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https://github.com/slsdetectorgroup/aare.git
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Add Python bindings for CUDA cluster finder
- Add bind_ClusterFinderCUDA.hpp with pybind11 bindings for ClusterFinderCUDA - Build CUDA bindings as separate _aare_cuda.so to avoid segfaults from mixing nvcc and gcc compiled code in the same shared object - Re-export CUDA classes onto _aare in __init__.py so user code uses `from aare import ClusterFinderCUDA` regardless of which .so hosts the class - Factory in ClusterFinder.py selects backend; RuntimeError if GPU requested on CPU-only build - Update python/CMakeLists.txt: _aare_cuda module gated behind AARE_CUDA and AARE_PYTHON_BINDINGS - Add validation notebook: ~20x speedup vs sequential ClusterFinder
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@@ -15,20 +15,50 @@ else()
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find_package(pybind11 2.13 REQUIRED)
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endif()
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# Add the compiled python extension
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pybind11_add_module(
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_aare # name of the module
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src/module.cpp # source file
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)
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set_target_properties(_aare PROPERTIES
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LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}
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)
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# ---- Main CPU module --------------------------------------------------------
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# module.cpp is the only source for the main module. When AARE_CUDA=ON, the
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# CUDA bindings live in a *separate* Python extension (_aare_cuda.so) loaded
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# independently at runtime. This isolates the nvcc-compiled translation unit
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# into its own ELF image so pybind11's type registry cannot be corrupted by
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# weak-symbol collisions between gcc-emitted and nvcc-emitted template
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# instantiations.
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pybind11_add_module(_aare NO_EXTRAS src/module.cpp)
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target_link_libraries(_aare PRIVATE aare_core aare_compiler_flags)
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target_include_directories(_aare SYSTEM PRIVATE
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$<TARGET_PROPERTY:Minuit2::Minuit2,INTERFACE_INCLUDE_DIRECTORIES>
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)
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set_target_properties(_aare PROPERTIES
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LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/aare
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INTERPROCEDURAL_OPTIMIZATION FALSE
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)
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# ---- CUDA module (separate .so) --------------------------------------------
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if(AARE_CUDA)
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pybind11_add_module(_aare_cuda NO_EXTRAS src/cuda_bindings.cu)
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target_link_libraries(_aare_cuda PRIVATE aare_cuda aare_compiler_flags)
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target_include_directories(_aare_cuda SYSTEM PRIVATE
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$<TARGET_PROPERTY:Minuit2::Minuit2,INTERFACE_INCLUDE_DIRECTORIES>
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)
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set_target_properties(_aare_cuda PROPERTIES
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LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/aare
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INTERPROCEDURAL_OPTIMIZATION FALSE
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CUDA_RESOLVE_DEVICE_SYMBOLS ON
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CUDA_SEPARABLE_COMPILATION ON
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)
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target_compile_options(_aare_cuda PRIVATE
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$<$<COMPILE_LANGUAGE:CUDA>:--expt-relaxed-constexpr>
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$<$<COMPILE_LANGUAGE:CUDA>:--extended-lambda>
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$<$<COMPILE_LANGUAGE:CUDA>:-Xcompiler=-fvisibility=hidden>
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$<$<COMPILE_LANGUAGE:CUDA>:-Xcompiler=-fPIC>
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)
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endif()
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# List of python files to be copied to the build directory
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set( PYTHON_FILES
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@@ -51,9 +81,9 @@ foreach(FILE ${PYTHON_FILES})
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configure_file(${FILE} ${CMAKE_BINARY_DIR}/${FILE} )
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endforeach(FILE ${PYTHON_FILES})
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set_target_properties(_aare PROPERTIES
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LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/aare
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)
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# set_target_properties(_aare PROPERTIES
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# LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/aare
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# )
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set(PYTHON_EXAMPLES
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examples/play.py
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@@ -69,8 +99,13 @@ endforeach(FILE ${PYTHON_EXAMPLES})
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if(AARE_INSTALL_PYTHONEXT)
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set(AARE_PY_INSTALL_TARGETS _aare)
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if(AARE_CUDA)
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list(APPEND AARE_PY_INSTALL_TARGETS _aare_cuda)
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endif()
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install(
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TARGETS _aare
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TARGETS ${AARE_PY_INSTALL_TARGETS}
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EXPORT "${TARGETS_EXPORT_NAME}"
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LIBRARY DESTINATION aare
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COMPONENT python
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@@ -80,5 +115,5 @@ if(AARE_INSTALL_PYTHONEXT)
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FILES ${PYTHON_FILES}
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DESTINATION aare
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COMPONENT python
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)
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)
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endif()
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@@ -49,6 +49,44 @@ def ClusterFinderMT(image_size, cluster_size = (3,3), dtype=np.int32, n_sigma=5,
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return cls(image_size, n_sigma=n_sigma, capacity=capacity, n_threads=n_threads)
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def _cuda_available():
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"""True if this build of aare was compiled with -DAARE_CUDA=ON."""
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return hasattr(_aare, "ClusterFinderCUDA_Cluster3x3i")
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def ClusterFinderCUDA(image_size, cluster_size=(3,3), n_sigma=5, dtype=np.int32,
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capacity=1024, n_streams=1):
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"""
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Factory function to create a ClusterFinderCUDA object. Provides a cleaner
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syntax for the templated ClusterFinderCUDA in C++. API mirrors
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ClusterFinder() plus CUDA-specific knobs (n_streams).
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.. code-block:: python
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from aare import ClusterFinderCUDA
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cf = ClusterFinderCUDA(image_size=(512, 1024),
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cluster_size=(3, 3),
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n_sigma=5,
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n_streams=4)
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for frame in pedestal_frames:
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cf.push_pedestal_frame(frame)
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for i, frame in enumerate(data_frames):
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cf.find_clusters(frame, frame_number=i)
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clusters = cf.steal_clusters()
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"""
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if not _cuda_available():
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raise RuntimeError(
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"ClusterFinderCUDA is not available in this build of aare. "
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"Rebuild with -DAARE_CUDA=ON (and -DAARE_PYTHON_BINDINGS=ON)."
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)
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cls = _get_class("ClusterFinderCUDA", cluster_size, dtype)
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return cls(image_size,
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n_sigma=n_sigma,
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capacity=capacity,
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n_streams=n_streams)
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def ClusterCollector(clusterfindermt, dtype=np.int32):
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"""
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Factory function to create a ClusterCollector object. Provides a cleaner syntax for
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@@ -2,6 +2,23 @@
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# Make the compiled classes that live in _aare available from aare.
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from . import _aare
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# ---- CUDA module (optional) ------------------------------------------------
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# When the package was built with AARE_CUDA=ON, a sibling extension
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# _aare_cuda contains the ClusterFinderCUDA_* classes. We re-export them
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# onto _aare so user code can do `from aare import ClusterFinderCUDA_*`
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# regardless of which .so physically hosts the class. On a CPU-only build
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# the import fails silently and ClusterFinderCUDA_* classes simply aren't
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# present; the factory in ClusterFinder.py handles that case with a clear
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# RuntimeError.
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try:
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from . import _aare_cuda as _aare_cuda_mod
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for _name in dir(_aare_cuda_mod):
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if _name.startswith("ClusterFinderCUDA"):
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setattr(_aare, _name, getattr(_aare_cuda_mod, _name))
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del _name
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except ImportError:
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pass
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from . import transform
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from ._aare import File, RawMasterFile, RawSubFile, JungfrauDataFile
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@@ -14,6 +31,7 @@ from ._aare import corner
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# from ._aare import ClusterFinderMT, ClusterCollector, ClusterFileSink, ClusterVector_i
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from .ClusterFinder import ClusterFinder, ClusterCollector, ClusterFinderMT, ClusterFileSink, ClusterFile
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from .ClusterFinder import ClusterFinderCUDA, _cuda_available
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from .ClusterVector import ClusterVector
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from .Cluster import Cluster
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@@ -0,0 +1,101 @@
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// SPDX-License-Identifier: MPL-2.0
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#pragma once
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#include "aare/ClusterFinderCUDA.hpp"
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#include "aare/ClusterVector.hpp"
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#include "aare/NDView.hpp"
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#include "aare/Pedestal.hpp"
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#include "np_helper.hpp"
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#include <cstdint>
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#include <pybind11/pybind11.h>
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// #include <pybind11/stl.h>
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#include <pybind11/stl_bind.h>
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namespace py = pybind11;
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using pd_type = double;
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using namespace aare;
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#pragma GCC diagnostic push
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#pragma GCC diagnostic ignored "-Wunused-parameter"
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namespace aare {
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template <typename T, uint8_t ClusterSizeX, uint8_t ClusterSizeY,
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typename CoordType = uint16_t>
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void define_ClusterFinderCUDA(py::module &m, const std::string &typestr) {
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auto class_name = fmt::format("ClusterFinderCUDA_{}", typestr);
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using ClusterType = Cluster<T, ClusterSizeX, ClusterSizeY, CoordType>;
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using CF = ClusterFinderCUDA<ClusterType, uint16_t, pd_type>;
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py::class_<CF>(m, class_name.c_str())
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.def(py::init<Shape<2>, pd_type, size_t, int>(),
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py::arg("image_size"),
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py::arg("n_sigma") = 5.0,
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py::arg("capacity") = 1'000'000,
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py::arg("n_streams") = 1)
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.def_property(
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"nSigma",
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&CF::get_nSigma,
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&CF::set_nSigma,
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R"(Number of sigma above the pedestal to consider a photon during cluster finding.)")
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.def("push_pedestal_frame",
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[](CF &self, py::array_t<uint16_t> frame) {
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auto view = make_view_2d(frame);
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self.push_pedestal_frame(view);
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})
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.def("clear_pedestal", &CF::clear_pedestal)
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.def_property_readonly(
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"pedestal",
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[](CF &self) {
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auto pd = new NDArray<pd_type, 2>{};
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*pd = self.pedestal();
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return return_image_data(pd);
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})
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.def_property_readonly(
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"noise",
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[](CF &self) {
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auto arr = new NDArray<pd_type, 2>{};
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*arr = self.noise();
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return return_image_data(arr);
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})
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.def(
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"steal_clusters",
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[](CF &self, bool realloc_same_capacity) {
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ClusterVector<ClusterType> clusters =
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self.steal_clusters(realloc_same_capacity);
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return clusters;
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},
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py::arg("realloc_same_capacity") = false)
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.def(
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"find_clusters",
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[](CF &self, py::array_t<uint16_t> frame, uint64_t frame_number) {
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auto view = make_view_2d(frame);
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self.find_clusters(view, frame_number);
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},
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py::arg("frame"), py::arg("frame_number") = 0)
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.def(
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"find_clusters_batched",
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[](CF &self, py::array_t<uint16_t> frames, uint64_t first_frame) {
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// frames is expected as a 3D numpy array (n_frames, nrows, ncols)
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auto view = make_view_3d(frames);
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return self.find_clusters_batched(view, first_frame);
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},
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py::arg("frames"), py::arg("first_frame") = 0,
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R"(Process a 3D array of frames (n_frames, nrows, ncols) in parallel
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across the configured CUDA streams. Returns a list of ClusterVector, one per
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input frame.)");
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}
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} // namespace aare
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#pragma GCC diagnostic pop
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@@ -0,0 +1,67 @@
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// SPDX-License-Identifier: MPL-2.0
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//
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// CUDA-only Python extension module. Registers ClusterFinderCUDA along with
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// the ClusterVector and Cluster types it exposes in its return values, so
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// the module is self-contained — users can call steal_clusters() and get
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// back a usable ClusterVector without _aare needing to be imported first.
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#include "bind_Cluster.hpp"
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#include "bind_ClusterVector.hpp"
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#include "bind_ClusterFinderCUDA.hpp"
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#include <pybind11/pybind11.h>
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namespace py = pybind11;
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// Register the Cluster + ClusterVector pair for one (T, N, M) combination.
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// Subset of DEFINE_CLUSTER_BINDINGS from module.cpp: we register what
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// ClusterFinderCUDA actually returns, nothing more. File I/O, eta and
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// reduce_to_2x2 stay on the CPU side.
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#define DEFINE_CUDA_CLUSTER_TYPES(T, N, M, U, TYPE_CODE) \
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define_ClusterVector<T, N, M, U>(m, "Cluster" #N "x" #M #TYPE_CODE); \
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define_Cluster<T, N, M, U>(m, #N "x" #M #TYPE_CODE);
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#define DEFINE_BINDINGS_CLUSTERFINDER_CUDA(T, N, M, U, TYPE_CODE) \
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aare::define_ClusterFinderCUDA<T, N, M, U>( \
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m, "Cluster" #N "x" #M #TYPE_CODE);
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PYBIND11_MODULE(_aare_cuda, m) {
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// Types first — finders reference them in their signatures.
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// SFINAE excludes 2x2 on ClusterFinderCUDA, so we skip it here too.
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DEFINE_CUDA_CLUSTER_TYPES(int, 3, 3, uint16_t, i);
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DEFINE_CUDA_CLUSTER_TYPES(double, 3, 3, uint16_t, d);
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DEFINE_CUDA_CLUSTER_TYPES(float, 3, 3, uint16_t, f);
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DEFINE_CUDA_CLUSTER_TYPES(int, 5, 5, uint16_t, i);
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DEFINE_CUDA_CLUSTER_TYPES(double, 5, 5, uint16_t, d);
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DEFINE_CUDA_CLUSTER_TYPES(float, 5, 5, uint16_t, f);
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DEFINE_CUDA_CLUSTER_TYPES(int, 7, 7, uint16_t, i);
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DEFINE_CUDA_CLUSTER_TYPES(double, 7, 7, uint16_t, d);
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DEFINE_CUDA_CLUSTER_TYPES(float, 7, 7, uint16_t, f);
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DEFINE_CUDA_CLUSTER_TYPES(int, 9, 9, uint16_t, i);
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DEFINE_CUDA_CLUSTER_TYPES(double, 9, 9, uint16_t, d);
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DEFINE_CUDA_CLUSTER_TYPES(float, 9, 9, uint16_t, f);
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// Finders
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DEFINE_BINDINGS_CLUSTERFINDER_CUDA(int, 3, 3, uint16_t, i);
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DEFINE_BINDINGS_CLUSTERFINDER_CUDA(double, 3, 3, uint16_t, d);
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DEFINE_BINDINGS_CLUSTERFINDER_CUDA(float, 3, 3, uint16_t, f);
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DEFINE_BINDINGS_CLUSTERFINDER_CUDA(int, 5, 5, uint16_t, i);
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DEFINE_BINDINGS_CLUSTERFINDER_CUDA(double, 5, 5, uint16_t, d);
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DEFINE_BINDINGS_CLUSTERFINDER_CUDA(float, 5, 5, uint16_t, f);
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DEFINE_BINDINGS_CLUSTERFINDER_CUDA(int, 7, 7, uint16_t, i);
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DEFINE_BINDINGS_CLUSTERFINDER_CUDA(double, 7, 7, uint16_t, d);
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DEFINE_BINDINGS_CLUSTERFINDER_CUDA(float, 7, 7, uint16_t, f);
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DEFINE_BINDINGS_CLUSTERFINDER_CUDA(int, 9, 9, uint16_t, i);
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DEFINE_BINDINGS_CLUSTERFINDER_CUDA(double, 9, 9, uint16_t, d);
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DEFINE_BINDINGS_CLUSTERFINDER_CUDA(float, 9, 9, uint16_t, f);
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}
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#undef DEFINE_CUDA_CLUSTER_TYPES
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#undef DEFINE_BINDINGS_CLUSTERFINDER_CUDA
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@@ -0,0 +1,422 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "789d5aab-0c75-4ed3-94bf-e66172f6737c",
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys; sys.path.append('/home/ferjao_k/aare/build')\n",
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"\n",
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"from pathlib import Path\n",
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"import matplotlib.pyplot as plt\n",
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"from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
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"import numpy as np\n",
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"import boost_histogram as bh\n",
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||||
"import time\n",
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"\n",
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"from aare import File, ClusterFinder, ClusterFinderMT, ClusterCollector, ClusterFinderCUDA"
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]
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},
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||||
{
|
||||
"cell_type": "code",
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||||
"execution_count": 2,
|
||||
"id": "3f6089d6-2245-4aca-aad3-2417c16afae2",
|
||||
"metadata": {},
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||||
"outputs": [],
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"source": [
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"def make_hist(clusters):\n",
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" h = bh.Histogram(bh.axis.Regular(100, -2, 4000))\n",
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" h.fill(clusters.sum())\n",
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" return h"
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]
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},
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{
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||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "415b09d4-d0d0-4166-8601-9d84302617bd",
|
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"metadata": {},
|
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"outputs": [
|
||||
{
|
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Image size: (400, 400)\n",
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"Pedestal frames: 1000\n",
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"Data frames: 40000\n"
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]
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}
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],
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"source": [
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"base = Path('/mnt/sls_det_storage/matterhorn_data/aare_test_data/')\n",
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"f = File(base / 'Moench03new/cu_half_speed_master_4.json')\n",
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"\n",
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"n_frames_pd = 1000\n",
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"N = 40000\n",
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"cluster_size = (3, 3)\n",
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"image_size = (f.rows, f.cols)\n",
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||||
"capacity = 100_000 #3_000_000\n",
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||||
"\n",
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||||
"print(f'Image size: {image_size}')\n",
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||||
"print(f'Pedestal frames: {n_frames_pd}')\n",
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||||
"print(f'Data frames: {N}')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "5531b79d-12ad-4de6-84b7-0224b10d13c3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Pedestal (both finders trained on identical frames)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e33b421f-969a-41d6-8aa6-5bd859a88fb3",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"- Modify the boolean `SERIAL` to choose between the sequential CPU version (ClusterFinder) and its multi-threaded homologue (ClusterFinderMT)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"id": "6ee7c469-5a23-421a-9bb4-a76e8f57a0c1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"SERIAL = True"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"id": "9d8f69bf-27f6-48c8-b2da-4157d67e6b04",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"if(SERIAL):\n",
|
||||
" cf_cpu = ClusterFinder(image_size, cluster_size, capacity=capacity)\n",
|
||||
"else:\n",
|
||||
" cf_cpu = ClusterFinderMT(image_size, cluster_size, capacity=capacity, n_threads=24)\n",
|
||||
" sink = ClusterCollector(cf_cpu)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"id": "cbdcb805-708b-4205-bda9-2aa163d0e81f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"N_STREAMS = 1 \n",
|
||||
"cf_cuda = ClusterFinderCUDA(image_size, cluster_size, capacity=capacity, n_streams=N_STREAMS)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"id": "1546f405-1bf6-4073-ab35-8134be695a6c",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Pedestal (1000 frames): 0.498s\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"t0 = time.perf_counter()\n",
|
||||
"for _ in range(n_frames_pd):\n",
|
||||
" img = f.read_frame()\n",
|
||||
" cf_cpu.push_pedestal_frame(img.copy())\n",
|
||||
" cf_cuda.push_pedestal_frame(img.copy())\n",
|
||||
"print(f'Pedestal ({n_frames_pd} frames): {time.perf_counter() - t0:.3f}s')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "5df035f0-7a27-4d5a-8f8d-c94e745bcd1a",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Read all data frames into memory (I/O out of the timing loop)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"id": "4573be3d-5ba8-4e18-bab0-c874f2b7dcb2",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Reading 40000 frames: 2.002s (19985 FPS, 6098.804 GB/s)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"f.seek(n_frames_pd)\n",
|
||||
"t0 = time.perf_counter()\n",
|
||||
"data = f.read_n(N)\n",
|
||||
"t_io = time.perf_counter() - t0\n",
|
||||
"print(f'Reading {N} frames: {t_io:.3f}s ({N/t_io:.0f} FPS, '\n",
|
||||
" f'{f.bytes_per_frame * N / 1024**2 / t_io:.3f} GB/s)')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c1e9362e-dcc2-41af-bfa1-c62d0968477e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## CPU clustering"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"id": "fbb14fda-2852-4b73-ba2c-9952abac1d99",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CPU clustering: 73.978s (541 FPS, 55449596 clusters, 1386.24/frame)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"t0 = time.perf_counter()\n",
|
||||
"for frame in data:\n",
|
||||
" cf_cpu.find_clusters(frame)\n",
|
||||
"t_cpu = time.perf_counter() - t0\n",
|
||||
"\n",
|
||||
"if(SERIAL):\n",
|
||||
" clusters_cpu = cf_cpu.steal_clusters(realloc_same_capacity=False)\n",
|
||||
" n_clusters_cpu = clusters_cpu.size\n",
|
||||
" \n",
|
||||
" hist_cpu = make_hist(clusters_cpu)\n",
|
||||
"else:\n",
|
||||
" cf_cpu.stop()\n",
|
||||
" sink.stop()\n",
|
||||
" \n",
|
||||
" clusters_cpu = sink.steal_clusters() #cf_cpu.steal_clusters(realloc_same_capacity=False)\n",
|
||||
" \n",
|
||||
" hist_cpu = bh.Histogram(bh.axis.Regular(100, -2, 4000))\n",
|
||||
" n_clusters_cpu = 0\n",
|
||||
" for cv in clusters_cpu:\n",
|
||||
" hist_cpu.fill(cv.sum())\n",
|
||||
" n_clusters_cpu += cv.size\n",
|
||||
" \n",
|
||||
"print(f'CPU clustering: {t_cpu:.3f}s ({N/t_cpu:.0f} FPS, '\n",
|
||||
" f'{n_clusters_cpu} clusters, {n_clusters_cpu/N:.2f}/frame)')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "8cfd7091-8020-49ff-b033-28bf773983d8",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## CUDA clustering"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"id": "4b8df93b-9a1b-41a5-9fed-9cda295fb523",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CUDA clustering: 3.880s (10310 FPS, 55810704 clusters, 1395.27/frame)\n",
|
||||
"Speedup (CPU / CUDA): 19.07×\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Warmup: first kernel launch pays CUDA context + pedestal H2D upload cost\n",
|
||||
"cf_cuda.find_clusters(data[0])\n",
|
||||
"_ = cf_cuda.steal_clusters(realloc_same_capacity=False)\n",
|
||||
"\n",
|
||||
"t0 = time.perf_counter()\n",
|
||||
"for frame in data:\n",
|
||||
" cf_cuda.find_clusters(frame)\n",
|
||||
"t_cuda = time.perf_counter() - t0\n",
|
||||
"clusters_cuda = cf_cuda.steal_clusters(realloc_same_capacity=False)\n",
|
||||
"n_clusters_cuda = clusters_cuda.size\n",
|
||||
"print(f'CUDA clustering: {t_cuda:.3f}s ({N/t_cuda:.0f} FPS, '\n",
|
||||
" f'{n_clusters_cuda} clusters, {n_clusters_cuda/N:.2f}/frame)')\n",
|
||||
"print(f'Speedup (CPU / CUDA): {t_cpu / t_cuda:.2f}×')\n",
|
||||
"\n",
|
||||
"hist_cuda = make_hist(clusters_cuda)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "1fda99ee-db54-40c1-8e5b-51168a33fb96",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# BATCH_SIZE = 500\n",
|
||||
"\n",
|
||||
"# # warmup\n",
|
||||
"# _ = cf_cuda.find_clusters_batched(data[:1], first_frame=0)\n",
|
||||
"\n",
|
||||
"# t0 = time.perf_counter()\n",
|
||||
"# clusters_cuda_per_frame = []\n",
|
||||
"# for start in range(0, N, BATCH_SIZE):\n",
|
||||
"# stop = min(start + BATCH_SIZE, N)\n",
|
||||
"# clusters_cuda_per_frame.extend(\n",
|
||||
"# cf_cuda.find_clusters_batched(data[start:stop], first_frame=start)\n",
|
||||
"# )\n",
|
||||
"# t_cuda = time.perf_counter() - t0\n",
|
||||
"\n",
|
||||
"# n_clusters_cuda = sum(cv.size for cv in clusters_cuda_per_frame)\n",
|
||||
"\n",
|
||||
"# print(f'CUDA clustering: {t_cuda:.3f}s ({N/t_cuda:.0f} FPS, '\n",
|
||||
"# f'{n_clusters_cuda} clusters, {n_clusters_cuda/N:.2f}/frame)')\n",
|
||||
"# print(f'Speedup (CPU / CUDA): {t_cpu / t_cuda:.2f}×')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "b7df7b9d-6873-46c4-b6bc-7e4238a5bdf7",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# def make_hist_from_batch(result_list):\n",
|
||||
"# h = bh.Histogram(bh.axis.Regular(100, -2, 4000))\n",
|
||||
"# energies = [np.asarray(cv.sum()).ravel() for cv in result_list if cv.size > 0]\n",
|
||||
"# if energies:\n",
|
||||
"# h.fill(np.concatenate(energies))\n",
|
||||
"# return h\n",
|
||||
"\n",
|
||||
"# hist_cuda = make_hist_from_batch(clusters_cuda_per_frame)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "b966cce1-0f73-4565-a707-1aec2a69f75e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Agreement check: \n",
|
||||
"- Cluster counts should match closely.\n",
|
||||
"- However, as the CUDA CF updates the pedestal once per frame rather than per-pixel, a small divergence after the first few frames is expected."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "d3a850df-7df0-485b-971d-381cfdc6be81",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Cluster count diff: 361108 (0.65%)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"diff = abs(n_clusters_cpu - n_clusters_cuda)\n",
|
||||
"rel = diff / max(n_clusters_cpu, 1)\n",
|
||||
"print(f'Cluster count diff: {diff} ({rel:.2%})')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "81638ad3-6112-4bd7-a93a-f4d97f1f94cf",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Plots"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"id": "9adeea2f-9309-4c0a-905b-7d1203ba1f4e",
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"image/png": "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 truncated
|
||||
"text/plain": [
|
||||
"<Figure size 900x600 with 2 Axes>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"output_type": "display_data"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"fig, (ax_spec, ax_ratio) = plt.subplots(\n",
|
||||
" 2, 1, figsize=(9, 6), sharex=True,\n",
|
||||
" gridspec_kw={'height_ratios': [3, 1]}\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"edges = hist_cpu.axes[0].edges\n",
|
||||
"cpu_vals = hist_cpu.values()\n",
|
||||
"cuda_vals = hist_cuda.values()\n",
|
||||
"\n",
|
||||
"ax_spec.stairs(cpu_vals, edges, label=f'CPU ({n_clusters_cpu} clusters)')\n",
|
||||
"ax_spec.stairs(cuda_vals, edges, label=f'CUDA ({n_clusters_cuda} clusters)', linestyle='--')\n",
|
||||
"ax_spec.set_ylabel('Counts')\n",
|
||||
"ax_spec.set_title('Cluster energy spectrum: CPU vs CUDA')\n",
|
||||
"ax_spec.legend()\n",
|
||||
"ax_spec.grid(alpha=0.3)\n",
|
||||
"\n",
|
||||
"with np.errstate(divide='ignore', invalid='ignore'):\n",
|
||||
" ratio = np.where(cpu_vals > 0, cuda_vals / cpu_vals, np.nan)\n",
|
||||
"\n",
|
||||
"ax_ratio.stairs(ratio, edges, color='k')\n",
|
||||
"ax_ratio.axhline(1.0, color='gray', linewidth=0.5)\n",
|
||||
"ax_ratio.set_ylabel('CUDA / CPU')\n",
|
||||
"ax_ratio.set_xlabel('Energy [ADU]')\n",
|
||||
"ax_ratio.set_ylim(0.5, 3.5)\n",
|
||||
"ax_ratio.grid(alpha=0.3)\n",
|
||||
"\n",
|
||||
"plt.tight_layout()\n",
|
||||
"plt.show()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a6ca4581-bb29-4c74-a34a-bd427551a39b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.15"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Reference in new issue
Block a user