Each crystal's (psi_x, psi_y) tilt was refined by a Ceres problem rebuilt for every inner
iteration: one auto-differentiated residual block and one Cauchy loss per reflection. With the
scaling reference cleaned up it was 70% of the stills scale/merge. The same objective - Cauchy
on the sigma-weighted intensity residuals plus the Gaussian prior on the tilt, the tilt bounded -
is now minimised by a small Levenberg-Marquardt on the rho'-reweighted 2x2 normal equations,
the tilt applied to all reflections through one rotation matrix and the Jacobian taken by
central differences.
It reaches the same answer to optimizer precision: on the new synthetic test
(StillsPartialityRefine_RecoversTheTilt) partiality error 0.0559 -> 0.03019 with Ceres and
0.03016 with this, mean tilt 0.02065 vs 0.02067 deg; on two 25k-frame XFEL runs (--mode scale)
CC1/2, I/sigma, R_pim and the CC against a deposited lysozyme model agree to 0.0005, mean
|dpsi| 0.023 deg on both. Scale+merge 6.9 -> 3.0 s (--mode scale, loaded box).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
In rugnux people choose the algorithm, not where it runs. -X fft (and the
old -F) now resolves at parse time to the GPU FFT indexer where a GPU is
visible and --no-gpu is not given, and to FFTW otherwise. -X cufft is the
GPU implementation alone, as -X fft was. -X cufft and -X ffbidx without a
GPU now stop at once with a clear message instead of failing later in the
indexer pool. Auto is unchanged and not redundant: on stills with a known
cell and a GPU it takes FFBIDX.
Only rugnux's CLI spelling changes. The internal IndexingAlgorithmEnum, the
broker and its OpenAPI enum keep FFT = the GPU implementation; the CLI
maps onto it, and the command line the viewer writes for a run (RugnuxCommandLine)
gives the enum's FFT back as -X cufft, which re-parses to the same thing.
A run without -X, and -X fft on a machine with a GPU, process exactly as before.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The stills scale/merge after the image loop was almost entirely serial:
- every scaling-reference merge (MergeAll: the per-image scaling reference and the two
partiality post-refinement references) ran French-Wilson single-threaded on its export,
about 3 s each, and assigned free-R flags - neither is read by a reference, which uses I
only. MergeAll now exports intensities alone (MergeOnTheFly::ExportIntensities).
- the merge accumulated into a std::map; it is now an unordered_map exported in key order,
which is the std::map order.
- AddImage, the error-model collection and the R_meas pass of MergeStats did the
per-observation work (systematic-absence test, ASU key, filters, error-model sigma,
outlier test, merged-I lookup) in one serial loop. They now reduce a block of images in
parallel and consume it in image order (ReduceImagesInOrder), so every floating-point
sum, half-set choice and hash-map insertion is made in the serial order.
Measured on a 25k-frame XFEL stills run (box loaded, load 9-14): merge P1 19.0 -> 7.1 s,
final-group merge 27.7 -> 13.2 s; on a second run 23.4 -> 7.7 s and 29.7 -> 13.6 s.
mtz/unmerged mtz data, hkl, cif and report identical on both runs, mx and --mode scale.
New test MergeOnTheFly_AddImagesIsTheSerialMerge checks the parallel merge against the
serial loop across a block boundary.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The measured-centre arm is a whole geometry pre-pass. It now integrates first a third of the pre-pass
frames, in 3k + 1 blocks of about 10 deg over the whole pre-pass arc with every third kept, and the
file's centre merges the same frames (its outcomes for the other frames are moved out while that
engine ingests). Where the measured centre loses on that subset on every line MeasuredCentreWins draws
by more than NEAR_TIE_SIGMA times the line's noise (Poisson for the strong counts, the Fisher standard
error for a CC1/2), it has lost and no full arm is run. Anything else, a near tie or a win, puts the
run back as the file's centre left it and runs the arm over all the pre-pass frames, deciding as before.
The run made again (decide_all_late_) runs the arm on all frames.
The same subset was tried for the metric-symmetry demoted arm and the walk refits and is not used
there: the held-out residual's standard error does not grow on a third of the frames, so a subset
margin overstates the evidence (one set's metric call flipped at 2.07 sigma), and a subset refit moved
one walk to a worse fixed point.
Against rc175 edde4439c, ci + 49 extra + 9 private sets: p.hkl and MTZ data identical on all 86. The
arm lost on the subset on 22 sets (no full arm), was a tie/win on 9 (full arm as before, +0.3-1.7 s).
Alternated runs: one set 62.1 -> 55.2 s, another 46.2 -> 42.4 s.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
Each refit reads a share of the error left, so the walk's steps contract by about the ratio of the
last two. Where the step that ratio predicts moves the end of the sweep by less than half a frame,
the refit (an integration of the pre-pass frames and a post-refinement) and the indexing probe after
it are not made; the scale the validation frames last preferred is adopted, as before.
Measured on the walk sets of the battery against rc175 edde4439c: p.hkl and MTZ data identical; it
fired on one open set (2 refits instead of 3, same adopted scale), about -7 s there.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
The intermittent "out of GPU memory" on the 3600-frame cbf.gz set (about one run in three on a 16 GB
card, base included) was caught with allocation backtraces: FitErrorModelGPU's 42.8 MB in
MergeAndStats failed while the speculative geometry probe (GPUWorkBeside) held its FFT indexers on
the same card. The engine's own arrays (RotationScaleMergeGPU Impl::Alloc) wait for such work to end
and ask again; the merge's helpers - ErrorModelGPU, WilsonOutliersGPU, PairSortGPU and the cub
scratch of the engine itself - allocated directly and failed at once.
CudaAlloc::SynchronousAfterWorkBeside: Synchronous, and where the card is full after the pool is
trimmed, it waits for the GPU work beside the main line (wait_for_gpu_work_beside, 10 min as for
the engine) and asks once more. The four merge files use it. Only the probe passes register
GPUWorkBeside and they index only, so none of them merges and nothing waits for itself. When a
buffer is allocated is all that changes; what is computed is not.
Catch2 CudaDevicePtr_AfterWorkBesideWaitsForItsMemory: with the card filled by work beside, a
Synchronous buffer fails and a SynchronousAfterWorkBeside one gets its memory once the work ends.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
A new last column, "disposition", gives each frame's merge disposition as
a number to plot directly: 1 merged, 0.5 downgraded, 0 rejected; nan for
a frame that never got a scale (never reached the merge) and on a run
that merged nothing, as the file writes every value nothing measured.
Existing columns keep their indices.
0.5 is a marker, not a weight. "Downgraded" is a label applied after the
merge (RotationScaleMerge::CountSweepDisposition marks the merged frames
inside a flagged sweep-quality range); the merge applies no factor of its
own to them - they are carried at whatever weight their own scale and
sigmas give - so there is no fixed or per-frame reduction factor that
could honestly be written instead.
Writer-only change: on three open-arm sets with downgraded and rejected
frames p.hkl is identical to the same commit without it, the first ten
columns are unchanged, and the column's 1/0.5/0 counts equal the report's
merged/downgraded/rejected counts.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
A screw candidate whose absent class no reflection contradicts, refused only because its
evidence could not reach min_screw_absence_evidence even with every predicted-absent axial
reflection at the floor (one such reflection reaches 6.9 nats; a row without a control class
reaches nothing), left the symmorphic group adopted and the report saying the group follows from
the absences. Such a row is now SPACE_GROUP_SCREW_UNDETERMINED, with the screw group in
SPACE_GROUP_ALTERNATIVES. Report only: the result is kept apart (unreached_screws) and folded in
after every decision, so the written group and files do not change.
Measured on a monoclinic set whose merge holds five 0k0 reflections at noise level, one of them
predicted absent: P2 was reported as decided by the absences against a deposited P21; it is now
P2 with the b screw open and P21 offered. Over the full battery's candidate tables the condition
holds on that set and one private set (the same P2/P21 case) only. ci tier: no change.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
determine_cutoff_radius() starts at the IT92 approximation of the radius. When the density there is
exactly the float cutoff (1e-5f) - which happens for a few B per element, e.g. a nitrogen at
B + blur = 277.64563 - neither loop runs, x1 == x2, and the interpolation returns 0/0 = NaN. Upstream
gemmi master has the same code.
A NaN radius cast to int gave MakeAtomBox() INT_MIN half-widths, so UseAtomBoxesByPlane() sized its
point buffer from a wrapped product (one point) and then wrote past it: the heap overflow (SIGSEGV, or
a crash later in FFTW) that killed molecular replacement after the rigid body of a large deposited
model, turned to imitate another crystal form, had failed (2 of 73476 atoms, in the CPU rigid body of
an MR placement). The GPU density path took the same NaN radius into its box sizes.
The outward search now steps at least once, so that case returns the point at the cutoff. Every other
B gives the same radius bit for bit (checked over ten elements, every 8th float B in 2-600 A^2: 3 NaN
before, none after, no finite radius changed).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
Every stage between the socket and the HDF5 write was bounded by a message count and never by
bytes: ZeroMQ receive watermark 100, cbor_fifo 200, outside_fifo 1000, repub_fifo 200 - up to
some 1500 images in flight per writer process, each a whole compressed image, so 6-12 GB for a
JUNGFRAU 4M and 30-60 GB for a 9M, and the writer's memory was a quantity nobody had chosen.
ReceiveRingBuffer is a fixed ring of bytes owned by the ImagePuller base class, so the ZeroMQ
and TCP pullers share it. A message is received straight into it (TCP reads the payload into
the ring; ZeroMQ copies the message in) and travels as a shared_ptr region in ImagePullerOutput;
the deserialised image points into that region, republish sends it as is, and the bytes come
back when the last holder lets go. Space is reclaimed in arrival order: a region released out
of turn - republish finishes with messages in its own order - is marked and waits for the ones
in front of it. Because of that, what republish may hold is capped at a quarter of the ring,
and an image over the cap is not republished, exactly as one that finds the republish queue
full; otherwise a stalled republish consumer would pin the ring and slow the writer to its
rate. A message larger than the whole ring (a START or END of a very long run) goes on the heap,
so it cannot wedge the receiver.
A full ring stalls the receive thread, and what that does depends on the stream. On TCP the
pusher waits behind the writer's BUSY heartbeats and gives up only after a minute in which no
image at all was written. On ZeroMQ the pusher's send blocks for one second, then drops the
image and sends without waiting for the rest of the run - so on ZeroMQ a ring that is smaller
than the writer's stalls costs images, where the old 1500-message queues absorbed them. That
is the trade: the budget is now a number, and it has to be chosen. jfjoch_writer sets it with
-m/--rcv_ring_mb, default 2048 MB per writer process; the ZeroMQ receive watermark default goes
from 100 to 10 messages, since it now only counts what sits beyond the ring. The ring's peak
use is in the end-of-run log line, and docs/JFJOCH_WRITER.md has a Memory section with the
sizing rule.
The puller's own default is no ring at all, which keeps every other user as it was: the
broker's DECTRIS stream2 puller (JFJochServices, JFJochReceiverService) stays zero-copy with
the ZeroMQ message as the buffer, and the broker's direct HDF5 writer (HDF5FilePusher ->
FileWriter) never goes through a puller. The puller threads now also catch std::exception, as
the writer does since the previous commit: deserialising the END message of a million-image
run allocates a vector per per-image array, and a bad_alloc leaving the thread ended the process.
Tests: ReceiveRingBufferTest (reuse, wrap-around, full ring, out-of-order release, heap
fallback, zero size, stop, a region outliving its ring); ZMQImageCommTest_SmallRingBuffer (64
one-megabyte images through a three-image ring, nothing lost, peak within the bound); one of
the two writers in TCPImageCommTest_2Writers_WithAck runs on a three-image ring.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
ProcessDataImage, ProcessEndMessage and NotifyReceiverOnFinalizedWrite caught only
JFJochException, while ProcessStartMessage and FinalizeDataCollection already caught
std::exception. Anything else - std::bad_alloc from a per-image array, filesystem_error from
a path check, the JSON library refusing a string that is not UTF-8 while publishing a
file-done notification - left CollectImages, and Run() logged "Exception not properly handled"
and returned with the state never reaching Finalized: no END acknowledgement, no writer
notification, the master's .tmp left behind until the next START. On the TCP image stream the
broker then waited for the END acknowledgement indefinitely, because the writer's heartbeat
thread keeps reporting it alive whatever state it is in.
Now the same catch as at the other sites: error state, fatal ACK, END processed, Finalized.
The new StreamWriter test provokes the JSON case through a sample name that is not UTF-8 with
the file-done socket enabled, and checks the run ends Finalized with no master written.
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
None of the eight GPU test cases in the file (BraggPredictionGPU*, the CPU/GPU consistency
cases and BraggPredictionRotGPU_DeviceOrderMatchesHost) checked for a device, so a CUDA build on
a machine without one failed them instead of skipping. Same guard as the other GPU tests.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
ModelValidation_ReindexesTheDataIntoTheModelsIndexing still expected no null for data already in
the model's indexing, as when the null ran only where a decision was pending. Since the unified
model validation it runs on every validation whose schedule asks for it (fit_test, the default),
and is skipped only where the verdict is another validation's; the test now checks that the null
ran and that the model fits.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
A UINT16_WIDENED frame (marCCD) was page-locked where it lay with PinInputRegion, which keeps the
registration until the engine moves on to another frame. The pre-scan hands its kept frames to the
spot measurement and to the defective-pixel search at once, each with its own engines, so one
engine could unregister a frame while another engine's copy from it was still in flight. That
takes the context down ("unspecified launch failure"), seen as a rare crash at the end of the
pre-scan on a 3072x3072 marCCD sweep (1 run in 30 reproduced it).
The stored bytes now go through the engine's own page-locked staging buffer, as the CBF and BSLZ4
decoders already do. The pixels decoded are unchanged.
Test: ImagePreprocessorGPU_SharedStoredFramesManyEngines - four engines upload four shared marCCD
frames concurrently; it aborted with the launch failure before the change and passes after it.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
The rest of the resident ingest moves to the card: the delta_phi re-solve against each
frame's smoothed lattice, the one exact-Bragg angle per rocking event, the partiality
recomputed from the smoothed mosaicity (with corr brought to it) and the histogram of the
frames an event spans. The host keeps the per-frame work (the smoothed lattices, the
mosaicity window) and reads back only corr; nothing per observation crosses back, and the
host arrays the smoothing worked on are no longer built on this path.
The integer work is the host's to the bit (the event-span histogram). The floating-point
work differs in the last bits: every rounding on the card is explicit (no contraction, so
the same bits on any GPU), the host's compiler fuses some of the same products into sums,
and erf/atan2 come from the CUDA library. This is the one place in the lever where results
change; see the hand-back report for the sets and metrics it moves.
Measured on 7qij (main engine, 78 M reflections): the whole ingest 6.5 s host + 1.3 s device
before this lever -> 2.9 s, of which 2.4 s is the upload (memory-bound, beside the
post-refinement gather) and 0.25 s this smoothing. Same space group, error model,
resolution and reflection count; IMEAN differs at the 1e-7 level except on near-zero
intensities.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
With the GPU resident, the host ingest swept the source reflections four times (background
and h histogram, sort keys, the in-range build, the device staging), bucket-sorted the keys
and cut the raw-hkl runs: 5-6 s of host time per engine on a 78 M-reflection sweep, run
twice beside the quality guard. Now the frames' reflections are copied once, through two
pinned buffers, to the card, where the per-frame background is summed, the reflections
are ordered by two stable radix passes ((image, index), then raw hkl - the one order the
host's total comparator gives), cut into runs by run-length encoding, filtered by the
resolution range and compacted. The observations, the runs and their order are the host
ingest's to the bit; a new GPU test checks the layout against the host's sort and cut.
The geometry smoothing still runs on the host in this step, on arrays read back.
The all-observation arm's engine is cloned after the main engine's device ingest (its
DeviceBytes are known only then) and copies the runs on the card as well. DeviceBytes now
also covers the ingest's own peak (90 bytes per reflection, 64 per run before the cut).
Measured: p.hkl and MTZ data identical on cytc and 7qij.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
The marCCD reader read each frame's 16-bit scanlines into scratch and widened them to int32 on the
worker thread (marccd::ReadInto, ~20 % of the image-loop worker CPU on 4nwv), and the 4-byte image was
uploaded. ReadRawImage now reads the scanlines straight into the image's buffer and hands them over as
a new read-only CompressionAlgorithm::UINT16_WIDENED (mode Int32): two bytes a pixel cross PCIe, from
page-locked memory, and the preprocessing kernel widens them itself.
It is not CompressedImageMode::Uint16, which calls 65535 an error pixel; a marCCD's 65535 is its
saturation marker. So the kernel compares each pixel with int32's error marker and the int32 saturation
cap - the comparisons are made on the pixel's value, in int64, rather than in the storage type - and
decides every pixel exactly as for the int32 image the host would have widened it to. The host widening
(JFJochDecompressPtr) is the CPU path.
Test: ImagePreprocessorGPU_StoredEncodingsMatchCPU, with a frame carrying 65535 at a saturation of
65535, against the CPU preprocessor on the host-widened image.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
The CBF reader decoded every frame's x-CBF_BYTE_OFFSET stream on the worker thread - 19-30 % of the
image-loop worker CPU on CBF sets (7qij, 4mtk) - and the 4-byte image was then uploaded. ReadRawImage
now hands over the stream as the file stores it, in the image's own buffer, as a new read-only
CompressionAlgorithm::CBF_BYTE_OFFSET (mode Int32, what it decodes to), and ImagePreprocessorGPU
decodes it on the card into the same int32 image and runs the same preprocessing kernel on it.
The device decode (CBFDecoderGPU) cuts the stream into 256-byte tiles. A token is 1, 3, 7 or 15 bytes,
so a tile can be entered at 15 offsets; for each one thread works out where the decode leaves the
tile, how many pixels it emitted and their sum modulo 2^32 (the walks from different entries meet
within a few bytes, after which they are one walk). An exclusive scan composing these maps gives each
tile its true entry, first pixel and running value, and a second walk writes the pixels. A stream
that ends before the image is refused as on the host.
The host decoder moves to compression/ (DecodeCBFByteOffset) and is reached through
JFJochDecompressPtr, so every host consumer - the CPU build, the azimuthal-integration worker, the
pre-scan's shadow and gain passes - decodes exactly as before, now from the stream. It is the CPU
path, not a fallback: a frame the device refuses is not retried on the host. A process file written
from CBF input stores the decoded int32 image, as it always did. .cbf.gz is still inflated on the host.
Tests: CBFDecoderGPU_MatchesHost (every token size, 0x80 inside payloads, escape runs across tiles,
trailing CIF text), CBFDecoderGPU_ShortStreamIsRefused, ImagePreprocessorGPU_StoredEncodingsMatchCPU.
Checked locally against the host decoder on 36 real frames of 9 open-arm CBF sets (signed 32-bit,
signed and unsigned 16-bit, plain and gzip): identical.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
The rotation predictor used to download every predicted reflection into pinned memory, copy it into
the host vector, build sort keys, std::sort them, gather the 88-byte structs into scratch and copy
them back (OrderOutput), and - on a frame over output_limit - partial_sort the structs themselves
(TruncateToOutput). On a large cell that is a quarter to a third of the image-loop worker CPU
(profiles of 4nwv and 7qij: TruncateToOutput 12-16 %, OrderOutput 9-13 %, plus the memmoves).
Now the same order is built on the device with stable cub radix sorts of an index: delta_phi, then
the packed hkl (OrderOutput's h, k, l, delta_phi), then - over the cap only - partiality descending
and excitation error ascending (TruncateToOutput, whose hkl tie-break the stable sort keeps). Only
the kept rows are gathered and downloaded. Floats are keyed by an order-preserving integer map with
-0 made +0, so the key order is the comparators' order; the only possible difference from the host
is between two solutions of the SAME hkl with bit-identical partiality and excitation error, whose
relative order partial_sort leaves to its heap and the stable sort to delta_phi.
The device buffers grow with half again of slack, so a frame predicting a few more than the last
record (the supercell probe's frames above all) no longer reallocates and predicts again; the host
side is sized for what is returned, at most output_limit.
Test: BraggPredictionRotGPU_DeviceOrderMatchesHost holds the device output against OrderOutput's
order and against TruncateToOutput applied on the host, at four caps.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
One --model now does the right thing by itself:
- A prediction (AlphaFold/ModelCIF, read from the file) goes straight to molecular replacement.
- An experimental structure is placed by the rigid body as before. The null now runs on every
--model run (no NOT_TESTED): "placed" = the fit beats its null (4.51 sd) in the answer's merge
or in the twin hypothesis' merge. Placed: behaviour unchanged (hand and reindex may go into
p.mtz); a subgroup the model fits and prefers is written as <prefix>_mr.
- Not placed (or no crystal cell): molecular replacement with the experimental-model path. It may
relabel the hand into p.mtz past the anomalous veto, never reindexes; another group or the twin
subgroup goes only to <prefix>_mr. Maps, R-free and CC come from validating the MR placement with
every decision switched off; MODEL_FIT stays the rigid body's verdict.
- The twin hypothesis' merge is made in memory with a model where a metric re-ask promoted the
group or the model's group has fewer rotations than the adopted one; written and reported only
where the model chooses it.
- --mr is kept as "--model, searched for by MR straight away".
- Report: MODEL_PLACEMENT= RIGID_BODY|MR|NOT_PLACED, MODEL_DECISIONS_TAKEN gains MR_ENANTIOMORPH,
MODEL_CHOICE_FILE_PREFIX/SPACE_GROUP; the MR keys move into 5. MODEL VALIDATION; MODEL_FIT absent
where no rigid body ran. MR NOT_PLACED is never a warning.
- Battery wrong-model step: check (c) also fails a wrong model placed by MR.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
The scale fit of every model-validation fit (fit_model: the real model, the indexing probe's relabellings
and every null replicate) was FitModelScale on the host - a long-double Levenberg-Marquardt per solvent grid
point, ~80% of the validation's CPU on a 400 A cell. It now runs on the GPU through ModelScaleGPU::FitSolvent,
the engine the rigid body and molecular replacement already use, from a small pool (ModelScaleGPUPool: up to
four engines with their own streams, sized on the card's total memory, at most an eighth of it). The grid,
the fits and the winner rule are FitModelScale's; the sums are taken in double on the device, so R-factors
agree with the host's to rounding. The CPU build, and a GPU build without a card, keep FitModelScale.
The change-of-basis probe gridded each candidate frame with gemmi's serial put_model_density_on_grid, once
for the data as merged and once more for each alternative indexing. It now grids each frame once, with
ModelGrid's parallel PutModelDensityOnGrid (the same grid, bit for bit), from threads outside the pool so the
gridding spreads over it, and scores every labelling on that F_calc. Exact.
4nwv (122k atoms, I222, 400 A cell): model validation 33.2 s -> 14.0 s; every logged R, sigma and decision
the same.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
- The option is --mr <file>. What kind of model it is is read from the file: a prediction where it
is ModelCIF (_ma_qa_metric) or has no cell and one B value per residue in 0-100 (AlphaFold PDB/mmCIF);
an experimental structure otherwise. A prediction is handled as before (pLDDT >= 70 trim, B from
pLDDT, PAE domains where the AlphaFold DB's PAE file sits beside it). An experimental structure is
taken as its polymer - no waters, ligands, hydrogens or alternative conformers, occupancy 1, isotropic
B as refined - with a coordinate error of 0.67 A (a prediction's at pLDDT 90) for sigma_A; a B column
above 100 no longer stops the search.
- The report section is MOLECULAR REPLACEMENT (EXPERIMENTAL), its keys MR_* (MR, MR_MODEL_FILE,
MR_MODEL_KIND, MR_DECISION, MR_SPACE_GROUP, MR_COPIES, MR_HAND_CHECK, MR_SECONDS, ...); the <prefix>_mr
outputs are unchanged.
- The twin hypothesis' merge that is made only for the model to test (a metric re-ask promoted the
indexed lattice's group) is written and reported only where the model chooses the subgroup.
Raw deposited coordinates of nine control sets, unconverted: every one placed and confirmed against the
deposition (all chains as one body), the groups the same as with the prediction or the prepared model.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
The spot-width stage of the pre-scan copied every preprocessed frame back to the host (72 MB a
frame on a 16M detector, page-locked host mirrors per worker) for measurements that read a few
pixels about each spot. Now:
- MeasureSpotsOnTiles: the flux curves and the footprint read (2*24+1)^2 squares about each spot,
gathered on the card (ImagePreprocessorBufferGPU::GatherTiles) - no copy_image_to_host, no host
mirror. The flux curves are bounded at R_BKG_OUT = 20 px. The footprint keeps its arithmetic; a
per-iteration check that its window is inside the square, and a spot whose window walks off it is
measured again from scratch on an 80 px square, then on the whole frame. Where the squares would
be more pixels than the frame, the frame is read. Same pixels in the same order, so the same bits.
Harness over 34 rotation sets (~1500 frames): 0 differences; misses at 24 on a few spots of most
frames, at 54 on up to 80% of frames of a few sets, at 80 on none.
- The azimuthal mapping and the per-worker spot engines are built while the frames are read.
- The tiers no longer wait for each other: workers run on through the visit order, each tier's
answer is drawn from exactly its own frames, and frames measured past the tier the answer stops
at are thrown away - a frame is measured the same whichever tier it is read for.
- PreScanMemo keeps the spot measurement, the defective pixels and the background gain under the
pre-scan key plus the spot-finding and azimuthal settings, so a copy of the run that pre-scans
again on the same inputs reads no frames.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
- Likelihood (Read 2001; Z-score over random positions as McCoy et al. 2005): log-likelihood gain of
Rice/Woolfson E distributions, sigma_A from the pLDDT coordinate error with its scale refined, to
2.5 A (or the 20000 lowest-resolution reflections). Further-copy TF peaks ranked by the gain on the
search's reflections (peaks that lower it dropped); a copy whose TF Z is below 8 also stands out where
its refined likelihood Z-score reaches 8 - only where the copies placed are as many as Matthews makes
most likely. The likelihood is computed only where a translation function does not stand out.
Copy structure factors exact from one FFT of the cell's grid, or read off the molecular transform
where it reaches the likelihood's resolution.
- PAE domains: the AlphaFold DB's predicted_aligned_error JSON beside the model (same name pattern) is
read automatically; average linkage on the symmetrised PAE (10 A, domains >= 40 residues). Where the
whole model is placed nowhere, each domain is searched by itself side by side; placed domains must
agree, and a domain alone may only confirm the answer's group.
- Twin comparison: where the answer's merge chose and the twin hypothesis' merge exists, the chosen
placement read as a crystal in the subgroup (its symmetry images as copies, the subgroup's setting
found from the two cells) against the model searched for afresh in the subgroup, both against the
data twinned by the law (fraction refined on the working set, Yeates 1997); the subgroup is taken by
a 0.03 R-free lead plus its own null/margin -> TWIN_SUBGROUP, written to p_mr with law and fraction.
With --af-model rugnux makes the subgroup merge wherever a metric re-ask promoted the indexed group.
- tNCS rule: a group with fewer copies beside tNCS (screws bridged by the vector, or the enantiomorph)
no longer leaves the choice undecided where the chosen copies, carried into it and refined, fit
worse by 0.03 in R-free.
- Speed: the molecular transform's FFT on the GPU (cuFFT, serialised), no rigid body for trials that
lower the likelihood, re-search only with tNCS, PAE domains searched in parallel.
Survey (193 sets, MR replay on stored merges): single GREEN 58 -> 68, multi 48 -> 58, partial 8 -> 9;
4bwl (and, in full runs, 2wnq and a withdrawn entry of the same protein) TWIN_SUBGROUP P21 instead of
a wrong AGREES; a P21212 set with tNCS now SPACE_GROUP P21212 instead of UNDECIDED; no wrong call.
CPU and GPU builds make the same decisions on 18 demo/multi-copy/twin sets.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
The beam-stop projection (ShadowAccumulatorGPU) and the defective-pixel sums (HotPixelFinderGPU) are
now kept one per card, fed by workers pinned t % gpus like the other pre-scan workers, and folded
into the first card when they are read (cudaMemcpyPeer + an integer add / max-with-count kernel).
All of them are integer sums or a maximum with the first-value rule, so the fold is order-free and
the result is identical to one card's. With one card nothing is folded.
The pre-scan sample is read once and kept (compressed as stored; up to 2 GiB, a frame past that is
read again where it is wanted) for the spot measurement and the defective pixels, which read every
frame again each before - a CBF/marCCD/SMV frame was decoded on the host three times.
A beam-stop detection that finds no shadow leaves the mask as it was, so every probe copy of the run
repeated it on the same inputs. Its answer (shadow, ring centre, mean projection) is now kept in a
memo shared with the copies, keyed on the sampled frames, the geometry the rings are drawn in, the
polarization and the binary mask; up to 4 entries.
Debug TIMING lines for the pre-scan's parts (read and projection, beam stop, defective pixels, spot
measurement). New tests: two accumulators/finders folded match one fed every frame.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
Copy number. The hard "Vm >= 4: MULTI_COPY, no decision" rule is gone. Copies
are searched one after another up to the largest copy number with a Matthews
probability of 1% (a Gumbel distribution with Kantardjieff & Rupp's 2003 mode
and mean, no resolution dependence); COPIES_LIKELY reports the most probable.
Sequential search. Copy n's translation function carries copies 1..n-1 as a
known partial structure: the cross term 2 Re(conj(F_known) F(t)) goes on the
same FFT grid (CPU FFTW and GPU cuFFT; KnownStructureFactors, AssemblyCorrelation).
Its peaks, past those that clash, are ranked by the correlation of the whole
assembly with E^2 (the TF itself, linear in |F|^2, rewards copying a placed
copy onto an origin shift), the first that stands out (TF Z >= 8), packs and
lowers R-free by 0.01 is kept; each copy is refined beside the others held
fixed (RefineRigidBody's fixed copies), and the first copy again beside the
second, the earlier ones once more at the end.
Packing: ClashFraction counts C-alpha clashes with other copies and all their
mates, binned (7mzt: 51 s of clash checks -> 1.9 s).
Search quality: rotation-function peaks hill-climbed off the 6 deg sampling
(6ze4: RF Z 10.4 -> 16.0, 3.3 deg off the deposited orientation -> 0.2); the
first copy goes down the TF list until three placements that pack are
refined, so a group is CLASH only where no offered orientation packs; groups
are searched side by side, and every group the best one leads is searched
again with the best one's copy orientations tried first, the better search kept.
tNCS: the native Patterson of E^2 (5-10 A) off the lattice; placed copies'
orientations join every further copy's search; and where the tNCS vector turns
one group's screw axes into another's (TncsRelatesGroups), a group whose search
stopped at fewer copies does not lose to the other - UNDECIDED. Measured on an
in-house tNCS crystal (vector c/3 along the 3-fold): both hands fit with three
copies to within 0.01 in R-free, and where one hand's search failed the other
was taken as ENANTIOMORPH; now UNDECIDED.
Null: the replicates are placed by the translation function in their own
orientation, as the search placed the real one (a null at the real centroid
reads high), and the gate is 4.51 = t(8) sqrt(1 + 1/9), 3 sd of a nine-draw
null, the gate --model's MODEL_FIT is now read at.
Speed: placements scored on the GPU structure-factor engines; further copies'
trials score with the first copy's bulk solvent held, the assembly refitted at
the end. Survey of 193 sets (jfjoch_mr_replay on stored merges, RTX 5080
shared with other jobs): MR median 1.15 s, max 24 s; demos 0.8-1.1 s.
Report: COPIES and COPIES_LIKELY columns, MODEL_PRIOR_COPIES.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
Three defects the battery's wrong-model null found (5 sets x 5 deposited models of unrelated proteins):
CC(model, data) significance. The per-shell sigma was Fisher's transform on independent reflections,
but the data and a scaled model share the falloff across a shell, the anisotropic B fitted to these
very data, the bulk solvent and the symmetry enhancement epsilon - whatever the model is. 21 of 24
wrong models read CC_MODEL_CONFIRMED_TO_D_MIN to the data's d_min (10-15 sigma in most shells).
The correlation is now formed on I/epsilon and |F_model|^2/epsilon, and its null is empirical: the
data at h against the model at h + d for the 26 nearest lattice offsets, which keeps everything smooth
in reciprocal space and loses what the model knows of h. SIGMA = (z - mean) / max(sd, 1/sqrt(N-3)) in
Fisher z over those 26, a new CC_NULL column shows the null, and a shell confirms only beside a coarser
shell that also clears 3 sigma (one shell alone did so once in 216 wrong-model shells). Prototyped on
the p_maps.mtz of a full open-arm run and the null runs: wrong models confirm 0/24 (was 21/24), the
deposited models confirm 197/200 (was 198/200 in the prototype's reading; the one lost has CC_MODEL
0.05-0.13 in every shell). The report no longer says a model cannot agree by accident.
MODEL_FIT gate. Accepted at 3.0 sample sds of a nine-draw null, as if the sd were known; an unrelated
P1 helical model on a P3(1) helical crystal was ACCEPTED at +3.05. Against 48 draws it reads +2.09 and
the best random placement fits as well as it does, and drawing the null about the cell axes instead
changes nothing (+2.15): the cause is the nine-draw sd, not the orientation. The gate is now 3 sigma
allowing for that: t(8) * sqrt(1 + 1/9) = 4.51. The deposited models of the open arm read +7.0 and up.
The indexing lead stays at 3 sigma (INDEXING_MARGIN_SIGMA): it is read only behind the fit gate, and
a deposited model settles one at +4.15.
Rigid-body GPU reserve. The finest zone's observations were counted by the data's d, but the zones
count them by the d of the index in the model's ASU, and a model's group need not keep the data's
metric (an I2(1)3 model on I222 data: 2320 in the zone against 1712 reserved), so the validation died
with "zone over the engine's reserve". Counted the zones' way, under every indexing probed.
Tests: ModelValidation_CCModelUnrelatedModelConfirmsNothing (Fisher alone reads +4.7 on an unrelated
model with an anisotropic falloff; the new sigma stays under 1), ModelValidation_WrongModelInAGroupOffTheDataMetric
(fails with the old count), ResultReport_ModelValidationSection (CC_NULL column, lone-shell rule).
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
gemmi's table lists some groups a second time on another origin ("P 42 21 2a", "P 21212(a)",
"I 2 2 2a", "C 1 21 1"). SameRotationGroups kept them as separate candidates, so the true group tied
with itself in R-free and the choice came out UNDECIDED: five single-copy sets of the --af-model
survey (and one partial-model set) ended there. A group equal to one already listed up to an origin
shift on the twelfths is now left out.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
- An MR ENANTIOMORPH decision is taken into the answer's label only as --model takes a model's
enantiomorph: ValidateAgainstModel runs on the placed model (in a scratch directory, removed after)
and its adopted_model_enantiomorph - fit over its null, anomalous map at the atoms not inverted -
decides; otherwise ENANTIOMORPH_VETOED. The hand-check numbers go to the MODEL PRIOR section.
- Rotation and translation functions on the GPU (MolecularReplacementGPU.cu): one block per
orientation for the rotation function; per orientation the copies, the bin weights, a gather of the
Crowther-Blow coefficients (CSR lists, fixed order, no atomics) and a cuFFT C2R, block-reduced
statistics finished on the host in a fixed order. The CPU path (FFTW) is unchanged in what it
computes; the orientation-independent part of the translation function (TranslationSetup) is now
built once per group on either path.
- Rescoring: a placement clashing with its mates over twice the limit is not refined, one rigid-body
engine per group, and the null is built only for the candidate the choice falls on.
Measured MR step on the RTX 5080 (before -> after, seconds; hand check extra where it runs):
3r6o 1.77 -> 0.67, neg 2.14 -> 0.37, 11if 1.20 -> 0.51 (+0.6 hand check), 7ris 1.46 -> 0.56 (+0.6),
9zmu 4.71 -> 1.07 (+0.9). Rotation 0.50 -> 0.02 s and translation 1.17 -> 0.13 s on 9zmu.
Mirrored-detector runs of two anomalous lysozyme sets: the hand check vetoes (map inverted at the
model's atoms); the unmirrored runs adopt.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
IndexerThreadPool_FFTIndexerWaitsForGPUWorkBeside filled only the test thread's
card, but an indexer worker takes its card round-robin (pin_gpu: a process-wide
counter modulo the device count). On the CI runner, a container given all of the
host's GPUs, the probe's worker could land on a card nobody had filled, build its
indexer and fail "CHECK(probe.Run(x, recip).error != nullptr)"; the two checks that
expect a wait passed there for the same wrong reason. On a one-card workstation the
worker is always on device 0, so it never showed.
Every card is now filled, each under its own device, and released the same way, and
the fill is topped up right before each Run so memory given back in between by
anything else does not let the indexer fit. The pools are trimmed on every card at
the end. What the test checks is unchanged.
Run 20x alone and with the [CUDAMemHelpers],[AdaptiveSpotFinderGPU] group, also with
another process holding 4 GB of the card: all pass.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
The pools' indexers return their memory to the allocation pool when destroyed; without a
trim the next GPU test in the same process (AdaptiveSpotFinderGPU_RunToRunReproducible)
found the card taken and failed with out of memory.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
A predicted model (AlphaFold mmCIF, pLDDT in the B column) is trimmed at pLDDT 70, given B-factors
from the pLDDT (Oeffner et al. 2022) and placed by molecular replacement, one copy in the ASU:
- rotation function on a uniform 6 deg sampling of the point group's rotation ASU, each orientation
scored by the correlation of E^2 with the symmetry-summed |F|^2 read off the model's molecular
transform, computed once by FFT and interpolated (Navaza 1994);
- full-symmetry Crowther & Blow translation function (one FFT per orientation and group, through
MapFromFPhi/FFTW) for the 50 best orientations in every group with the merge's rotations;
- the best three placements per group through RefineRigidBody (GPU where there is one), bulk solvent
and R-free to 3 A, a C-alpha packing check against symmetry mates, Matthews, and the
random-orientation null for a placed candidate.
Decision: PLACED = TF Z >= 8, clash <= 5%, Vm in [1.5, 4), null >= 3 sd. Within the answer's merge
the placed group with the lowest R-free is chosen where it leads every other packing group by 0.03;
the twin hypothesis' merge is searched, and chosen, only where no group of the answer's merge can
hold the model at all (Matthews, packing). An enantiomorph relabels the answer as --model does;
another group goes to <prefix>_mr.mtz/.cif plus <prefix>_mr_model.pdb; the MODEL PRIOR report
section carries the table. The search runs on the CPU in both builds; without --af-model nothing
changes.
Measured (RTX 5080, MR step): 3r6o twin I41 chosen over I4122 (Matthews + clash) in 1.8 s, solution
0.09 A CA rmsd from Phaser's; 11if P41 -> P43 1.2 s; 7ris P3121 -> P3221 1.7 s; 9zmu P6122 -> P6522
4.7 s; 11if's model against 3r6o's data NOT_PLACED.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01EizBKhTqrAYago9KJG3dA3
