# 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](DEPLOYMENT.md); for the package-repository URLs see [Linux package repositories](REPOSITORIES.md). ## Artefacts | Artefact | Distributed via | Contains | | --- | --- | --- | | `.rpm` / `.deb` packages | [package repositories](REPOSITORIES.md) | The full server stack: `jfjoch` (broker, frontend, FPGA and detector tools), `jfjoch-writer`, `jfjoch-viewer` (incl. the XDS plugin), `jfjoch-driver-dkms` | | `jfjoch_viewer--linux-cuda.tgz`, `...-linux-cpu.tgz` | Gitea release page | Portable Linux viewer package: `jfjoch_viewer`, `rugnux`, `jfjoch_extract_hkl`, `jfjoch_recompress` and the license notices | | `jfjoch-viewer--win64-cuda.exe`, `...-win64-cpu.exe` | Gitea release page | Windows installer with the same four programs, plus the Qt runtime | | `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.` | Gitea release page | XDS HDF5 read plugin (built on RHEL 8); see [Integration with MX software](SOFTWARE_INTEGRATION.md) | | `jfjoch-client` | [PyPI](https://pypi.org/project/jfjoch-client/) and the Gitea PyPI index | Generated Python OpenAPI client | | Documentation | [Read the Docs](https://jungfraujoch.readthedocs.io) 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](FPGA.md)) 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` is built on RHEL 8, the oldest supported distribution, so its glibc floor is low enough to run on any newer Linux — that is what it is for, and why it replaces the per-distro packaging of the viewer on the release page. The Windows installer is built and verified on Windows 11. ## CUDA and non-CUDA builds Every binary artefact is released in **two variants**, `cuda` 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.** Of the CUDA components only **cuFFT** is linked dynamically — the CUDA runtime and the fast-feedback indexer are linked statically — and cuFFT itself has no link-time dependency on the NVIDIA driver library. 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, provided the cuFFT runtime can be loaded: - **Portable `.tgz` and Windows installer** — cuFFT is **part of the distribution**, shipped next to the executable (on Linux found through an `$ORIGIN` rpath). Nothing else is needed: no CUDA toolkit, and on a GPU machine only the NVIDIA driver. - **`.rpm` / `.deb`** — cuFFT comes from the distribution's own CUDA packages, 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. The cuFFT runtime is large (the Windows DLL is ~256 MB), so the CUDA artefacts are correspondingly bigger than the CPU ones — the other reason for shipping both. On a machine with an NVIDIA GPU, take the CUDA variant: only that one uses the GPU. ## Windows installer The Windows artefact covers `jfjoch_viewer` and the portable analysis CLIs only; 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--win64-cuda.exe` | `Jungfraujoch (CUDA)` | | CPU-only | `jfjoch-viewer--win64-cpu.exe` | `Jungfraujoch (CPU)` | To build the viewer yourself on Windows, see [jfjoch_viewer ▸ Building from source on Windows](JFJOCH_VIEWER.md#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 under `share/doc/jfjoch`. See [Third-party software notices](THIRD_PARTY_NOTICES.md).