on accelerators

Demonstrator tracking chain for accelerators.

✅: exists, 🟡: work started, ⚪: work not started yet

The relations between datatypes and algorithms is given in the (approximately commutative) diagram shown below. Black lines indicate CPU algorithms, green lines indicate CUDA algorithms, blue lines indicate SYCL algorithms, and brown lines indicate Futhark algorithms. Solid algorithms are ready for use, dashed algorithms are in development or future goals. Data types for different heterogeneous platforms are contracted for legibility, and identities are hidden.

flowchart LR
    subgraph clusterization [<a href='https://github.com/acts-project/traccc/blob/main/core/include/traccc/clusterization/'>Clusterization</a>]
        direction TB
        cell(Cells);
        cluster(Clusters);
        meas(Measurements);
    end

    subgraph trkfinding [<a href='https://github.com/acts-project/traccc/blob/main/core/include/traccc/finding/'>Track Finding</a>]
        sp(Spacepoints);
        bin(Spacepoint Grid);
        seed(Seeds);
        ptrack(Prototracks);
    end

    subgraph trkfitting [<a href='https://github.com/acts-project/traccc/blob/main/core/include/traccc/fitting/'>Track Fitting</a>]
        trackc(Track Candidates)
        tracks(Track States);
    end

    click cell href "https://github.com/acts-project/traccc/blob/main/core/include/traccc/edm/silicon_cell_collection.hpp";
    click cluster href "https://github.com/acts-project/traccc/blob/main/core/include/traccc/edm/silicon_cluster_collection.hpp";
    click meas href "https://github.com/acts-project/traccc/blob/main/core/include/traccc/edm/measurement.hpp";
    click sp href "https://github.com/acts-project/traccc/blob/main/core/include/traccc/edm/spacepoint.hpp";
    click bin href "https://github.com/acts-project/traccc/blob/main/core/include/traccc/seeding/detail/spacepoint_grid.hpp";
    click seed href "https://github.com/acts-project/traccc/blob/main/core/include/traccc/edm/seed.hpp";
    click ptrack href "https://github.com/acts-project/traccc/blob/main/core/include/traccc/edm/track_parameters.hpp";
    click trackc href "https://github.com/acts-project/traccc/blob/main/core/include/traccc/edm/track_candidate.hpp";
    click tracks href "https://github.com/acts-project/traccc/blob/main/core/include/traccc/edm/track_state.hpp";

    %% Host CCL algorithm
    cell -->|<a href='https://github.com/acts-project/traccc/blob/main/core/include/traccc/clusterization/sparse_ccl_algorithm.hpp'>Sparse CCL</a>| cluster;
    linkStyle 0 stroke: black;

    %% Host measurement creation
    cluster -->|<a href='https://github.com/acts-project/traccc/blob/main/core/include/traccc/clusterization/measurement_creation_algorithm.hpp'>Meas. Creat.</a>| meas;
    linkStyle 1 stroke: black;

    %% SYCL clusterization
    cell -->|<a href='https://github.com/acts-project/traccc/blob/main/device/sycl/include/traccc/sycl/clusterization/clusterization_algorithm.hpp'>Clustering</a>| meas;
    linkStyle 2 stroke: blue;

    %% CUDA clusterization
    cell -->|<a href='https://github.com/acts-project/traccc/blob/main/device/cuda/include/traccc/cuda/clusterization/clusterization_algorithm.hpp'>Clustering</a>| meas;
    linkStyle 3 stroke: green;

    %% Alpaka clusterization
    cell -->|<a href='https://github.com/acts-project/traccc/blob/main/device/alpaka/include/traccc/alpaka/clusterization/clusterization_algorithm.hpp'>Clustering</a>| meas;
    linkStyle 4 stroke: orange;

    %% Host spacepoint formation
    meas -->|<a href='https://github.com/acts-project/traccc/blob/main/core/include/traccc/seeding/silicon_pixel_spacepoint_formation_algorithm.hpp'>SP Form.</a>| sp;
    linkStyle 5 stroke: black;

    %% SYCL spacepoint formation
    meas -->|<a href='https://github.com/acts-project/traccc/blob/main/device/sycl/include/traccc/sycl/seeding/silicon_pixel_spacepoint_formation_algorithm.hpp'>SP Form.</a>| sp;
    linkStyle 6 stroke: blue;

    %% CUDA spacepoint formation
    meas -->|<a href='https://github.com/acts-project/traccc/blob/main/device/cuda/include/traccc/cuda/seeding/spacepoint_formation_algorithm.hpp'>SP Form.</a>| sp;
    linkStyle 7 stroke: green;

    %% Alpaka spacepoint formation
    meas -->|<a href='https://github.com/acts-project/traccc/blob/main/device/alpaka/include/traccc/alpaka/seeding/spacepoint_formation_algorithm.hpp'>SP Form.</a>| sp;
    linkStyle 8 stroke: orange;

    %% Host spacepoint binning
    sp -->|<a href='https://github.com/acts-project/traccc/blob/main/core/include/traccc/seeding/spacepoint_binning.hpp'>SP Binning</a>| bin;
    linkStyle 9 stroke: black;

    %% SYCL spacepoint binning
    sp -->|<a href='https://github.com/acts-project/traccc/blob/main/device/sycl/include/traccc/sycl/seeding/spacepoint_binning.hpp'>SP Binning</a>| bin;
    linkStyle 10 stroke: blue;

    %% CUDA spacepoint binning
    sp -->|<a href='https://github.com/acts-project/traccc/blob/main/device/cuda/include/traccc/cuda/seeding/spacepoint_binning.hpp'>SP Binning</a>| bin;
    linkStyle 11 stroke: green;

    %% Alpaka spacepoint binning
    sp -->|<a href='https://github.com/acts-project/traccc/blob/main/device/alpaka/include/traccc/alpaka/seeding/spacepoint_binning.hpp'>SP Binning</a>| bin;
    linkStyle 12 stroke: orange;

    %% Host seeding
    bin -->|<a href='https://github.com/acts-project/traccc/blob/main/core/include/traccc/seeding/seed_finding.hpp'>Seeding</a>| seed;
    linkStyle 13 stroke: black;

    %% SYCL seeding
    bin -->|<a href='https://github.com/acts-project/traccc/blob/main/device/sycl/include/traccc/sycl/seeding/seed_finding.hpp'>Seeding</a>| seed;
    linkStyle 14 stroke: blue;

    %% CUDA seeding
    bin -->|<a href='https://github.com/acts-project/traccc/blob/main/device/cuda/include/traccc/cuda/seeding/seed_finding.hpp'>Seeding</a>| seed;
    linkStyle 15 stroke: green;

    %% Alpaka seeding
    bin -->|<a href='https://github.com/acts-project/traccc/blob/main/device/alpaka/include/traccc/alpaka/seeding/seed_finding.hpp'>Seeding</a>| seed;
    linkStyle 16 stroke: orange;

    %% Host param est.
    seed -->|<a href='https://github.com/acts-project/traccc/blob/main/core/include/traccc/seeding/track_params_estimation.hpp'>Param. Est.</a>| ptrack;
    linkStyle 17 stroke: black;

    %% SYCL param est.
    seed -->|<a href='https://github.com/acts-project/traccc/blob/main/device/sycl/include/traccc/sycl/seeding/track_params_estimation.hpp'>Param. Est.</a>| ptrack;
    linkStyle 18 stroke: blue;

    %% CUDA param est.
    seed -->|<a href='https://github.com/acts-project/traccc/blob/main/device/cuda/include/traccc/cuda/seeding/track_params_estimation.hpp'>Param. Est.</a>| ptrack;
    linkStyle 19 stroke: green;

    %% Alpaka param est.
    seed -->|<a href='https://github.com/acts-project/traccc/blob/main/device/alpaka/include/traccc/alpaka/seeding/track_params_estimation.hpp'>Param. Est.</a>| ptrack;
    linkStyle 20 stroke: orange;

    %% Host CKF
    ptrack -->|<a href='https://github.com/acts-project/traccc/blob/main/core/include/traccc/finding/combinatorial_kalman_filter_algorithm.hpp'>CKF</a>| trackc;
    linkStyle 21 stroke: black;

    %% Host Kalman filter
    trackc -->|<a href='https://github.com/acts-project/traccc/blob/main/core/include/traccc/fitting/kalman_fitting_algorithm.hpp'>Kalman filter</a>| tracks;
    linkStyle 22 stroke: black;

    %% SYCL CKF
    ptrack -->|<a href='https://github.com/acts-project/traccc/blob/main/device/sycl/include/traccc/sycl/finding/combinatorial_kalman_filter_algorithm.hpp'>CKF</a>| trackc;
    linkStyle 23 stroke: blue;

    %% SYCL Kalman filter
    trackc -->|<a href='https://github.com/acts-project/traccc/blob/main/device/sycl/include/traccc/sycl/fitting/kalman_fitting_algorithm.hpp'>Kalman filter</a>| tracks;
    linkStyle 24 stroke: blue;

    %% CUDA CKF
    ptrack -->|<a href='https://github.com/acts-project/traccc/blob/main/device/cuda/include/traccc/cuda/finding/finding_algorithm.hpp'>CKF</a>| trackc;
    linkStyle 25 stroke: green;

    %% CUDA Kalman filter
    trackc -->|<a href='https://github.com/acts-project/traccc/blob/main/device/cuda/include/traccc/cuda/fitting/fitting_algorithm.hpp'>Kalman filter</a>| tracks;
    linkStyle 26 stroke: green;

Please note that due to the complexity of this software and its build system, it may be somewhat fragile in the face of compiler version changes. The following are general guidelines for getting traccc to compile:

• The C++ compiler must support C++20

In addition, the following requirements hold when CUDA is enabled:

• The CUDA Toolkit version must be greater than major version 12
• Ensure that the CUDA host compiler supports C++20 and is compatible with the nvcc compiler driver

The following table lists currently combinations of builds, compilers, and toolchains that are currently known to work (last updated 2022/01/24):

• Boost: program_options
• CMake
• (Optional) ROOT: RIO, Hist, Tree

The Spack project provides a particularly easy way to install the dependencies that you need to use traccc. In order to use Spack to manage your dependencies, simply create a new Spack environment using the provided environment file:

spack env create traccc spack.yaml
spack -e traccc concretize -f
spack -e traccc install
spack env activate traccc

This way, Spack will automatically download and install all dependencies necessary to use traccc with the CUDA, SYCL, Kokkos, and Alpaka programming models. When using Spack to manage your dependencies, make sure to compile traccc with the -DTRACCC_USE_SPACK_LIBS=ON flag.

Clone the repository and setup the data directory.

git clone https://github.com/acts-project/traccc.git
./traccc/data/traccc_data_get_files.sh

cmake --preset <preset> [options] -S traccc/ -B build
cmake --build build/

<build_directory>/bin/traccc_seq_example \
   --detector-file=geometries/odd/odd-detray_geometry_detray.json \
   --material-file=geometries/odd/odd-detray_material_detray.json \
   --grid-file=geometries/odd/odd-detray_surface_grids_detray.json \
   --use-detray-detector \
   --digitization-file=geometries/odd/odd-digi-geometric-config.json \
   --use-acts-geom-source --input-directory=odd/geant4_10muon_10GeV/ \
   --input-events=10

<build_directory>/bin/traccc_throughput_mt \
   --detector-file=geometries/odd/odd-detray_geometry_detray.json \
   --material-file=geometries/odd/odd-detray_material_detray.json \
   --grid-file=geometries/odd/odd-detray_surface_grids_detray.json \
   --use-detray-detector \
   --digitization-file=geometries/odd/odd-digi-geometric-config.json \
   --use-acts-geom-source --input-directory=odd/geant4_10muon_10GeV/ \
   --input-events=10 --processed-events=1000 --threads=1

Depending on the build options, can also use variants of the executables postfixed by _cuda, _sycl and _alpaka, with the same options.

Users can generate muon-like particle simulation data with the pre-built detray geometries:

# Generate telescope geometry data
<build_directory>/bin/traccc_simulate_telescope --gen-vertex-xyz-mm=0:0:0 --gen-vertex-xyz-std-mm=0:0:0 --gen-mom-gev=100:100 --gen-phi-degree=0:0 --gen-events=10 --gen-nparticles=2000 --output-directory=detray_simulation/telescope_detector/n_particles_2000/ --gen-eta=1:3

# Generate toy geometry data
<build_directory>/bin/traccc_simulate_toy_detector --gen-vertex-xyz-mm=0:0:0 --gen-vertex-xyz-std-mm=0:0:0 --gen-mom-gev=100:100 --gen-phi-degree=0:360 --gen-events=10 --gen-nparticles=2000 --output-directory=detray_simulation/toy_detector/n_particles_2000/ --gen-eta=-3:3 --constraint-step-size-mm=1 --search-window 3:3

# Generate drift chamber data
<build_directory>/bin/traccc_simulate_wire_chamber --gen-vertex-xyz-mm=0:0:0 --gen-vertex-xyz-std-mm=0:0:0 --gen-mom-gev=2:2 --gen-phi-degree=0:360 --gen-events=10 --gen-nparticles=100 --output-directory=detray_simulation/wire_chamber/n_particles_100/ --gen-eta=-1:1 --constraint-step-size-mm=1 --search-window 3:3

The simulation will also generate the detector json files (geometry, material and surface_grid) in the current directory. It is user's responsibility to move them to an appropriate place (e.g. <detector_directory>) and match them to the input file arguments of reconstruction chains.

If users have a geometry json file already, it is also possible to run simulation with traccc_simulate application

# Given that users have a geometry json file
<build_directory>/bin/traccc_simulate  --output-directory=<output-directory>  --detector-file=<geometry_file> --material-file=<material-file> --grid-file=<grid-file>  --event=10 --constraint-step-size-mm=1

There are three types of partial reconstruction chain users can operate: seeding_example, truth_finding_example, and truth_fitting_example where their algorithm coverages are shown in the table below. Each of them starts from truth measurements, truth seeds, and truth tracks, respectively.

The dirft chamber will not produce meaningful results with seeding_example as the current seeding algorithm is only designed for 2D measurement objects. Truth finding works OK in general but the combinatoric explosion can occur for a few unlucky events, leading to poor pull value distributions. The followings are example commands:

# Run cuda seeding example for toy geometry
<build_directory>/bin/traccc_seeding_example_cuda --input-directory=detray_simulation/toy_detector/n_particles_2000/ --check-performance --detector-file=<detector_directory>/toy_detector_geometry.json --material-file=<detector_directory>/toy_detector_homogeneous_material.json --grid-file=<detector_directory>/toy_detector_surface_grids.json --input-events=1 --track-candidates-range=3:30 --constraint-step-size-mm=1000 --run-cpu=1 --search-window 3:3

# Run cuda truth finding example for toy geometry
<build_directory>/bin/traccc_truth_finding_example_cuda --input-directory=detray_simulation/toy_detector/n_particles_2000/ --check-performance --detector-file=<detector_directory>/toy_detector_geometry.json --material-file=<detector_directory>/toy_detector_homogeneous_material.json --grid-file=<detector_directory>/toy_detector_surface_grids.json --input-events=1 --track-candidates-range=3:30 --constraint-step-size-mm=1000 --run-cpu=1 --search-window 3:3

# Run cuda truth finding example for drift chamber
<build_directory>/bin/traccc_truth_finding_example_cuda --input-directory=detray_simulation/wire_chamber/n_particles_100/ --check-performance --detector-file=<detector_directory>/wire_chamber_geometry.json --material-file=<detector_directory>/wire_chamber_homogeneous_material.json --grid-file=<detector_directory>/wire_chamber_surface_grids.json  --input-events=10 --track-candidates-range=6:30 --constraint-step-size-mm=1 --run-cpu=1 --search-window 3:3

# Run cpu truth fitting example for drift chamber
<build_directory>/bin/traccc_truth_fitting_example --input-directory=detray_simulation/wire_chamber/n_particles_2000_100GeV/ --check-performance --detector-file=<detector_directory>/wire_chamber_geometry.json --material-file=<detector_directory>/wire_chamber_homogeneous_material.json --grid-file=<detector_directory>/wire_chamber_surface_grids.json --input-events=10 --constraint-step-size-mm=1 --search-window 3:3

Users can open the performance root files (with --check-performance=true) and draw the histograms.

$ root -l performance_track_finding.root
root [0]
Attaching file performance_track_finding.root as _file0...
(TFile *) 0x3871910
root [1] finding_trackeff_vs_eta->Draw()

The traccc code is formatted using clang-format; the recommended way to ensure that your code is properly formatted is to use pre-commit. The pre-commit webpage has a useful guide for using the tool, but the simplest way of using it (without installing it as a pre-commit hook) is as follows. First, install the tool with your favourite Python package manager:

# With pip
$ pip install pre-commit
# With pipx
$ pip install pre-commit

The install step needs to be executed only once. After that, the code can be easily formatted as follows:

$ pre-commit run --all-files

Monitoring the event throughput of track reconstruction with the toy geometry

• Number of events: 100
• Number of tracks per event: 5000
• Algorithms used: seeding, track finding and track fitting

The following are potentially useful instructions for troubleshooting various problems with your build:

You may experience errors being issued about standard library features, for example:

/usr/include/c++/11/bits/std_function.h:435:145: note:         ‘_ArgTypes’
/usr/include/c++/11/bits/std_function.h:530:146: error: parameter packs not expanded with ‘...’:
  530 |         operator=(_Functor&& __f)

In this case, your nvcc host compiler is most likely incompatible with your CUDA toolkit. Consider installing a supported version and selecting it through the CUDAHOSTCXX environment variable at build-time.

You may encounter errors that look like the following:

ptxas fatal   : Unexpected non-ASCII character encountered on line 30
ptxas fatal   : Ptx assembly aborted due to errors

This means that you are compiling with a version later than CUDA 12.8 which has not been fixed and you are running in debug mode. This error is due to a bug in the CUDA toolkit. Either downgrade to CUDA 12.6 or disable the debug build.

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