- 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>
165 lines
4.1 KiB
C++
165 lines
4.1 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 <atomic>
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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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// How many probe passes (GPUWorkBeside) and main-line image passes (GPUImagePass) are running, for a
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// thread to wait until none of one kind is.
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std::mutex gpu_work_m;
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std::condition_variable gpu_work_changed;
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int gpu_work_beside_n = 0, gpu_image_passes_n = 0, gpu_merges_n = 0;
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// A card too small for a probe pass beside the main line's image pass, or beside another probe pass:
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// measured on a 16 Mpx detector, two of them together held 8.6 GB. On such a card a probe pass waits,
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// before it starts, for every other one and for the image passes to end, and an image pass for the
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// probe passes - so they take turns, and only when each runs depends on the card, never what it
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// computes. None waits once it is running, so nothing waits in a circle.
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constexpr size_t TAKE_TURNS_BELOW_BYTES = 12'000'000'000ull;
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bool take_turns() {
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static const bool small = get_gpu_count() > 0 && get_gpu_total_memory(0) < TAKE_TURNS_BELOW_BYTES;
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return small;
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}
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void remove(int &n) {
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{
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std::lock_guard lock(gpu_work_m);
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--n;
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}
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gpu_work_changed.notify_all();
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}
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}
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bool gpu_takes_turns() {
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return take_turns();
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}
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GPUWorkBeside::GPUWorkBeside(bool after_merges) {
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std::unique_lock lock(gpu_work_m);
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if (take_turns())
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gpu_work_changed.wait(lock, [after_merges] {
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return gpu_image_passes_n == 0 && gpu_work_beside_n == 0 && (!after_merges || gpu_merges_n == 0);
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});
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++gpu_work_beside_n;
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}
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GPUWorkBeside::~GPUWorkBeside() {
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remove(gpu_work_beside_n);
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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_m);
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return gpu_work_changed.wait_for(lock, timeout, [] { return gpu_work_beside_n == 0; });
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}
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GPUImagePass::GPUImagePass() {
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std::unique_lock lock(gpu_work_m);
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if (take_turns())
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gpu_work_changed.wait(lock, [] { return gpu_work_beside_n == 0; });
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++gpu_image_passes_n;
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}
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GPUImagePass::~GPUImagePass() {
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remove(gpu_image_passes_n);
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}
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GPUMerge::GPUMerge() {
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std::lock_guard lock(gpu_work_m);
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++gpu_merges_n;
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}
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GPUMerge::~GPUMerge() {
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remove(gpu_merges_n);
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}
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void wait_for_gpu_image_passes() {
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std::unique_lock lock(gpu_work_m);
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gpu_work_changed.wait(lock, [] { return gpu_image_passes_n == 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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namespace {
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std::atomic<bool> gpus_hidden{false};
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}
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void hide_gpus() {
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gpus_hidden = true;
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}
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bool gpus_are_hidden() {
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return gpus_hidden;
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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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size_t get_gpu_total_memory(int32_t) {
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return 0;
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}
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bool get_gpu_free_memory(size_t &, size_t &) {
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return false;
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}
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void set_gpu(int32_t dev_id) {}
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bool gpu_direct_host_read() {
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return false;
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}
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void pin_gpu() {}
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void pin_gpu(int32_t dev_id) {}
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ScopedGpuPin::ScopedGpuPin(int32_t) {}
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ScopedGpuPin::~ScopedGpuPin() {}
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void enable_gpu_numa_binding() {}
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std::vector<int> get_gpu_numa_nodes() { return {}; }
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void set_gpu_blocking_sync() {}
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void create_gpu_contexts(int32_t) {}
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void release_gpu_contexts() {}
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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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