leonarski_fandFilip Leonarski
19b135186d
* Rugnux: Processes a collection live while jfjoch_broker writes it (`rugnux <master.h5> <broker URL>`). * Rugnux: Processing starts on the first images and confirms its choices once all are in, so analysis can begin during collection. * Rugnux: Faster processing, especially on servers with several GPUs and CPU sockets and on computers without a GPU (`--no-gpu`, macOS). * Rugnux: Uses much less GPU memory, so large data sets stay on the GPU of smaller cards, and a data set too large for the card is processed on the CPU instead. * Rugnux: On macOS, Bragg integration runs on the Apple GPU. * Rugnux: Better handling of twinning and pseudo-symmetry, with the twin hypothesis written as `<prefix>_twin_<SG>.mtz`, and a wrong `--model` no longer confirms the data or decides the hand. * Rugnux: `--model` accepts predicted (AlphaFold) models and falls back to molecular replacement when the model is not placed as a rigid body (experimental `--mr` forces it), settling space group, hand and twinning and writing a different answer as `<prefix>_mr.mtz`. * Rugnux: Molecular replacement is faster and settles the space group in more cases with several copies in the asymmetric unit. * Rugnux: Deposition-ready mmCIF, a richer report, and numbered steps on the terminal with the full log in `<prefix>_log.txt`; `_P1.mtz`, `--estimate-beam-center` and `--beam-center-search` are removed. * Rugnux: The beam-stop shadow is found correctly on tilted detectors. * Rugnux: Stills files that record a stationary goniometer axis get the default stills geometry refinement. * jfjoch_viewer: "Analyze dataset" uses the same defaults as a bare `rugnux <file>`, and shows the results of processed CBF, marCCD and SMV sweeps. * jfjoch_viewer: The Files panel refreshes on F5 and every few seconds, and the plot type and other selections stay put during live collection. * jfjoch_writer: Memory stays bounded and old data files are closed efficiently on long runs, any failure ends the run in an error state the broker is told about, and the master file appears only after its data files; the ZeroMQ pusher logs when a stalled writer makes it drop images. * Fixes: The TCP image pusher no longer blocks the broker's receiver, GPU out-of-memory failures stop with a clear message, and the XDS plugin masks module and chip edges. --------- Co-authored-by: Filip Leonarski <jungfrau@filips-mac-mini.home> Reviewed-on: #85 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
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Installing Rugnux
:local:
:depth: 2
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, macOS 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 |
rugnux-<version>-macos-arm64-cpu.tgz |
macOS 13 (Ventura) or newer on Apple Silicon (M1 and newer). No CUDA on macOS: CPU, with Bragg integration on the Apple GPU (Metal). Intel Macs are not supported |
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.
macOS: the binary is signed with the project's Apple Developer ID and notarized, so a
.tgzdownloaded with a browser runs without further steps; the first run of a freshly downloaded copy checks the notarization online. Unpacking into a directory under your home directory (~/rugnux-<version>) rather than/optavoids needingsudoon a Mac.
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 Linux and Windows archives are CUDA builds (the macOS one is CPU-only). 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 — but it is set up to give the same results: the build turns
floating-point contraction off, so the x86 level a binary targets changes its speed and not its
answer.
On a Mac (Apple Silicon), leave the two -march flags out — they name an x86 level; the Apple
compiler's default target is already the M1 — and use sysctl -n hw.ncpu for the job count. The
build has no CUDA there and needs nothing installed beyond Xcode and CMake; its Bragg integration runs
on the Apple GPU through Metal, with the same result as the CPU engine to the bit
(GPU data analysis ▸ Apple GPUs).
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 (cufft); without CUDA
only the CPU fftw indexer is available, and -X fft runs on it. With a GPU present most of the per-image pipeline runs on
the device — bitshuffle+LZ4 decompression, miniCBF byte-offset decoding and marCCD widening, image
preprocessing, azimuthal integration, spot finding, prediction and Bragg integration — as do the
pre-scan (beam-stop mask, background beam-centre fit, spot widths, defective pixels; one accumulator
per card), rotation scaling and merging from the ingest of the reflections onwards (sorting and the
resolution cut included) with its correction surfaces, and --model: the structure factors, scale
fits and rigid-body placement of the validation and the rotation and translation functions of
molecular replacement. The CPU implementations are the path where there is no GPU, and the two agree
to rounding rather than bit for bit (parts of the scaling and the model fits round differently
on the card), so a GPU and a CPU run can differ in the last digits of the merged data. The choice is automatic;
a CUDA build runs on the CPU when told to (rugnux --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.
Rotation scaling holds less on the card since rc.175 than when these were measured, so the device
column is an upper bound.
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. The steps that can be too large for
a card decide between GPU and CPU before they touch it, from the card's total memory, so the same
input on the same card always takes the same path: rotation scaling and merging runs on the CPU where
it would take more than 80% of the card (and logs ... scaling on the CPU instead), --model
validation and molecular replacement place each of their parts on the GPU or the CPU against a budget
of 80% of the card, the beam-centre FFT runs on the CPU where it would take more than half of it, and
on a small card the per-image workers are fewer. Past those decisions 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.
Very large unit cells
The rule above holds for the cells in that table, and a very large cell sits far beyond it. A long axis, a dense pattern and many frames multiply together: each frame predicts more reflections, each reflection is split over more frames, and tens of millions of partial observations are normal for a big cell. One sweep of a crystal with an axis of about 640 Å predicted 236 million reflections and peaked at 17.5 GB of host memory on a GPU build and 72.5 GB on a CPU-only build. A CPU-only build keeps in host RAM what a GPU build keeps on the card, so it is the one that needs the large machine. When such a set is too large for GPU scaling on the card, the run scales and merges it on the CPU instead, with a warning in the log — and then wants that much host memory for that step.
When host memory runs out
Nothing estimates the host memory a run will need before it starts, so a set that does not fit runs until the memory is gone, and then ends in one of two ways.
Where the allocation is refused — under an address-space limit (ulimit -v, which some batch
systems set), with strict overcommit, or for one request larger than the machine could ever grant —
the run stops with
Processing failed: out of host memory (std::bad_alloc) - this data set needs more RAM than is available
and a non-zero exit status. The CUDA driver reserves a large address space of its own, so a GPU build
under a tight ulimit -v can stop before reading anything, with CUDA (GPU) error (out of memory).
More often on Linux the allocation succeeds and the memory runs out as it is used. The kernel's
OOM killer then ends the process with SIGKILL, which no program can catch: the run stops with no
message of its own, the shell prints Killed (exit status 137), and dmesg or the batch system's
log has an Out of memory: Killed process line. A job capped by a cgroup memory limit ends the same
way. A run that disappears like this needs more RAM than the machine or the job was given, exactly as
if it had said so. --no-merge lowers the host peak, as above; otherwise the run needs a larger
machine.