v1.0.0-rc.173 (#83)

* 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>

common/CUDAWrapper.cpp

@@ -3,6 +3,9 @@
 
 #include "CUDAWrapper.h"
 
+#include <condition_variable>
+#include <mutex>
+
 // Build-independent: the CUDA build gets get_gpu_names() from CUDAWrapper.cu, the CPU-only build from
 // the stub below, and this collapses whichever list came back. Four identical cards read better as
 // "4x <name>" than as the same name four times, and a mixed machine keeps one group per model.
@@ -24,6 +27,30 @@ std::string get_gpu_description() {
     return out;
 }
 
+namespace {
+    std::mutex gpu_work_beside_mutex;
+    std::condition_variable gpu_work_beside_done;
+    int gpu_work_beside = 0;
+}
+
+GPUWorkBeside::GPUWorkBeside() {
+    std::lock_guard lock(gpu_work_beside_mutex);
+    ++gpu_work_beside;
+}
+
+GPUWorkBeside::~GPUWorkBeside() {
+    {
+        std::lock_guard lock(gpu_work_beside_mutex);
+        --gpu_work_beside;
+    }
+    gpu_work_beside_done.notify_all();
+}
+
+bool wait_for_gpu_work_beside(std::chrono::seconds timeout) {
+    std::unique_lock lock(gpu_work_beside_mutex);
+    return gpu_work_beside_done.wait_for(lock, timeout, [] { return gpu_work_beside == 0; });
+}
+
 #ifndef JFJOCH_USE_CUDA
 
 int32_t get_gpu_count() {
@@ -38,6 +65,14 @@ void set_gpu(int32_t dev_id) {}
 
 void pin_gpu() {}
 
+void pin_gpu(int32_t dev_id) {}
+
+void enable_gpu_numa_binding() {}
+
+void set_gpu_blocking_sync() {}
+
 void cuda_clear_error() {}
 
+void cuda_throw_if_context_lost() {}
+
 #endif