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Khalil Daniel Ferjaoui
a43814801a
Implements a GPU version of the sequential ClusterFinder for single-frame cluster reconstrcution. Kernel (ClusterFinderCUDA.cuh): - Shared memory tiling with generalized halo loading for arbitrary cluster sizes (3x3, 5x5, ...) - Zero-initialization of shared memory to handle image boundary and partial edge-block cases. - Pedestal subtraction during shared memory loading. - Compile-time cluster geometry enabling full loop unrolling of the stencil reduction - Atomic global counter for lock-free cluster output across blocks. - RAII host wrapper; `ClusterFinderCUDA` struct.
aare
Data analysis library for PSI hybrid detectors
Documentation
Detailed documentation including installation can be found in Documentation
License
This project is licensed under the MPL-2.0 license. See the LICENSE file or https://www.mozilla.org/en-US/MPL/ for details.
Build and install
Prerequisites
- cmake >= 3.14
- C++17 compiler (gcc >= 8)
- python >= 3.10
Development install (for Python)
git clone git@github.com:slsdetectorgroup/aare.git --branch=v1 #or using http...
mkdir build
cd build
#configure using cmake
cmake ../aare -DAARE_PYTHON_BINDINGS=ON
#build (replace 4 with the number of threads you want to use)
make -j4
Now you can use the Python module from your build directory
import aare
f = aare.File('Some/File/I/Want_to_open_master_0.json')
To run from other folders either add the path to your conda environment using conda-build or add the module to your PYTHONPATH
export PYTHONPATH=path_to_aare/aare/build:$PYTHONPATH
Install using conda/mamba
#enable your env first!
conda install aare -c slsdetectorgroup # installs latest version
Install to a custom location and use in your project
Working example in: https://github.com/slsdetectorgroup/aare-examples
#build and install aare
git clone git@github.com:slsdetectorgroup/aare.git --branch=v1 #or using http...
mkdir build
cd build
#configure using cmake
cmake ../aare -DCMAKE_INSTALL_PREFIX=/where/to/put/aare
#build (replace 4 with the number of threads you want to use)
make -j4
#install
make install
#Now configure your project
cmake .. -DCMAKE_PREFIX_PATH=SOME_PATH
Local build of conda pkgs
conda build . --variants="{python: [3.11, 3.12, 3.13]}"