- main moved ClusterFinder to FastPedestal and dropped Pedestal's
push_fast/get_sum/get_sum2, which ClusterFinderFrozen and both CUDA
drivers still called.
- Move all three to FastPedestal. Its uniform sample count is what the
CUDA kernel already assumes, and matching ClusterFinder keeps CPU and
GPU warm-up identical, which ClusterFinderFrozen exists to verify.
FastPedestal gains get_sum/get_sum2 to de-interleave its internal
Entry{sum,sum2} for the CUDA upload.
- Also expose min_pedestal_samples on these constructors and factories,
make the factories keyword-only after image_size. Same API in C++ and Python.
Three files conflicted:
- ClusterFinderMT::collect(): both sides rewrote it. Keep this branch's
version (f4bbbc9), which reads the stop flags before sweeping, over
main's (afce91b), which still samples queue emptiness mid-condition.
The staged splice had also lost a brace, swallowing popFront().
- python/aare/ClusterFinder.py: restore the CUDA API deleted by the
resolution (_cuda_available, _CUDA_ALGORITHMS, ClusterFinderCUDA,
find_cluster_views_batched_iter, ClusterFinderCUDAGraph), and main's
min_pedestal_samples forwarding, dropped as an adjacent hunk.
- bind_ClusterVector.hpp: keep py::module_local() (133cedf) alongside
main's new docstrings; without it _aare and _aare_cuda cannot both
register Cluster/ClusterVector under pybind11 3.
Does not build on its own: ClusterFinderFrozen and the CUDA drivers
still call Pedestal APIs main removed. Fixed in the follow-up.
Cleaned up the helper functions by:
- Added template for make_view (and new make_const_view) instead of the
copy paste implementation
- checks c_style and dimensions inside make_view
- test binding to be able to probe the checks in make_view
Call sites can be reviewed in a second PR since usage of const or
strictness of conversion depends...
We have no code in aare to deal with RawFiles that have missing packets
if they are not padded to full size or frames with packet loss dropped.
Added:
- check that we don't try to read files that are not supported
- python bindings for frame discard policy
at least we get the check in:
closes #171
---------
Co-authored-by: AliceMazzoleni99 <alice.mazzoleni@psi.ch>
- seek to the edge of a file + read_into would select the wrong file
- Visible for example in f.read_frame(index) in python
Added fix, release notes and a small test.
closes #371
Co-authored-by: AliceMazzoleni99 <alice.mazzoleni@psi.ch>
- renamed pedestal.hpp to bind_Pedestal.hpp for python bindings
- variance is now always calculated in double and made private
- cost correctness in a few places
- removed push_fast and explicit updates of mean from Pedestal. If
performance is needed use FastPedestal
- Pedestal no longer caches std
Improved API for ClusterFile
- read_frame now returns a std::optional<ClusterVector> to differentiate
between an empty frame and end of file
- f.frames() produces an iterator over frames
- f.chunks() produces an iterator reading chunks of clusters
- python string repr for Cluster and Eta
- Context manager for RawMasterFile
- slice operator for ROI
---------
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
ClusterFinderCUDA mixed three things: which kernel runs and what device
state it needs, the per-stream buffers, and the batching pipeline. A
second algorithm meant a second copy of all of it. The pieces are now
separate and the driver is templated on the algorithm.
cuda::FixedWindow (clusterfinder_algo.cuh) the fixed-size window
search: DeviceState pointer bundle, launch(), and the pedestal
transforms. Stateless; every pointer is supplied by the caller,
so external code can drive the kernel on its own memory and
stream without touching the driver.
StreamContext<Algo> one stream's buffers, RAII, non-movable.
ClusterFinderCUDA<Algo> the pipeline, unchanged in behaviour.
no_2x2_cluster moves to Cluster.hpp so the kernel and algorithm headers
no longer drag in ClusterFinder.hpp. The Python factory gains
algorithm="fixed_window"; memory stays internal.
Kernel SASS identical to before. Positions identical against
ClusterFinderFrozen for 3x3, 5x5, 9x9 (stream and graph finders). New
Catch2 test drives FixedWindow::launch on raw cudaMalloc buffers and
requires exact agreement with the driver.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
- Updated error message to mention out of range and which file it
applies to
- If there are different number of frames in the subfiles, take the
minimum and print a warning
Closes: #363
Closes: #362
- Default `ClusterVector` move operations simplifying the code and
fixing a bug.
- Fixed inconsistent frame number type (uint64/int32)
- Validate Python masks as one-dimensional, C-contiguous Boolean arrays,
handle empty masks safely, and reserve filtered storage based on the
selected cluster count.
- Align the C++ and Python API documentation with the implementation,
including concise `hitmap` and reduction documentation and a correctly
rendered constructor example.
collect() cached queue emptiness across sweeps, so a worker's last
write could land after the final sweep but before shutdown was
observed, stranding that frame's clusters. Read the stop flags before
sweeping instead: once workers are joined, a sweep that moves nothing
proves the queues are drained.
Found via ClusterFinderCUDA.ipynb's drift plot, which jumped by one
frame's worth of clusters at the very end.
- handles file reading of disabled udp ports
- adds members disabled_udp_ports in master file as an optional
- adds member diasbled_udp_port_types in master file as an optional
- Treats enabled udp ports as ROIs
- merges/combines consecutive ROis into one
### To discuss:
- right now can only handle either ROI or disabled udp ports (disabled
udp ports has higher precedence)
- Should Frames in File be removed from Master File as value is nonsense
for disabled udp ports
---------
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
Doxygen never read *.cuh and needs EXTENSION_MAPPING to parse it; add
both, plus C++/Python pages and AARE_CUDA build docs.
Delete ClusterFinderCUDA_old.hpp (declared the same class, polluting
the API page), utils/batch.hpp, and the deck. All unreferenced.
TODO:
- Frozen + CUDAGraph: bound in Python, headers not installed. Ship or drop?
- test_ClusterFinderCUDA.py guarded on _cuda_available()
- AARE_CUDA_ARCHITECTURES=native needs CMake 3.24, project min is 3.15
- make docs somewhere with sphinx+breathe; new .rst files never rendered
- fmt nvcc warnings: gone at -std=c++20 or with -isystem
Host-side pipeline work ported from the benchmark branch. The kernel is
unchanged, so cluster results are identical.
- collect_view()/BatchView: read clusters in place from the pinned D2H
buffer instead of copying one ClusterVector per frame. The view is
released back to the finder, so keep nothing that borrows it.
- reserve_output_slots(), chunk_size_for(): pre-pin the output slots and
size a batch to fit one.
- find_cluster_views_batched_iter(): submits chunk i+1 before collecting
chunk i.
- time_kernels, default off. avg_kernel_time_ms() returns NaN unless
enabled, and always for the Graph variant.
- n_streams defaults to 4 everywhere.
ClusterFinderCUDA.ipynb cut to CPU MT vs batched CUDA plus a cluster-set
diff, and it now reports peak clusters/frame.
Removed src/ClusterFinderCUDA{,_old}.test.cu and src/Makefile (early C++
benchmarks, never in the CMake build), the perf and Frozen-vs-CUDA
notebooks, and helper.py.
Optimized pedestal tracking with FastPedestal and performance improvements to the cluster finder.
- ~2x faster hitting 14k FPS on benchmark dataset on 16 core threadripper pro
**Changes**
- No bounds check on interior of frame. (only within half of a cluster)
(~15% improvement)
- Cache threshold (std*n_rms) Biggest improvement comes from not
computing std for each access
- Cache pedestal subtracted frame
- Switched to FastPedestal which assumes we reached steady state
**Things tried but rejected:**
- Always store cluster values, commit on val==max (~15% drop in frame
rate)
**Options**
- using 16 bit clusters and 16 bit pedestal. (slows down the single
threaded case but allow us to reach 12k with multi threading)
**Other notes on performance**
- Passing in memory frames from python and doing nothing: 0.8us/frame
- Passing + pedestal subtraction: 28us/frame
- Passing + pedestal + cluster finding (no store): 652us/frame
- Passing + pedestal + cluster finding: 886 us/frame
- Reading files from nfs shares tops out at ~1GB/s for single threaded
reads.
- MultiThreadedFileReader uses our File wrapper to read a generic file
in parallel
- Placed in aare::experimental to show that it's not production ready
- Diagnostic twin of ClusterFinder: identical decisions, but the pedestal is
frozen per frame (snapshot at frame start, updates deferred to frame end),
matching the CUDA kernel's update model.
- Isolates pedestal-update timing as the sole remaining CPU/GPU mismatch.
- Adds class + bindings + factory, validation notebook, and helper utilities.
- Move Chi2.hpp from include/aare/ to src/ (private)
- Pimpl on FitModel<Model>: MnUserParameters/MnStrategy behind opaque
src/FitModelImpl.hpp, no Minuit2 includes in public headers
- Move fit_pixel/fit_3d bodies to Fit.cpp with explicit instantiations
for all 8 models; drop FCN template param from public API
- CMake: aare::Minuit2 wrapped in $<BUILD_INTERFACE:...> (hidden from
exported targets, same pattern as lmfit), MINUIT2_INSTALL OFF, Chi2.hpp
removed from PUBLICHEADERS
- Update python bindings and benchmark callsites accordingly
---------
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
Co-authored-by: Alice <alice.mazzoleni@psi.ch>
Collection of small improvements in usability:
- NDView works for large arrays
- Direct subtraction of Pedestal from np.array
- len() support for python bindings of files
- reshape image directly in decoder such that first dimension is num
counters
- take into account chip artefact in decoder.
---------
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
- Eliminate the ~200–300 µs inter-batch idle gap by allowing two batches
to be in-flight simultaneously:
- submit_batch() enqueues H2D+kernel+D2H without blocking
- collect() syncs via cudaEventSynchronize (not
cudaStreamSynchronize) so a queued second batch runs uninterrupted.
- Two ping-pong output slots (NUM_SLOTS=2) with per-slot pinned buffers
and cudaEventDisableTiming sync events.
- find_clusters_batched() keeps its direct implementation.
* Measured: 0.026 -> 0.022 ms/frame (~18%).
- Device pedestal arrays (mean/sum/sum2) are now float instead of
double: halves global-memory bandwidth for pedestal reads/writes and
eliminates FP64 arithmetic in the kernel (3.3x kernel speedup,
15µs -> 4.6µs).
- Replace the per-cluster push_back loop in the D2H drain with a
single resize()+memcpy().
Rework the multi-stream pipeline to eliminate per-frame sync barriers and
fix the D2H staging architecture.
Sync reduction:
- Replace one cudaStreamSynchronize per frame with one per stream per batch,
cutting synchronisation calls from O(n_frames x n_streams) to O(n_streams)
- Introduce a unified per-frame D2H output layout [uint32_t count | clusters[max]]
stored in a single class-level lazy-allocated pinned pool (h_output_pinned),
replacing the per-stream separate cluster/count device buffers
- Move CUDA event pool from per-stream fixed-size to per-frame-slot lazy-allocated,
enabling correct kernel timing across any batch size
Pinned H2D without CPU-side copy:
- Add register_input_buffer(ptr, bytes) / unregister_input_buffer() wrapping
cudaHostRegister so callers can pin their existing batch buffer once; all
find_clusters_batched() slices then transfer at DMA speed (~22 GB/s) instead
of ~15 GB/s for pageable, with no extra memcpy or WC-memory penalty
Result (RTX 4090, 400x400 uint16, 3x3 clusters, batch=2000, 5 streams):
Before: ~34 µs/frame -> After: ~28 µs/frame (−18 %)
- Use per-stream pinned host staging buffers for truly async CUDA transfers.
- Avoid reserving full device capacity per result frame.
- Reduce kernel work by delaying cluster payload construction.
- Use squared comparisons and removing per-pixel sqrtf() ops.
- Stencil arithmetic and shared memory use float (COMPUTE_TYPE alias).
- Pedestal accumulation stays double to preserve variance accuracy.
Notes:
- On RTX 4090, FP32 throughput is ~64× higher than FP64, so moving
stencil math to float improves performance.
- Using float also avoids shared memory bank conflicts: stride-18 maps
to distinct banks for 32-bit values, but caused conflicts with 64-bit.
- Allowing the users more flexibility to play around with custom eta
functions without touching the c++ code
- passing vector of eta values to ``transform_eta_values``
```
from aare import Interpolator, ClusterVector, Etai, Cluster
import numpy as np
def custom_eta(cluster_pixel_coordinate_x, cluster_pixel_coordinate_y, cluster_data):
# dummy custom eta function that just returns the sum of the cluster data
eta = Etai()
eta.x = 0.1 # dummy x value
eta.y = 0.1 # dummy y value
eta.sum = np.sum(cluster_data) # sum of the cluster data as the "energy
return eta
# Create a dummy eta distribution and bins
eta_distribution = np.zeros((10, 10, 1)) # dummy eta distribution
etax_bins = np.linspace(0, 1.0, 11)
etay_bins = np.linspace(0, 1.0, 11)
e_bins = np.array([0., 10.]) # dummy energy bins
# Create the interpolator
interpolator = Interpolator(eta_distribution, etax_bins, etay_bins, e_bins)
# Create a dummy cluster vector
cluster_vector = ClusterVector()
cluster_vector.push_back(Cluster(10, 5, np.ones(shape=9, dtype = np.int32)))
cluster_vector.push_back(Cluster(20, 10, np.ones(shape=9, dtype = np.int32)))
# Create dummy etas for the clusters
cluster_array = np.array(cluster_vector)
etas = np.array([custom_eta(cluster["x"], cluster["y"], cluster["data"]) for cluster in cluster_array])
# transform eta values to uniform coordinates
uniform_coordinates = interpolator.transform_eta_values(etas)
# Interpolate to get the photon coordinates e.g. apply interpolation logic
photon_coordinates_x = cluster_array["x"] + uniform_coordinates["x"] # add to pixel coordinate
photon_coordinates_y = cluster_array["y"] + uniform_coordinates["y"] # add to pixel coordinate
```
advantage: full control over interpolation logic,
downside: inefficient quite some loops in python
- passing pre computed eta values to interpolate function
```
Interpolator.interpolate(cluster_vector, etas)
```
downside: less flexibility in interpolation logic.
downside: People might misuse it instead of using interpolate directly
with a pre compiled eta function implemented in c++
- 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
To improve codebase quality and reduce human error, this PR introduces
the pre-commit framework. This ensures that all code adheres to project
standards before it is even committed, maintaining a consistent style
and catching common mistakes early.
Key Changes:
- Code Formatting: Automated C++ formatting using clang-format (based on
the project's .clang-format file).
- Syntax Validation: Basic checks for file integrity and syntax.
- Spell Check: Automated scanning for typos in source code and comments.
- CMake Formatting: Standardization of CMakeLists.txt and .cmake
configuration files.
- GitHub Workflow: Added a CI action that validates every Pull Request
against the pre-commit configuration to ensure compliance.
The configuration includes a [ci] block to handle automated fixes within
the PR. Currently, this is disabled. If we want the CI to automatically
commit formatting fixes back to the PR branch, this can be toggled to
true in .pre-commit-config.yaml.
```yaml
ci:
autofix_commit_msg: [pre-commit] auto fixes from pre-commit hooks
autofix_prs: false
autoupdate_schedule: monthly
```
The last large commit with the fit functions, for example, was not
formatted according to the clang-format rules. This PR would allow to
avoid similar mistakes in the future.
Python fomat with `ruff` for tests and sanitiser for `.ipynb` notebooks
can be added as well.
- Set parameter starting values, limits or fix through name as well as
index
- Updated parameter names for the scurve
- Fast approximation to erf function (~10% speedup of fitting)
---------
Co-authored-by: Khalil Ferjaoui <khalilferjaoui@yahoo.fr>
## Unified Minuit2 fitting framework with FitModel API
### Models (`Models.hpp`)
Consolidate all model structs (Gaussian, RisingScurve, FallingScurve)
into a
single header. Each model provides: `eval`, `eval_and_grad`, `is_valid`,
`estimate_par`, `compute_steps`, and `param_info` metadata. No Minuit2
dependency.
### Chi2 functors (`Chi2.hpp`)
Generic `Chi2Model1DGrad` (analytic gradient) templated on the model
struct.
Replaces the separate Chi2Gaussian, Chi2GaussianGradient,
Chi2Scurves, and Chi2ScurvesGradient headers.
### FitModel (`FitModel.hpp`)
Configuration object wrapping `MnUserParameters`, strategy, tolerance,
and
user-override tracking. User constraints (fixed parameters, start
values, limits)
always take precedence over automatic data-driven estimates.
### Fit functions (`Fit.hpp`)
- `fit_pixel<Model, FCN>(model, x, y, y_err)` -> single-pixel,
self-contained
- `fit_pixel<Model, FCN>(model, upar_local, x, y, y_err)` -> pre-cloned
upar for hot loops
- `fit_3d<Model, FCN>(model, x, y, y_err, ..., n_threads)` ->
row-parallel over pixel grid
### Python bindings
- `Pol1`, `Pol2`, `Gaussian`, `RisingScurve`, `FallingScurve` model
classes with
`FixParameter`, `SetParLimits`, `SetParameter`, and properties for
`max_calls`, `tolerance`, `compute_errors`
- Single `fit(model, x, y, y_err, n_threads)` dispatch replacing the old
`fit_gaus_minuit`, `fit_gaus_minuit_grad`, `fit_scurve_minuit_grad`,
etc.
### Benchmarks
- Updated `fit_benchmark.cpp` (Google Benchmark) to use the new FitModel
API
- Jupyter notebooks for 1D and 3D S-curve fitting (lmfit vs Minuit2
analytic)
- ~1.8x speedup over lmfit, near-linear thread scaling up to physical
core count
---------
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
Matterhorn10 Transform
some other Transformations from pyctbGUI
added method get_reading_mode for easier error handling in decoders
## TODO:
- proper error handling for all other decoders
- proper documentation for all other decoders
- refactoring all other decoders to store hard coded values in a Struct
ChipSpecification
Reading multiple ROI's for aare
- read_frame, read_n etc throws for multiple ROIs
- new functions read_ROIs, read_n_ROIs
- read_roi_into (used for python bindings - to not copy)
all these functions use get_frame or get_frame_into where one passes the
roi_index
## Refactoring:
- each roi keeps track of its subfiles that one has to open e.g.
subfiles can be opened several times
- refactored class DetectorGeometry - keep track of the updated module
geometries in new class ROIGeometry.
- ModuleGeometry updates based on ROI
## ROIGeometry:
- stores number of modules overlapping with ROI and its indices
- size of ROI
Note: only tested size of the resulting frames not the actual values
---------
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
Co-authored-by: Erik Fröjdh <erik.frojdh@gmail.com>
- automatically run python tests
- automatically run test using data files on local runner from gitea
- fixed some of the workflows
---------
Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
kferjaoui
lunin_l