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leonarski_f dd67cffde6 Docs: bring the release notes and the rugnux page up to rc.164

The changelog section had grown to 22 entries written from the commits
rather than for a user. Collapsed to 13, each saying what is gained
before what changed, with related work merged - the six anisotropy
entries into one, five space-group entries into two, five
integration-radius entries into one - and four things added that had no
entry at all: the packaging split, the statically linked cuFFT, the Ceres
back-end drop and REPORT_VERSION reaching 3.

CPU_DATA_ANALYSIS gains 9.5, the adaptive integration radius: the r80
measurement, the clamp, the constant-ring-area r3, the convergence test,
why it applies to the final pass only and the density fallback. Three
passages there had gone stale against the code: 13.1 still described the
I/sigma quantile fallback that b90fcfb98 deleted, and 13.5 said the gate
tests delta_B when it tests delta_B_linear and that the high-symmetry
caution fires wherever the Laue class leaves one free direction, which
0da593b32 narrowed.

RUGNUX gains an Installation section - there was no page anywhere saying
where rugnux comes from - a synopsis, a worked first run naming the files
that actually appear and the report keys to grep, and sections on the
unmerged export and on diffraction anisotropy. Its report section list
was one section behind: anisotropy is 9, warnings is 10.

The packaging change reached further than the changelog implied, so the
pages describing what a release ships were corrected too: the viewer
tarball no longer carries rugnux or the two developer utilities, the
Linux archives link cuFFT statically rather than shipping it beside the
binary under an $ORIGIN rpath, the aarch64 archive has a higher glibc
floor than the RHEL 8 ones and is untested on hardware, the archives
unpack with no top-level directory, and the notices live per package
under share/doc/jfjoch_<component>. The RHEL 9.5 vm_flags workaround in
FPGA_PCIE_DRIVER is obsolete and now says so.

2026-08-26 15:16:02 +02:00

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Release contents

This page describes what a Jungfraujoch release ships and what each artefact needs on the target machine — which CPU instruction set the binaries were compiled for, which CUDA toolkit they were built against, and which runtime libraries are bundled rather than expected from the host.

The artefacts in the table below are built and published by the continuous-integration pipeline (.gitea/workflows/build_and_test.yml) when a tag is pushed. For how to install and configure the result see Deployment; for the package-repository URLs see Linux package repositories.

Artefacts

Artefact	Distributed via	Contains
`.rpm` / `.deb` packages	package repositories	The full server stack: `jfjoch` (broker, frontend, FPGA and detector tools), `jfjoch-writer`, `jfjoch-viewer` (incl. the XDS plugin), `jfjoch-driver-dkms`, and `rugnux` (offline analysis, independent of the rest)
`jfjoch_viewer-<version>-linux-cuda<major>.tgz`, `...-linux-cpu.tgz`	Gitea release page	Portable Linux viewer: `jfjoch_viewer`, its desktop entry, icon and D-Bus service, and the license notices
`jfjoch-viewer-<version>-win64-cuda<major>.exe`, `...-win64-cpu.exe`	Gitea release page	Windows installer for `jfjoch_viewer`, plus the Qt runtime
`rugnux-<version>-linux-x86_64-cuda<major>.tgz`	Gitea release page	Portable Linux `rugnux`, the offline analysis CLI, and the license notices. One executable
`rugnux-<version>-linux-aarch64-cuda<major>.tgz`	Gitea release page	The same, cross-built for 64-bit Arm — NVIDIA GH200 and DGX Spark
`rugnux-<version>-win64-cuda<major>.zip`	Gitea release page	The same for Windows, plus the cuFFT DLL
`jfjoch-writer` `.rpm` / `.deb`	Gitea release page	The writer alone, for a file-writing machine without the rest of the stack
`libjfjoch_xds_plugin.so.<version>`	Gitea release page	XDS HDF5 read plugin (built on RHEL 8); see Integration with MX software
`jfjoch-client`	PyPI and the Gitea PyPI index	Generated Python OpenAPI client
Documentation	Read the Docs and the `gitea-pages` branch	This documentation set

The FPGA firmware (.mcs) images are attached to the release as well. The firmware is stable and is carried from version to version, and is rebuilt with Vivado (see FPGA smartNIC) when it needs to change — so a card keeps its image across a software upgrade unless the release notes say otherwise.

CPU instruction set

The architecture flags live in the CI configuration rather than in CMakeLists.txt, so a site building from source picks its own (x86-64-v4 on an AVX-512 cluster, -march=native, or the plain baseline the compiler defaults to). The released binaries are compiled to a fixed floor:

Release	Flags	Minimum CPU
Linux (all packages, and the portable `.tgz`)	`-march=x86-64-v3 -flto=auto`	AVX2 + FMA + BMI2 — Intel Haswell (2013) / AMD Zen (2017) and newer
Windows installer	`/arch:AVX`	AVX — Intel Sandy Bridge (2011) / AMD Bulldozer and newer

The Windows floor is lower because MSVC has no spelling for the x86-64-v2 level; /arch:AVX is the nearest one and implies SSE4.1/4.2, which is what actually matters — without it Eigen has no vectorised round and falls back to a libm call per element. Link-time optimisation is applied on Linux only.

A binary will fault with an illegal instruction on a CPU below its floor. If you must run on older hardware, build from source without the flags.

Operating-system floor

The .rpm / .deb packages are built per distribution (RHEL/Rocky 8 and 9, Ubuntu 22.04 and 24.04) and are tied to it. The portable viewer .tgz and the x86_64 rugnux .tgz are built on RHEL 8, the oldest supported distribution, so their glibc floor is low enough to run on any newer Linux — that is what they are for, and why they replace the per-distro packaging of those programs on the release page. The aarch64 rugnux .tgz is the exception: it is cross-built against Ubuntu 24.04, so it needs glibc 2.39 or newer (which DGX OS 7 and any current Arm server distribution have). The Windows installer is built and verified on Windows 11.

The portable archives have no top-level directory. They unpack straight into bin/ and share/, so always extract them into a directory of their own (tar xzf … -C /opt/rugnux-<version>) rather than into a working directory.

CUDA and non-CUDA builds

Every binary artefact is released in two variants, cuda<major> and cpu. The CUDA variant adds the GPU fast-feedback indexer (ffbidx), the GPU FFT indexer and GPU image processing; the CPU-only variant runs the same pipeline on the CPU with the FFTW indexer, at much lower throughput.

The CUDA toolkit used is the one on the corresponding build machine: CUDA 12 for the RHEL 8 packages, CUDA 13 for RHEL 9, Ubuntu and Windows. The major version is part of the artefact and repository name, so a download is self-identifying. Building from source needs CUDA 12.8 or newer.

A CUDA build does not require a CUDA machine. Jungfraujoch asks how many CUDA devices are present at start-up and treats "none" (including "no driver installed") as zero GPUs, falling back to the CPU path. So a CUDA build starts and runs correctly on a machine with no NVIDIA GPU at all. What each artefact has to find at run time differs:

Portable Linux .tgz — nothing. The CUDA runtime, the fast-feedback indexer and cuFFT are all linked statically, so each archive is a single executable that depends on nothing but the C and C++ runtimes. On a GPU machine the NVIDIA driver is the only NVIDIA component needed.
Windows installer and .zip — the CUDA toolkit ships no static cuFFT for Windows, so the cuFFT DLL is part of the distribution, next to the executable. No CUDA toolkit is needed.
.rpm / .deb — these deliberately keep cuFFT dynamic, so that one dependency is managed centrally with the rest of CUDA. Install the cuFFT package alongside, or use the nocuda repositories on a machine where CUDA is not wanted.

Static CUDA linkage makes the Linux CUDA artefacts substantially bigger than the CPU ones, and the Windows cuFFT DLL is ~256 MB — which is the other reason for shipping both variants.

On a machine with an NVIDIA GPU, take the CUDA variant: only that one uses the GPU.

GPU generations and the NVIDIA driver

A CUDA variant carries compiled device code for a fixed set of GPU generations, and which generations those are follows from the CUDA toolkit it was built with. The CUDA runtime is linked statically, so the only NVIDIA component the target machine has to supply is the driver — there is no CUDA-toolkit version requirement on the host.

Artefact	CUDA toolkit	GPU generations	Minimum driver
RHEL 8 packages, portable viewer `.tgz`, x86_64 `rugnux` `.tgz`	12.9	Volta (V100) through Blackwell: `sm_70`, `75`, `80`, `86`, `89`, `90`, `100`, `120`, `121`	525.60.13
RHEL 9 and Ubuntu packages, Windows installer, Windows `rugnux` `.zip`	13.x	Turing (T4) through Blackwell: the same list without `sm_70`	580.65.06 (Linux), R580 (Windows)
aarch64 `rugnux` `.tgz`	13.x	`sm_90` (GH200) and `sm_121` (DGX Spark) only	580.65.06
any `cpu` / `nocuda` variant	—	—	none

The aarch64 build is cross-compiled and verified in CI to be Arm, self-contained and to carry both GPU targets, but it is not exercised on hardware — CI has no GH200 or Spark runner.

A V100 needs the CUDA 12 build. CUDA 13 dropped offline compilation for Volta, and the PTX a fatbin also carries only ever JIT-compiles forwards, so a CUDA 13 artefact contains nothing a V100 can execute: every kernel launch fails with no kernel image is available for execution on the device. On a V100 host take the RHEL 8 packages or the portable Linux .tgz. Nothing older than Volta is supported.

Newer GPUs never need a newer build — the highest generation in the list ships PTX as well as SASS, which the driver JIT-compiles for a GPU that came out after the release.

The minimum driver above is the floor for the whole CUDA major version, which is what applies here because the CUDA runtime is statically linked (CUDA minor version compatibility). Newer drivers are always fine; they are backward compatible. A driver from the same release as the build toolkit (575.57.08 for the CUDA 12.9 build, 610.43.02 for a CUDA 13.3 one) additionally rules out the single caveat of minor version compatibility — a call into a driver API newer than the installed driver, which fails with cudaErrorCallRequiresNewerDriver.

Windows installer

The Windows artefacts are the jfjoch_viewer installer and the separate rugnux .zip; the rest of Jungfraujoch (broker, receiver, FPGA host, detector control) is Linux-only.

The toolchain bounds of the released installer are:

Visual Studio 2026 with the C++ (MSVC) toolset. MSVC is not optional — CUDA on Windows builds through it — and it is what the release is compiled with.
CUDA Toolkit 13.3 for the cuda13 variant.
Qt 6.11 for MSVC (msvc2022_64), including Qt Charts.
Ninja as the generator; zlib and Eigen 3.4 supplied from a build prefix.

The installer is generated with NSIS and bundles the Qt runtime (via windeployqt) and, on the CUDA variant, the cuFFT DLL — so the end user installs neither Qt nor a CUDA toolkit. The two variants share an install directory and Start Menu group and replace each other (CUDA is a strict superset); they are told apart by the installer filename and the Add/Remove Programs entry:

Build	Installer file	Add/Remove Programs
CUDA (default)	`jfjoch-viewer-<version>-win64-cuda<major>.exe`	`Jungfraujoch (CUDA)`
CPU-only	`jfjoch-viewer-<version>-win64-cpu.exe`	`Jungfraujoch (CPU)`

To build the viewer yourself on Windows, see jfjoch_viewer ▸ Building from source on Windows.

Licenses

Every package variant carries the project license, the third-party manifest and the verbatim license texts of the bundled dependencies, each package under a directory of its own — share/doc/jfjoch_broker, jfjoch_writer, jfjoch_viewer, jfjoch_rugnux, jfjoch_driver_dkms — so that no two packages claim the same path and they can be upgraded independently. See Third-party software notices.

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