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BraggIntegrationEngineMetal is BraggIntegrationEngineCPU with its integer pixel work run as Metal kernels, one thread per reflection: the neighbour mask, the owner map, pass A's walk over every box and the background clip. Everything in double, the profile learning and pass B stay on the CPU, in the CPU engine's own code - Apple GPUs have no double, and no GPU exp matches macOS expf bit for bit. The sums are integers, so the GPU's order does not matter; the stencil arithmetic is the same BraggStencil.h, compiled at run time (MSL 3.0, no offline Metal toolchain needed) with fast math off (sqrt and division correctly rounded, measured) and fp contraction off, as the host already builds (-ffp-contract=off, now extended to Objective-C++). The image is read in place (unified memory). The CPU engine is split so both engines share the finishing code: pass A counts into BraggBoxSums (integer; the double ring sums were exact integers already), the clip threshold is its integer floor, FinishPassA and ProfilePasses are shared. Verified byte-identical p.hkl and MTZ data before/after the split and with the Metal engine on lysozyme, cytochrome C, myoglobin, 4nwv and a small-molecule set. Measured on an Apple M6 (wall, CPU engine -> Metal): 4nwv 180.5 -> 151.2 s, cytochrome C 72.7 -> 59.8 s, lysozyme 29.2 -> 22.5 s, myoglobin 43.3 -> 41.2 s; the sparse small-molecule set +1.5 s (few reflections per frame leave the GPU idle). CI: a macOS step runs tests/BraggIntegrationEngineMetalTest.cpp, which requires every float of every reflection and every count to match the CPU engine over 19 setting combinations on random crowded frames (negative pixels, exact-pixel ties, hot, masked and overloaded pixels), three frames per engine; it fails without a Metal device and if the GPU path fell back to the CPU. Ten planted kernel bugs were each caught. rugnux --no-gpu hides the Metal device too (gpus_are_hidden). Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
209 lines
6.5 KiB
Plaintext
209 lines
6.5 KiB
Plaintext
// 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 <fstream>
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#include <thread>
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#include <atomic>
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#include <mutex>
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#include <vector>
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#include <cuda.h>
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#include "CUDAWrapper.h"
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#include "ThreadAffinity.h"
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#include "JFJochException.h"
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inline void cuda_err(cudaError_t val) {
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if (val != cudaSuccess)
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throw JFJochException(JFJochExceptionCategory::GPUCUDAError, cudaGetErrorString(val));
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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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int32_t get_gpu_count() {
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if (gpus_hidden)
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return 0;
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int device_count;
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cudaError_t val = cudaGetDeviceCount(&device_count);
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switch (val) {
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case cudaSuccess:
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return device_count;
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case cudaErrorNoDevice:
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case cudaErrorInsufficientDriver:
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return 0;
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default:
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throw JFJochException(JFJochExceptionCategory::GPUCUDAError, cudaGetErrorString(val));
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}
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}
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std::vector<std::string> get_gpu_names() {
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std::vector<std::string> names;
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const int32_t count = get_gpu_count();
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names.reserve(count);
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for (int32_t i = 0; i < count; i++) {
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cudaDeviceProp prop{};
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// A device that cannot be queried still exists and still gets work, so it is listed - just
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// without a name. Losing the whole list over one unreadable device would be worse.
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if (cudaGetDeviceProperties(&prop, i) == cudaSuccess)
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names.emplace_back(prop.name);
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else
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names.emplace_back("unknown GPU");
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}
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return names;
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}
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size_t get_gpu_total_memory(int32_t dev_id) {
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cudaDeviceProp prop{};
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if (cudaGetDeviceProperties(&prop, dev_id) != cudaSuccess)
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return 0;
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return prop.totalGlobalMem;
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}
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void set_gpu(int32_t dev_id) {
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auto dev_count = get_gpu_count();
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// Ignore if no GPU present
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if (dev_count > 0) {
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if ((dev_id < 0) || (dev_id >= dev_count))
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throw JFJochException(JFJochExceptionCategory::InputParameterInvalid, "Device ID cannot be negative");
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cuda_err(cudaSetDevice(dev_id));
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}
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}
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namespace {
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std::atomic<bool> gpu_numa_binding{false};
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// The NUMA node of each device, read once.
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int GpuNumaNode(int32_t dev_id) {
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static std::mutex m;
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static std::vector<int> node;
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std::lock_guard<std::mutex> lock(m);
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if (node.empty()) {
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const int32_t count = get_gpu_count();
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node.assign(count, -1);
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for (int32_t i = 0; i < count; i++) {
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char bus_id[32] = {};
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if (cudaDeviceGetPCIBusId(bus_id, sizeof(bus_id), i) == cudaSuccess)
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node[i] = NumaNodeOfPciDevice(bus_id);
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}
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}
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return dev_id < static_cast<int32_t>(node.size()) ? node[dev_id] : -1;
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}
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}
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std::vector<int> get_gpu_numa_nodes() {
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std::vector<int> nodes;
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for (int32_t i = 0; i < get_gpu_count(); i++)
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nodes.push_back(GpuNumaNode(i));
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return nodes;
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}
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void enable_gpu_numa_binding() {
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gpu_numa_binding = true;
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}
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void pin_gpu(int32_t dev_id) {
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if (get_gpu_count() == 0)
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return;
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set_gpu(dev_id);
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if (gpu_numa_binding)
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PinThreadToNumaNode(GpuNumaNode(dev_id));
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}
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ScopedGpuPin::ScopedGpuPin(int32_t dev_id) {
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if (get_gpu_count() == 0)
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return;
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cuda_err(cudaGetDevice(&prev_device));
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const int node = gpu_numa_binding ? GpuNumaNode(dev_id) : -1;
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if (node >= 0)
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prev_cpus = GetThreadCpus();
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set_gpu(dev_id);
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PinThreadToNumaNode(node);
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}
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ScopedGpuPin::~ScopedGpuPin() {
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SetThreadCpus(prev_cpus);
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if (prev_device >= 0)
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cudaSetDevice(prev_device); // not set_gpu: a destructor must not throw
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}
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void pin_gpu() {
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static std::atomic<uint32_t> counter{0};
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auto dev_count = get_gpu_count();
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if (dev_count > 0)
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pin_gpu(static_cast<int32_t>(counter.fetch_add(1) % dev_count));
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}
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void set_gpu_blocking_sync() {
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// Through the driver's primary-context flags, which a context created later takes: the runtime's
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// cudaSetDevice creates the device's context, serially, and on four cards that was most of a
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// second before the run had started. The function is taken from the driver at run time, so the
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// program does not link against it and still starts on a machine without one.
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// Without a device nothing is called at all: even cudaGetLastError() would load the driver.
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if (get_gpu_count() == 0)
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return;
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void *fn = nullptr;
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cudaDriverEntryPointQueryResult found{};
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if (cudaGetDriverEntryPointByVersion("cuDevicePrimaryCtxSetFlags", &fn, 12000, cudaEnableDefault, &found)
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!= cudaSuccess
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|| found != cudaDriverEntryPointSuccess) {
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cudaGetLastError();
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return;
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}
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const auto set_flags = reinterpret_cast<CUresult (*)(CUdevice, unsigned int)>(fn);
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for (int32_t i = 0; i < get_gpu_count(); i++)
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set_flags(i, CU_CTX_SCHED_BLOCKING_SYNC);
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}
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void create_gpu_contexts(int32_t first_device) {
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std::vector<std::thread> threads;
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for (int32_t i = first_device; i < get_gpu_count(); i++)
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threads.emplace_back([i] {
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// A device that fails here fails again, with its error, where it is used.
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if (cudaSetDevice(i) != cudaSuccess || cudaFree(nullptr) != cudaSuccess)
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cudaGetLastError();
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});
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for (auto &t : threads)
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t.join();
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}
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void release_gpu_contexts() {
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std::vector<std::thread> threads;
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for (int32_t i = 0; i < get_gpu_count(); i++)
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threads.emplace_back([i] {
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if (cudaSetDevice(i) != cudaSuccess || cudaDeviceReset() != cudaSuccess)
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cudaGetLastError();
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});
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for (auto &t : threads)
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t.join();
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}
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void cuda_clear_error() {
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if (!gpus_hidden) // hidden, nothing has used CUDA, and this call would still load the driver
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cudaGetLastError();
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}
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void cuda_throw_if_context_lost() {
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if (gpus_hidden)
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return;
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// cudaFree(nullptr) frees nothing, but a lost context fails it. The last error cannot say the same:
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// once cudaGetLastError() has returned a sticky error, it and cudaPeekAtLastError() report success.
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const cudaError_t err = cudaFree(nullptr);
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if (err != cudaSuccess)
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throw JFJochException(JFJochExceptionCategory::GPUCUDAError,
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std::string("CUDA device unusable after an unrecoverable error: ")
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+ cudaGetErrorString(err));
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}
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