leonarski_f
6dfe065365
* Fixed `jfjoch_broker` cancelling every data collection with a CUDA "out of memory" error after long operation: GPU memory no longer leaks with each collection. * Rugnux scales a rotation sweep until the per-frame scales settle instead of for a fixed three rounds, and says so when they did not - merged intensities, and the space group, resolution cut and frame rejection read off them, change accordingly; `--scaling-iterations` is now the cap on that loop (default 100). * Rugnux places every frame of a marCCD, SMV or miniCBF series at the spindle angle its own header states, so a series with missing frames, or with angles written modulo 360, is no longer read at the wrong geometry or refused. * Every rotation run writes two diagnostic files beside its reflections: `<prefix>_detector.jpg`, the detector projection with the pixel mask and the detected beam-stop shadow drawn on it, and `<prefix>_plot.txt`, one row per image. Reviewed-on: #82 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
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Software requirements
Operating system
Recommended operating system is Red Hat Enterprise Linux (RHEL) / Rocky Linux versions 8 or 9. For these operating systems we provide RPMs with pre-built binaries to simplify deployment. On an experimental basis we also build repositories for Ubuntu 22.04 and 24.04.
Running Jungfraujoch on Red Hat Enterprise Linux 7 is currently not tested and not recommended, but likely possible by providing some packages from external repositories.
Two programs additionally run on Windows 11: the desktop viewer jfjoch_viewer, shipped as a
pre-built installer, and rugnux, the offline analysis CLI, shipped as a .zip. Both
can be built from source with Visual Studio 2026 (MSVC) and CUDA 13.3 — the viewer additionally
needs Qt 6.11; see
jfjoch_viewer ▸ Building from source on Windows.
The Windows artefacts bundle the Qt runtime (viewer only) and, on the CUDA builds, the cuFFT DLL, so
end users need neither Qt nor a CUDA toolkit installed — only an NVIDIA GPU driver for the GPU path.
On Linux the portable archives link CUDA entirely statically and so need nothing but the driver.
rugnux is also built for 64-bit Arm Linux (GH200, DGX Spark). The rest of Jungfraujoch is
Linux-only and x86-64-only. See Release contents for the CPU baseline and
CUDA requirements of each released package.
Software dependencies
Required:
- C++20 compiler and C++20 standard library; recommended GCC 11+ or clang 14+ (Intel OneAPI and AMD AOCC also work)
- CMake version 3.26 or newer + a build tool (GNU make or Ninja)
HDF5, libtiff, libjpeg-turbo, zlib and Eigen used to be required system packages; they are now downloaded and built automatically by CMake (see the note below), so none of them needs to be installed. Beyond the optional dependencies listed below, no third-party library has to be provided by the host any more.
Optional:
- CUDA compiler version 12.8 or newer - required for the MX fast feedback indexer and GPU analysis
- FFTW library - for indexing if GPU/CUDA is absent (also auto-downloaded by CMake)
- Node.js - to build the frontend
- Qt version 6 (for jfjoch_viewer)
- OpenSSL 3.0 or newer - required to build jfjoch_viewer on Linux, where the fetched libcurl uses it for TLS; a build on OpenSSL 1.1 fails in libcurl
Many further dependencies (spdlog, Zstandard, HDF5, slsDetectorPackage, libzmq, libtiff,
libjpeg-turbo, Ceres, the fast feedback indexer, Catch2, ...) are downloaded automatically
by CMake and statically linked; building therefore requires network access on the first configure.
zlib (as zlib-ng in its zlib-compatible mode) and Eigen are among them: a copy already on the
machine is used instead if the configure is given -DZLIB_ROOT=<prefix> or -DEigen3_DIR=<dir>.
Others are vendored directly in the source tree. The complete list of third-party components, with
copyright holders, licenses and verbatim license texts, is in
Third-party software notices and the licenses/ directory.