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* jfjoch_broker: Optional per-dataset authentication - statistics, images and plots can require a bearer token, which jfjoch_viewer supports. * jfjoch_viewer: Dark mode and a theme-matched colour scheme, a magnifier panel, and simpler contrast and background controls. * Rugnux: Multiple performance improvements on GPU and CPU (CPU-only processing up to 40% faster, faster image decoding on ARM), with unchanged results. * Rugnux: `--model` rigid-body refinement runs on the GPU, and the model-validation check is faster and more reliable. * Rugnux: Improved scaling and merging - error model, outlier rejection, absorption correction and French-Wilson amplitudes now agree more closely with XDS and ctruncate. * Rugnux: Improved integration - radial background on powder and ice rings, crowded rotation data keep their reflections, and CPU-only builds integrate large unit cells as GPU builds do. * Rugnux: More robust detector geometry - measured beam centre, X-ray bandwidth and goniometer rate, and geometry refinement accepted only on significant evidence. * Rugnux: Merged files are written in the standard setting, or in the setting of a reference MTZ, structure-factor mmCIF or model, with its free-R flags. * Rugnux: Richer report - ice and powder rings, further lattices, superstructure candidates and mosaicity, with warnings worded as prompts to check. * Rugnux: Clear error messages when a data set needs more GPU or host memory than is available. Reviewed-on: #83 Co-authored-by: Filip Leonarski <filip.leonarski@psi.ch>
189 lines
7.7 KiB
C++
189 lines
7.7 KiB
C++
// SPDX-FileCopyrightText: 2026 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 <catch2/catch_all.hpp>
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#include <algorithm>
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#include <cmath>
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#include <mutex>
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#include <numeric>
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#include <stdexcept>
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#include <vector>
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#include "../common/ParallelFor.h"
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namespace {
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const std::vector<size_t> THREAD_COUNTS = {0, 1, 2, 3, 8, 64};
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const std::vector<int> SIZES = {0, 1, 2, 7, 64, 1000};
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}
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// The split is fixed and contiguous, which is what lets a pass whose per-element work is independent
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// give the serial answer bit for bit. Every element has to land in exactly one slice, the slices have
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// to tile [0, n) in order, and there must never be an empty one.
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TEST_CASE("ParallelChunks_SlicesTileTheRange", "[ParallelFor]") {
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for (int n: SIZES) {
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for (size_t nthreads: THREAD_COUNTS) {
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CAPTURE(n, nthreads);
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std::mutex m;
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std::vector<std::pair<int, int>> slices;
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std::vector<int> visits(static_cast<size_t>(std::max(n, 1)), 0);
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ParallelChunks(n, nthreads, [&](int lo, int hi) {
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{
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std::scoped_lock lock(m);
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slices.emplace_back(lo, hi);
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}
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for (int i = lo; i < hi; i++)
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visits[static_cast<size_t>(i)]++; // slices are disjoint, so no lock needed
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});
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std::sort(slices.begin(), slices.end());
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int expected_lo = 0;
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for (const auto &[lo, hi]: slices) {
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CHECK(lo == expected_lo);
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CHECK(hi > lo); // never an empty slice
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expected_lo = hi;
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}
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CHECK(expected_lo == n); // and they reach the end
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CHECK(slices.size() <= static_cast<size_t>(std::max(n, 0)));
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for (int i = 0; i < n; i++)
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CHECK(visits[static_cast<size_t>(i)] == 1);
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}
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}
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}
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// Work stealing, so a worker takes whatever is next rather than a fixed slice - but every item still
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// has to be done exactly once, whatever order they come out in.
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TEST_CASE("ParallelFor_VisitsEveryItemOnce", "[ParallelFor]") {
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for (int n: SIZES) {
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for (size_t nthreads: THREAD_COUNTS) {
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CAPTURE(n, nthreads);
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std::vector<std::atomic<int>> visits(static_cast<size_t>(std::max(n, 1)));
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for (auto &v: visits)
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v.store(0);
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ParallelFor(n, nthreads, [&](int i) { visits[static_cast<size_t>(i)].fetch_add(1); });
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for (int i = 0; i < n; i++)
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CHECK(visits[static_cast<size_t>(i)].load() == 1);
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}
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}
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}
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// One thread means the caller's loop, in order - the property the serial fallbacks rely on.
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TEST_CASE("ParallelFor_IsSerialAndInOrderForOneThread", "[ParallelFor]") {
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for (size_t nthreads: {size_t{0}, size_t{1}}) {
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CAPTURE(nthreads);
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std::vector<int> order;
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ParallelFor(16, nthreads, [&](int i) { order.push_back(i); });
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std::vector<int> expected(16);
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std::iota(expected.begin(), expected.end(), 0);
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CHECK(order == expected);
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}
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}
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// Splitting the work must not change the answer. Each element is written by exactly one worker, so
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// the result has to match the serial loop element for element, at every thread count.
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TEST_CASE("ParallelFor_SplitDoesNotChangeTheResult", "[ParallelFor]") {
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constexpr int N = 1000;
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std::vector<double> serial(N);
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for (int i = 0; i < N; i++)
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serial[static_cast<size_t>(i)] = std::sin(i * 0.001) * 1e6 + i;
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for (size_t nthreads: THREAD_COUNTS) {
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CAPTURE(nthreads);
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std::vector<double> chunked(N, 0.0), stolen(N, 0.0);
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ParallelChunks(N, nthreads, [&](int lo, int hi) {
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for (int i = lo; i < hi; i++)
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chunked[static_cast<size_t>(i)] = std::sin(i * 0.001) * 1e6 + i;
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});
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ParallelFor(N, nthreads, [&](int i) {
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stolen[static_cast<size_t>(i)] = std::sin(i * 0.001) * 1e6 + i;
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});
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CHECK(chunked == serial); // bit for bit, not approximately
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CHECK(stolen == serial);
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}
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}
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// The blocks depend on n alone: the same tiling of [0, n), block b always the same range, whatever the
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// thread count.
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TEST_CASE("ParallelBlocks_PartitionIgnoresTheThreadCount", "[ParallelFor]") {
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for (int n: {0, 1, 7, 4095, 4096, 100000, 5000000}) {
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const int nb = ReductionBlocks(n);
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CHECK(nb <= MAX_REDUCTION_BLOCKS);
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std::vector<std::pair<int, int>> reference;
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for (size_t nthreads: THREAD_COUNTS) {
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CAPTURE(n, nthreads);
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std::vector<std::pair<int, int>> range(static_cast<size_t>(nb), {-1, -1});
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ParallelBlocks(n, nthreads, [&](int b, int lo, int hi) { range[static_cast<size_t>(b)] = {lo, hi}; });
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int expected_lo = 0;
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for (const auto &[lo, hi]: range) {
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CHECK(lo == expected_lo);
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CHECK(hi > lo);
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expected_lo = hi;
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}
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CHECK(expected_lo == n);
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if (reference.empty()) reference = range;
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CHECK(range == reference);
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}
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}
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}
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// What the blocks are for: a floating-point sum folded per block and added up in block order has the
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// same bits at any thread count. The terms span many decades, so any change of grouping would show.
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TEST_CASE("ParallelBlocks_SumIsTheSameAtAnyThreadCount", "[ParallelFor]") {
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constexpr int N = 1000003;
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constexpr int NCELL = 37;
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std::vector<double> term(N);
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for (int i = 0; i < N; i++)
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term[static_cast<size_t>(i)] = std::exp(std::sin(i * 0.37) * 20.0) * (i % 3 == 0 ? -1.0 : 1.0);
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auto blocked_sums = [&](size_t nthreads) {
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const int nb = ReductionBlocks(N);
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std::vector<double> part(static_cast<size_t>(nb), 0.0);
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std::vector<std::vector<double>> cell_part(static_cast<size_t>(nb), std::vector<double>(NCELL, 0.0));
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ParallelBlocks(N, nthreads, [&](int b, int lo, int hi) {
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for (int i = lo; i < hi; i++) {
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part[static_cast<size_t>(b)] += term[static_cast<size_t>(i)];
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cell_part[static_cast<size_t>(b)][static_cast<size_t>(i % NCELL)] += term[static_cast<size_t>(i)];
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}
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});
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std::vector<double> out(NCELL + 1, 0.0);
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for (int b = 0; b < nb; b++) {
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out[NCELL] += part[static_cast<size_t>(b)];
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for (int c = 0; c < NCELL; c++)
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out[static_cast<size_t>(c)] += cell_part[static_cast<size_t>(b)][static_cast<size_t>(c)];
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}
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return out;
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};
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const std::vector<double> one = blocked_sums(1);
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for (size_t nthreads: {size_t{3}, size_t{7}, size_t{32}}) {
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CAPTURE(nthreads);
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CHECK(blocked_sums(nthreads) == one); // bit for bit, not approximately
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}
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}
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// A negative or zero count is a no-op rather than an error - callers pass a computed size.
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TEST_CASE("ParallelFor_DoesNothingForAnEmptyRange", "[ParallelFor]") {
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int calls = 0;
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for (int n: {0, -1, -1000}) {
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ParallelChunks(n, 8, [&](int, int) { calls++; });
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ParallelFor(n, 8, [&](int) { calls++; });
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ParallelBlocks(n, 8, [&](int, int, int) { calls++; });
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}
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CHECK(calls == 0);
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}
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// An exception thrown in a worker reaches the caller rather than terminating: the futures are waited
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// on, so the other workers finish first and only then does it propagate.
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TEST_CASE("ParallelFor_PropagatesAnException", "[ParallelFor]") {
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CHECK_THROWS_AS(ParallelChunks(64, 4, [](int lo, int) {
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if (lo == 0) throw std::runtime_error("from a chunk");
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}), std::runtime_error);
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CHECK_THROWS_AS(ParallelFor(64, 4, [](int i) {
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if (i == 0) throw std::runtime_error("from an item");
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}), std::runtime_error);
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}
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