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* jfjoch_broker: Optional per-dataset authentication - statistics, images and plots can require a bearer token, which jfjoch_viewer supports. * jfjoch_viewer: Dark mode and a theme-matched colour scheme, a magnifier panel, and simpler contrast and background controls. * Rugnux: Multiple performance improvements on GPU and CPU (CPU-only processing up to 40% faster, faster image decoding on ARM), with unchanged results. * Rugnux: `--model` rigid-body refinement runs on the GPU, and the model-validation check is faster and more reliable. * Rugnux: Improved scaling and merging - error model, outlier rejection, absorption correction and French-Wilson amplitudes now agree more closely with XDS and ctruncate. * Rugnux: Improved integration - radial background on powder and ice rings, crowded rotation data keep their reflections, and CPU-only builds integrate large unit cells as GPU builds do. * Rugnux: More robust detector geometry - measured beam centre, X-ray bandwidth and goniometer rate, and geometry refinement accepted only on significant evidence. * Rugnux: Merged files are written in the standard setting, or in the setting of a reference MTZ, structure-factor mmCIF or model, with its free-R flags. * Rugnux: Richer report - ice and powder rings, further lattices, superstructure candidates and mosaicity, with warnings worded as prompts to check. * Rugnux: Clear error messages when a data set needs more GPU or host memory than is available. Reviewed-on: #83 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
79 lines
1.9 KiB
C++
79 lines
1.9 KiB
C++
// SPDX-FileCopyrightText: 2024 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
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// SPDX-License-Identifier: GPL-3.0-only
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#include "CUDAWrapper.h"
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#include <condition_variable>
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#include <mutex>
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// Build-independent: the CUDA build gets get_gpu_names() from CUDAWrapper.cu, the CPU-only build from
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// the stub below, and this collapses whichever list came back. Four identical cards read better as
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// "4x <name>" than as the same name four times, and a mixed machine keeps one group per model.
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std::string get_gpu_description() {
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const auto names = get_gpu_names();
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std::string out;
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for (size_t i = 0; i < names.size();) {
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size_t n = 1;
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while (i + n < names.size() && names[i + n] == names[i])
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n++;
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if (!out.empty())
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out += ", ";
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if (n > 1)
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out += std::to_string(n) + "x ";
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out += names[i];
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i += n;
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}
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return out;
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}
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namespace {
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std::mutex gpu_work_beside_mutex;
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std::condition_variable gpu_work_beside_done;
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int gpu_work_beside = 0;
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}
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GPUWorkBeside::GPUWorkBeside() {
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std::lock_guard lock(gpu_work_beside_mutex);
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++gpu_work_beside;
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}
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GPUWorkBeside::~GPUWorkBeside() {
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{
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std::lock_guard lock(gpu_work_beside_mutex);
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--gpu_work_beside;
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}
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gpu_work_beside_done.notify_all();
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}
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bool wait_for_gpu_work_beside(std::chrono::seconds timeout) {
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std::unique_lock lock(gpu_work_beside_mutex);
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return gpu_work_beside_done.wait_for(lock, timeout, [] { return gpu_work_beside == 0; });
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}
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#ifndef JFJOCH_USE_CUDA
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int32_t get_gpu_count() {
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return 0;
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}
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std::vector<std::string> get_gpu_names() {
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return {};
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}
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void set_gpu(int32_t dev_id) {}
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void pin_gpu() {}
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void pin_gpu(int32_t dev_id) {}
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void enable_gpu_numa_binding() {}
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void set_gpu_blocking_sync() {}
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void cuda_clear_error() {}
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void cuda_throw_if_context_lost() {}
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#endif
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