Files
Jungfraujoch/common/CUDAWrapper.cpp
T
leonarski_fandClaude Opus 5.5 c644c4e38a rugnux: GPU merge waits for the probe pass running beside it instead of failing
The speculative geometry probe (StartSpeculativeGeometryProbe) runs an
indexing-only pass on a copy of the run beside the pass's scaling merge.
It holds ~4.5 GB of engines (25 image-sized spot-finding buffers, the
per-engine tables, FFT indexers) plus what its streams' pool retains, for a
few seconds. When a merge allocation landed in that window and did not fit,
RotationScaleMergeGPU::Impl::Alloc reported "needs more GPU memory than this
card has ... too large for GPU scaling", although the per-observation arrays
were 1.3 GB on a 16.6 GB card and the set runs fine alone. Timing-dependent:
one full-battery failure, not reproduced in ~15 plain reruns; reproduced
deterministically by letting the probe hold extra device memory.

- GPUWorkBeside (common/CUDAWrapper): a process-wide count of GPU work
  running beside the main line, with a condition variable signalled when the
  last one ends. The speculative probe holds one for its whole pass.
- Alloc: on failure, wait for that work to end (bounded, 10 min, then a
  "GPU busy" error), then ask for the same buffer once more. Nothing else in
  flight means no wait, so a genuine shortage still fails at once. The
  computation is the same whenever the allocation succeeds, so results do
  not depend on the wait.
- "Too large for this card" is now said only by the early check of the
  per-observation arrays against total device memory; a later shortage with
  nothing running beside gets its own message (buffer size, free/total, and
  that another program or the set's size is the cause).

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01D1G8gJVAy6gp1K5Dz3NE5C
2026-09-29 07:44:01 +02:00

79 lines
1.9 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 <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 {
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() {
return 0;
}
std::vector<std::string> get_gpu_names() {
return {};
}
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