Files
Jungfraujoch/common/CUDAWrapper.cpp
T
leonarski_fandClaude Opus 5.5 bf8f0a482d GPU memory: beam-centre point transforms in two spectra and one work area; a probe on a small card indexes with one FFT indexer
- BeamCenterFFTGPU's point surfaces: each spectrum product goes in the place
  of a spectrum that is finished with (a.a where m's spectrum goes before it
  is there, a.m and m.m and a^2.m in a's), so the third spectrum buffer goes,
  and the forward and inverse plans share one work area - they never run at
  once. The same transforms and products in the same order, so the surfaces
  are unchanged. 2.6 -> 2.05 GB on a 16 Mpx detector, the largest single
  holder at the first pass's peak; DeviceMemoryNeeded counts it so.
- Rugnux::FirstPassIndexerThreads: a probe pass beside the main line on a card
  below the take-turns size (gpu_takes_turns(), 12 GB on the first card's
  total) builds one FFT indexer for its pools instead of two, and indexes its
  two schemes one after the other. Which indexer indexes a frame does not
  change what it finds.

Measured with the RTX 5080's total read as 8.55 GB: first-pass peak 6.50 ->
5.92 GB (8qaw), 5.76 -> 4.84 GB (4nwv) together with the batched FFT indexer;
the speculative probe after the output 6.30 -> 4.35 GB (8qaw). Output
unchanged: p.hkl byte-identical on cytc_x10sa, myob_x10sa, 4nwv and 8qaw on
this card and on the 8.55 GB simulation; run times unchanged within noise.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
2026-10-15 16:44:01 +02:00

165 lines
4.1 KiB
C++

// SPDX-FileCopyrightText: 2024 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#include "CUDAWrapper.h"
#include <atomic>
#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.
std::string get_gpu_description() {
const auto names = get_gpu_names();
std::string out;
for (size_t i = 0; i < names.size();) {
size_t n = 1;
while (i + n < names.size() && names[i + n] == names[i])
n++;
if (!out.empty())
out += ", ";
if (n > 1)
out += std::to_string(n) + "x ";
out += names[i];
i += n;
}
return out;
}
namespace {
// How many probe passes (GPUWorkBeside) and main-line image passes (GPUImagePass) are running, for a
// thread to wait until none of one kind is.
std::mutex gpu_work_m;
std::condition_variable gpu_work_changed;
int gpu_work_beside_n = 0, gpu_image_passes_n = 0, gpu_merges_n = 0;
// A card too small for a probe pass beside the main line's image pass, or beside another probe pass:
// measured on a 16 Mpx detector, two of them together held 8.6 GB. On such a card a probe pass waits,
// before it starts, for every other one and for the image passes to end, and an image pass for the
// probe passes - so they take turns, and only when each runs depends on the card, never what it
// computes. None waits once it is running, so nothing waits in a circle.
constexpr size_t TAKE_TURNS_BELOW_BYTES = 12'000'000'000ull;
bool take_turns() {
static const bool small = get_gpu_count() > 0 && get_gpu_total_memory(0) < TAKE_TURNS_BELOW_BYTES;
return small;
}
void remove(int &n) {
{
std::lock_guard lock(gpu_work_m);
--n;
}
gpu_work_changed.notify_all();
}
}
bool gpu_takes_turns() {
return take_turns();
}
GPUWorkBeside::GPUWorkBeside(bool after_merges) {
std::unique_lock lock(gpu_work_m);
if (take_turns())
gpu_work_changed.wait(lock, [after_merges] {
return gpu_image_passes_n == 0 && gpu_work_beside_n == 0 && (!after_merges || gpu_merges_n == 0);
});
++gpu_work_beside_n;
}
GPUWorkBeside::~GPUWorkBeside() {
remove(gpu_work_beside_n);
}
bool wait_for_gpu_work_beside(std::chrono::seconds timeout) {
std::unique_lock lock(gpu_work_m);
return gpu_work_changed.wait_for(lock, timeout, [] { return gpu_work_beside_n == 0; });
}
GPUImagePass::GPUImagePass() {
std::unique_lock lock(gpu_work_m);
if (take_turns())
gpu_work_changed.wait(lock, [] { return gpu_work_beside_n == 0; });
++gpu_image_passes_n;
}
GPUImagePass::~GPUImagePass() {
remove(gpu_image_passes_n);
}
GPUMerge::GPUMerge() {
std::lock_guard lock(gpu_work_m);
++gpu_merges_n;
}
GPUMerge::~GPUMerge() {
remove(gpu_merges_n);
}
void wait_for_gpu_image_passes() {
std::unique_lock lock(gpu_work_m);
gpu_work_changed.wait(lock, [] { return gpu_image_passes_n == 0; });
}
#ifndef JFJOCH_USE_CUDA
int32_t get_gpu_count() {
return 0;
}
namespace {
std::atomic<bool> gpus_hidden{false};
}
void hide_gpus() {
gpus_hidden = true;
}
bool gpus_are_hidden() {
return gpus_hidden;
}
std::vector<std::string> get_gpu_names() {
return {};
}
size_t get_gpu_total_memory(int32_t) {
return 0;
}
bool get_gpu_free_memory(size_t &, size_t &) {
return false;
}
void set_gpu(int32_t dev_id) {}
bool gpu_direct_host_read() {
return false;
}
void pin_gpu() {}
void pin_gpu(int32_t dev_id) {}
ScopedGpuPin::ScopedGpuPin(int32_t) {}
ScopedGpuPin::~ScopedGpuPin() {}
void enable_gpu_numa_binding() {}
std::vector<int> get_gpu_numa_nodes() { return {}; }
void set_gpu_blocking_sync() {}
void create_gpu_contexts(int32_t) {}
void release_gpu_contexts() {}
void cuda_clear_error() {}
void cuda_throw_if_context_lost() {}
#endif