leonarski_f
30b6800289
* Rugnux: basic support for CCD images (marCCD, SMV) and for gzipped miniCBF. * `jfjoch_viewer`: opens the CCD formats, and fixes to the dataset plots. * Documentation updates. Reviewed-on: #81 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
8.2 KiB
Installing Rugnux
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rugnux is a single self-contained executable. It needs no CUDA toolkit, no Qt, and no
Jungfraujoch service running anywhere; on a machine with an NVIDIA GPU it needs the NVIDIA
driver, and without one it still runs on the CPU.
From the package repositories (RHEL / Rocky / Ubuntu)
On a distribution covered by the package repositories, rugnux is a package of
its own:
sudo dnf install rugnux # RHEL / Rocky 8 and 9
sudo apt install rugnux # Ubuntu 22.04 / 24.04
It installs /usr/bin/rugnux and depends on nothing from the acquisition side — no broker, no
detector libraries, no Qt — so it can go on a machine that only processes data.
Upgrading from rc.163 or earlier.
/usr/bin/rugnuxused to belong to thejfjoch-viewerpackage. Therugnuxpackage declares that the file has moved, so installing it upgrades an oldjfjoch-viewerin the same transaction instead of failing on the duplicate path. If yourjfjoch-vieweris pinned to an old version, unpin it or remove it first.
From the release archive
For a machine no package manager covers — or for Windows and Arm, which have no repository — take the archive for your architecture from the Gitea release page:
| Archive | For |
|---|---|
rugnux-<version>-linux-x86_64-cuda12.tgz |
64-bit Intel/AMD Linux. Built on RHEL 8, so it runs on any newer Linux |
rugnux-<version>-linux-aarch64-cuda13.tgz |
64-bit Arm Linux — NVIDIA GH200 and DGX Spark. Built on Ubuntu 24.04, so it needs glibc 2.39 or newer. Cross-compiled and not yet exercised on Arm hardware |
rugnux-<version>-win64-cuda13.zip |
64-bit Windows |
The archive has no top-level directory — it unpacks straight into bin/ and share/. Always
give tar a destination of its own, or it will scatter those into whatever directory you are in:
mkdir -p /opt/rugnux-1.0.0
tar xzf rugnux-1.0.0-linux-x86_64-cuda12.tgz -C /opt/rugnux-1.0.0
/opt/rugnux-1.0.0/bin/rugnux # prints the usage
What you get is:
bin/rugnux the program
share/doc/jfjoch_rugnux/LICENSE GPLv3
share/doc/jfjoch_rugnux/THIRD_PARTY_NOTICES.md
share/doc/jfjoch_rugnux/licenses/ verbatim licence texts of the bundled dependencies
Nothing is written outside that directory, nothing needs root, and several versions can sit side by
side. To remove it, delete the directory. Put bin/ on your PATH if you want to type rugnux
rather than the full path.
Mixing the two. If a
rugnuxpackage is also installed,/usr/bin/rugnuxwill normally win onPATH. Put the archive'sbin/first, or call it by its full path, to be sure which one you are running —rugnuxprints its version on every run.
GPU support
The released archives are CUDA builds. They need only an NVIDIA driver on the host — 525.60.13
or newer for the CUDA 12 archive, 580.65.06 or newer for the CUDA 13 ones — and no CUDA toolkit,
because everything CUDA is linked statically. With no GPU or no driver, rugnux reports zero CUDA
devices and falls back to the CPU path, which works but is far slower and offers only the fftw
indexer. A V100 needs the CUDA 12 archive; which generations each build covers is in
Release contents ▸ GPU generations and the NVIDIA driver.
Building from source
rugnux alone, without the server stack or Qt:
cmake -S . -B build -DJFJOCH_RUGNUX_ONLY=ON -DCMAKE_BUILD_TYPE=Release \
-DCMAKE_CXX_FLAGS="-march=x86-64-v3" -DCMAKE_C_FLAGS="-march=x86-64-v3"
cmake --build build -j$(nproc) --target rugnux
The binary lands in build/rugnux/rugnux. No library has to come from the system: every dependency
is downloaded and built during the first configure, which therefore needs network access. cmake --build build --target package produces the same .tgz the release ships. The -march flag is not set by the
build system on purpose, so a plain build is slower than the released one on the CPU-bound stages —
see the note in CMakeLists.txt.
Hardware
As with the rest of Jungfraujoch, serious performance requires an NVIDIA GPU. The CUDA build
provides the GPU fast-feedback indexer (ffbidx) and the GPU FFT indexer (fft); without CUDA
only the CPU fftw indexer is available. With a GPU present most of the per-image pipeline runs on
the device — bitshuffle+LZ4 decompression, image preprocessing, azimuthal integration, spot finding,
prediction and Bragg integration — as does rotation scaling and merging, with CPU implementations as
the fallback where there is no GPU. The thread count (-N) governs the CPU side of all of it.
The released CUDA builds need only an NVIDIA driver on the host, no CUDA toolkit: 525.60.13 or
newer for the CUDA 12 artefacts (RHEL 8 packages, the x86_64 rugnux archive) and 580.65.06 or
newer for the CUDA 13 ones (RHEL 9, Ubuntu, the aarch64 and Windows rugnux archives). Which GPU
generations each artefact supports — a V100 in particular works only with the CUDA 12 build — is in
Release contents ▸ GPU generations and the NVIDIA driver.
Memory
A run's memory is set by how many reflection observations it integrates, not by how many pixels
the detector has. Measured on rc.169 at -N 6, over rotation sweeps of 1200-1800 frames:
| Detector | Observations integrated | Host (peak RSS) | Device |
|---|---|---|---|
| 1679 x 1475 (2.5 MP) | 25 M | 7.8 GB | 3.8 GB |
| 2527 x 2463 (6.2 MP) | 13 M | 4.9 GB | 3.1 GB |
| 3262 x 3108 (10.1 MP) | 7 M | 2.5 GB | 3.0 GB |
| 4371 x 4150 (18.1 MP) | 13 M | 4.3 GB | 3.2 GB |
| 4371 x 4150 (18.1 MP) | 20 M | 6.0 GB | 4.4 GB |
| 3262 x 3108 (10.1 MP) | 50 M | 14.2 GB | 7.0 GB |
Two sweeps on the same detector differ by a factor of five, so size the machine on the observation
count rather than on the detector. As a rule, 0.6 GB + 0.3 GB of host memory per million
observations, and 2.4 GB + 0.1 GB of device memory per million. The run reports what it actually
integrated — RotationScaleMerge: ingested ... partial observations. 32 GB of host RAM and an
8 GB card cover every dataset measured here at -N 6; an ordinary sweep needs 16 GB and 6 GB.
Both peaks fall in scaling and merging, not in the per-image loop: the loop alone stays under
3.2 GB on the device at -N 6, whatever the detector. The stage that needs the most is the 3D combine.
-N multiplies the per-image loop only — about 8 bytes of device memory per detector pixel per
worker (150 MB per worker at 18 MP, 87 MB at 6 MP) on top of a fixed ~2.6 GB, and it is linear
with no ceiling: the same 18 MP sweep takes 2.7 GB of device memory at -N 1, 3.2 GB at -N 6 and
8.0 GB at -N 32. Host memory does not move with -N, and neither does the merge's own device
memory. Where several runs share one card, -N is the knob that decides how many fit.
To use less:
-N— the only flag that lowers the per-image loop's device memory. Lowering it is what rescues a run on a card that is otherwise full.--no-merge— on the heaviest sweep measured it took the host peak from 14.2 to 8.3 GB and the device peak from 7.0 to 2.9 GB. It also gives up the merged output.--no-export-unmergeddoes not save memory.<prefix>_unmerged.mtzis the largest file a run writes, but its observations are resident either way; the flag saves disk, not RAM.
Nothing measures the free memory on the card before allocating. A run that does not fit degrades
where it can — device decompression falls back to the host, the beam-stop projection falls back to
the host, an indexer thread that cannot allocate reports the frame as not indexed — and fails the
run otherwise, with Processing failed: CUDA (GPU) error (Failed to allocate device memory) and a
non-zero exit status. That is deliberate: a CUDA out-of-memory says nothing about the frame in
flight and everything about the machine, so skipping frames would leave a run that looks complete
with quietly different merged numbers. A run is never silently short; it either completes or fails.
On a GPU shared with another job, lower -N or wait for the card.