leonarski_fandClaude Opus 5.5
4b1d7feebd
- set_gpu_blocking_sync(): every device is put in cudaDeviceScheduleBlockingSync before its context exists, so a host thread waiting on the GPU sleeps instead of spinning on a core. On a 16M rotation run a fifth of all CPU time was that spinning; wall time unchanged within noise. Called first thing in rugnux. - enable_gpu_numa_binding(): from then on pin_gpu() (and the new pin_gpu(dev), used by the first-pass spot workers that take a card by index) also keeps the thread on the CPUs of the NUMA node the card hangs off. The node and its CPUs come from /sys (no libnuma), intersected with the process's own mask; Linux only, and nothing happens on a machine with a single node. rugnux turns it on; the broker does not. - A thread inherits its creator's affinity, so the shared ParallelFor pool would run every later pass on one socket if a pinned worker created it: its threads now reset to the mask the process started with (common/ThreadAffinity). Byte-identical output. The NUMA part is a no-op on the single-node test box and still has to be measured on a two-socket machine. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01D1G8gJVAy6gp1K5Dz3NE5C
50 lines
1.2 KiB
C++
50 lines
1.2 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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// 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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#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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#endif
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