Files
plotter/tests/test_series.cpp
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Martino Ferrari 0ac5d47fa6 perf(data): absolute-space pyramid so acquire() is O(produced), not O(window)
Frame cost was O(capacity x levels) once the ring saturated: acquire() saw the
window base move (it always moves once the ring wraps), cleared the pyramid and
re-appended all 1,048,576 retained samples, and append() walked every live level
with no early stop -- 13,647,936 bucket merges and 73.8 ms per frame at a
1M-sample window, scaling with the window, which is what capped the 1 MS/s demo
at 32-58 ms/frame while it was still filling.

Index the pyramid in absolute bucket space instead. Level l bucket k covers the
samples [k * (kBucket << l), (k + 1) * (kBucket << l)), so a bucket's span never
depends on where the window starts and the window can slide over the aggregate
instead of forcing it to be rebuilt. Each level keeps its complete buckets in a
deque plus the absolute base of its first entry; append() folds a sample into the
single in-progress level-0 bucket and, only when that bucket completes, cascades
upward, stopping at the first level that gains no new entry; a new drop_before()
slides the window by discarding leading buckets and remembers where the window
begins. acquire() is now: drop to the new base, then append exactly what was
produced -- 256 merges and 0.034 ms per frame for 8192 new samples, independent
of the window, and memory bounded by the window (32,767 entries after 50M samples
through a 1M ring, the same as after 10M). A producer that laps the consumer by
more than capacity() between two acquires restarts the window rather than
splicing a gap.

Interface: `level(l) -> std::span<const MinMax>` cannot survive a ring and is
replaced by level_base/level_size/bucket in absolute bucket coordinates, plus
window_base/window_end/drop_before. The *_buckets queries stay in window
coordinates (index 0 = oldest retained sample), so Series keeps its window
semantics for callers; its exactness split now aligns in absolute space
(window_base() + the ceil/floor) because the bucket grid is absolute. The sums
move from per-level prefix sums to per-bucket ones -- the only representation a
sliding front can keep exact -- and StreamSeries::y_bounds() becomes
reduce(0, size()), exact in O(log n + kBucket) per frame instead of a scan of the
window. The pyramid owns the published window now, so there is no second copy of
base/end to keep in step, and gather() emits only buckets that are both complete
and wholly retained.

Tests: test-only merge counters (mirroring PLOT_PYRAMID_COUNT_COVERS, absent from
every non-test build) pin an acquire proportional to production and independent
of capacity, an append cost amortised over 16 live levels, and bounded memory;
brute-force checks pin absolute bucket aggregates across slides, sliding-window
reduce/sum/sumsq over unaligned ranges, and the level-1 gather mapping. The demo
now draws 946 samples/px at 1.03 ms/frame (967 fps) where the saturated window
cost 54.96 ms/frame (18 fps).
2026-09-28 00:05:15 +02:00

348 lines
14 KiB
C++

#include "harness.hpp"
#include <plot/data/array_series.hpp>
#include <plot/data/detail/merge_count.hpp>
#include <plot/data/stream_series.hpp>
#include <algorithm>
#include <array>
#include <atomic>
#include <chrono>
#include <cmath>
#include <random>
#include <thread>
#include <vector>
using plot::data::kBucket;
PLOT_TEST(array_series_bounds_and_gather) {
std::vector<f32> y{1, 5, -2, 3};
plot::data::ArraySeries s(std::move(y), /*x0=*/10.0, /*dx=*/0.5);
PLOT_CHECK(s.size() == 4);
PLOT_CHECK_NEAR(s.x_bounds().min, 10.0, 1e-9);
PLOT_CHECK_NEAR(s.x_bounds().max, 11.5, 1e-9);
PLOT_CHECK_NEAR(s.y_bounds().min, -2.0, 1e-6);
std::vector<f32> out;
s.gather(0, 0, 4, out);
PLOT_CHECK(out.size() == 8); // interleaved x,y
PLOT_CHECK_NEAR(out[0], 10.0, 1e-6);
PLOT_CHECK_NEAR(out[1], 1.0, 1e-6);
PLOT_CHECK_NEAR(out[6], 11.5, 1e-6);
}
PLOT_TEST(stream_series_acquire_publishes_snapshot) {
plot::data::StreamSeries s(1 << 12, /*x0=*/0.0, /*dx=*/1e-6);
for (int i = 0; i < 100; ++i) s.push(f32(i));
PLOT_CHECK(s.produced() == 100);
s.acquire();
PLOT_CHECK(s.size() == 100);
// Pyramid must track the published samples.
PLOT_CHECK_NEAR(s.reduce(0, 100).max, 99.0, 1e-4);
for (int i = 100; i < 250; ++i) s.push(f32(i));
s.acquire();
PLOT_CHECK(s.size() == 250);
}
PLOT_TEST(stream_series_drops_oldest_when_full) {
plot::data::StreamSeries s(256, 0.0, 1.0);
for (int i = 0; i < 1000; ++i) s.push(f32(i));
s.acquire();
PLOT_CHECK(s.size() <= 256);
PLOT_CHECK_NEAR(s.y_bounds().max, 999.0, 1e-4);
}
PLOT_TEST(array_series_exact_range_queries_match_bruteforce) {
// The strong test for the exactness split: arbitrary, deliberately
// unaligned ranges must match a brute-force scan of the raw samples.
std::mt19937 rng(7);
std::uniform_real_distribution<float> d(-5.0f, 5.0f);
std::vector<f32> y(4000);
for (auto& v : y) v = d(rng);
plot::data::ArraySeries s(y, 0.0, 1.0);
for (u64 a = 0; a < 4000; a += 173) {
for (u64 b = a + 1; b <= 4000; b += 331) {
float lo = y[a], hi = y[a];
double sum = 0.0, sq = 0.0;
for (u64 i = a; i < b; ++i) {
lo = std::min(lo, y[i]); hi = std::max(hi, y[i]);
sum += y[i]; sq += double(y[i]) * double(y[i]);
}
const plot::Range r = s.reduce(a, b);
PLOT_CHECK_NEAR(r.min, lo, 1e-4);
PLOT_CHECK_NEAR(r.max, hi, 1e-4);
PLOT_CHECK_NEAR(s.sum(a, b), sum, 1e-2);
PLOT_CHECK_NEAR(s.sumsq(a, b), sq, 1e-1);
}
}
}
PLOT_TEST(stream_series_exact_queries_survive_ring_wrap) {
plot::data::StreamSeries s(1u << 10, 0.0, 1.0);
constexpr u64 kPushes = 3000;
constexpr u64 kFirstRetained = kPushes - (1u << 10); // 1976
for (u64 i = 0; i < kPushes; ++i) s.push(f32(i % 97)); // wraps the ring twice
s.acquire();
const u64 n = s.size();
PLOT_CHECK(n == 1u << 10);
PLOT_CHECK_NEAR(s.x0(), 1976.0, 1e-9);
PLOT_CHECK(s.x_bounds().min == s.x0());
PLOT_CHECK(s.pyramid().size() == n);
const plot::Range r = s.reduce(0, n); // retained window, wrapped
PLOT_CHECK_NEAR(r.max, 96.0, 1e-4);
PLOT_CHECK_NEAR(r.min, 0.0, 1e-4);
double sum = 0.0, sumsq = 0.0;
for (u64 i = kFirstRetained; i < kPushes; ++i) {
const double v = static_cast<double>(i % 97);
sum += v;
sumsq += v * v;
}
PLOT_CHECK_NEAR(s.sum(0, n), sum, 1e-2);
PLOT_CHECK_NEAR(s.sumsq(0, n), sumsq, 1e-1);
std::vector<f32> out;
s.gather(0, 0, n, out); // level 0: raw samples
PLOT_CHECK(out.size() == 2 * n);
PLOT_CHECK_NEAR(out[0], s.x0(), 1e-6);
PLOT_CHECK_NEAR(out[1], f32(kFirstRetained % 97), 1e-6);
PLOT_CHECK_NEAR(out[2 * n - 2], s.x0() + f64(n - 1), 1e-6);
}
PLOT_TEST(stream_series_push_and_read_are_race_free) {
// The ring overwrites, so the producer reuses the slot of the snapshot's oldest
// retained sample while the consumer may still be reading it. Relaxed atomic
// slots make that defined; nothing else in the suite has two threads, so this is
// the case ThreadSanitizer actually flags when the slots are plain.
//
// The two flags make the overlap structural, not scheduling luck: the reader
// waits until the producer is already pushing, acknowledges, and only then does
// the producer push the rest -- so the reader's window spans pushes that are in
// flight by construction. The reader then loops until the producer reports done,
// which also makes "at least one read" true by construction instead of by
// winning a race with a fast producer.
plot::data::StreamSeries s(1u << 8, 0.0, 1.0); // one read spans reused slots
constexpr u64 kStride = 512; // pushes before the reader starts
constexpr u64 kPushes = 4'096; // total: a few thousand only
std::atomic<bool> started{false};
std::atomic<bool> reading{false};
std::atomic<bool> done{false};
std::thread producer([&] {
for (u64 i = 0; i < kStride; ++i) s.push(f32(i % 971));
started.store(true, std::memory_order_release);
while (!reading.load(std::memory_order_acquire)) {
}
for (u64 i = kStride; i < kPushes; ++i) s.push(f32(i % 971));
done.store(true, std::memory_order_release);
});
while (!started.load(std::memory_order_acquire)) {
}
reading.store(true, std::memory_order_release);
u64 reads = 0;
bool sizes_ok = true;
bool finished = false;
do {
finished = done.load(std::memory_order_acquire);
s.acquire();
const u64 n = s.size();
if (n > 0) {
(void)s.reduce(0, n);
(void)s.sum(0, n);
(void)s.sumsq(0, n);
std::vector<f32> out;
s.gather(0, 0, n, out); // reads the oldest edge
if (out.size() != 2 * n) sizes_ok = false;
}
++reads;
} while (!finished);
producer.join();
// Checked only after the join: a throw while the producer is joinable would
// std::terminate instead of reporting the failure.
PLOT_CHECK(s.produced() == kPushes);
PLOT_CHECK(reads >= 1);
PLOT_CHECK(sizes_ok);
}
namespace {
/// Buckets the pyramid holds across every level: its footprint, not its window.
u64 pyramid_entries(const plot::data::MinMaxPyramid& p) {
u64 total = 0;
for (u32 l = 0; l < p.level_count(); ++l) total += p.level_size(l);
return total;
}
} // namespace
PLOT_TEST(stream_series_sliding_window_queries_match_bruteforce) {
// After the base has moved many times, an exact query must still match a scan
// of exactly the retained samples. The pyramid's buckets live in absolute
// sample space while the window's base is arbitrary, and that meeting point is
// where an off-by-one hides -- so test the sliding case, not just the static
// ArraySeries one.
constexpr u64 kCapacity = 1u << 14;
constexpr u64 kPushes = 60'000;
const auto value = [](u64 i) { return f32((i * 29u) % 173u) - 86.0f; };
plot::data::StreamSeries s(kCapacity, 0.0, 1e-6);
for (u64 i = 0; i < kPushes; i += 337) {
const u64 n = std::min<u64>(337, kPushes - i);
for (u64 k = 0; k < n; ++k) s.push(value(i + k));
s.acquire(); // the window's base moves on every one of these
}
const u64 n = s.size();
const u64 base = s.produced() - n;
PLOT_CHECK(n == kCapacity);
PLOT_CHECK(base > 2 * kCapacity); // the window really did slide
PLOT_CHECK(s.pyramid().size() == n); // pyramid() indexes the window
PLOT_CHECK(s.pyramid().window_base() == base);
PLOT_CHECK(s.pyramid().window_end() == s.produced());
PLOT_CHECK_NEAR(s.x0(), f64(base) * 1e-6, 1e-12);
PLOT_CHECK(s.x_bounds().min == s.x0());
PLOT_CHECK_NEAR(s.x_bounds().max, f64(base + n - 1) * 1e-6, 1e-9);
// Brute force in window coordinates over the retained samples only.
const auto brute = [&](u64 a, u64 b) {
f64 lo = f64(value(base + a)), hi = lo;
f64 sum = 0.0, sq = 0.0;
for (u64 i = a; i < b; ++i) {
const f64 v = f64(value(base + i));
lo = std::min(lo, v);
hi = std::max(hi, v);
sum += v;
sq += v * v;
}
return std::array<f64, 4>{lo, hi, sum, sq};
};
const u64 starts[] = {0, 1, 63, 64, 65, 1000, 7919, n - 200};
for (const u64 a : starts) {
for (u64 b = a + 1; b <= n; b += 1301) {
const std::array<f64, 4> want = brute(a, b);
const plot::Range r = s.reduce(a, b);
PLOT_CHECK_NEAR(r.min, want[0], 1e-4);
PLOT_CHECK_NEAR(r.max, want[1], 1e-4);
PLOT_CHECK_NEAR(s.sum(a, b), want[2], 1e-2);
PLOT_CHECK_NEAR(s.sumsq(a, b), want[3], 1e-1);
}
}
const std::array<f64, 4> whole = brute(0, n);
PLOT_CHECK_NEAR(s.y_bounds().min, whole[0], 1e-6);
PLOT_CHECK_NEAR(s.y_bounds().max, whole[1], 1e-6);
// The level accessors are in absolute bucket coordinates: a stored bucket must
// aggregate its own absolute span, the sliding window included.
const u64 span = u64{kBucket} << 1;
const u64 lbase = s.pyramid().level_base(1);
const u64 lsize = s.pyramid().level_size(1);
PLOT_CHECK(lsize >= 2);
for (u64 b = lbase; b < lbase + std::min<u64>(lsize, 4); ++b) {
const std::array<f64, 4> want = brute(b * span - base, (b + 1) * span - base);
const plot::data::MinMax m = s.pyramid().bucket(1, b);
PLOT_CHECK_NEAR(m.min, want[0], 1e-6);
PLOT_CHECK_NEAR(m.max, want[1], 1e-6);
}
// level-1 gather: one envelope pair per retained bucket the range overlaps, x
// at the bucket's own first and last sample. This is the mapping the renderer
// draws from.
std::vector<f32> out;
s.gather(1, 0, n, out);
const u64 first = std::max(base / span, lbase);
const u64 last = std::min((base + n - 1) / span + 1, lbase + lsize);
PLOT_CHECK(out.size() == 4 * (last - first));
for (u64 bi = first; bi < last; ++bi) {
const std::array<f64, 4> want = brute(bi * span - base, (bi + 1) * span - base);
const plot::usize k = static_cast<plot::usize>(bi - first);
const u64 first_sample = bi * span - base;
const u64 last_sample = std::min((bi + 1) * span, base + n) - 1 - base;
// x is in data coordinates: x0() is the retained window's first sample, so
// window index j plots at (base + j) * dx.
PLOT_CHECK_NEAR(out[4 * k], f32(f64(base + first_sample) * 1e-6), 1e-9);
PLOT_CHECK_NEAR(out[4 * k + 1], want[0], 1e-6);
PLOT_CHECK_NEAR(out[4 * k + 2], f32(f64(base + last_sample) * 1e-6), 1e-9);
PLOT_CHECK_NEAR(out[4 * k + 3], want[1], 1e-6);
}
}
PLOT_TEST(stream_series_steady_state_acquire_is_proportional_to_produced) {
// The defect this pins: a saturated ring whose base moved every frame used to
// rebuild the whole window, costing O(capacity * live_levels) merges per
// acquire. It must cost O(samples produced since the last acquire) instead, so
// the same production through a 16x larger window must cost the same.
const auto merges_for = [](u64 capacity) {
plot::data::StreamSeries s(capacity, 0.0, 1.0);
constexpr u64 kSaturate = 1u << 20;
for (u64 i = 0; i < kSaturate; ++i) s.push(f32(i % 977));
s.acquire();
PLOT_CHECK(s.size() == capacity);
constexpr u64 kFrames = 64;
constexpr u64 kFrame = 1u << 13; // 8192 samples: 1 MS/s at ~122 fps
plot::data::detail::merge_count().store(0, std::memory_order_relaxed);
for (u64 f = 0; f < kFrames; ++f) {
for (u64 k = 0; k < kFrame; ++k) s.push(f32((kSaturate + f * kFrame + k) % 977));
s.acquire();
}
return plot::data::detail::merge_count().load(std::memory_order_relaxed);
};
constexpr u64 kProduced = 64 * (1u << 13);
const u64 small = merges_for(1u << 16); // 64k-sample window
const u64 big = merges_for(1u << 20); // 1M-sample window: 16x to rebuild
// ~kProduced/32 in practice; rebuilding the 1M window alone would merge
// kCapacity/64 * live_levels ~ 245'000 buckets per acquire.
PLOT_CHECK(small <= kProduced / 16);
PLOT_CHECK(big <= kProduced / 16);
PLOT_CHECK(big <= 2 * small + kBucket);
}
PLOT_TEST(stream_series_steady_state_acquire_is_fast) {
// Wall-clock backstop for the merge count above: rebuilding the 1M-sample
// window measured ~44 ms, so 128 such frames would need ~5.6 s. The sliding
// path does the same work in a few milliseconds; the bound is two orders of
// magnitude clear of both, so it is not a thin timing margin.
plot::data::StreamSeries s(1u << 20, 0.0, 1.0);
constexpr u64 kSaturate = 1u << 20;
for (u64 i = 0; i < kSaturate; ++i) s.push(f32(i % 977));
s.acquire();
constexpr u64 kFrames = 128;
constexpr u64 kFrame = 1u << 13;
const auto t0 = std::chrono::steady_clock::now();
for (u64 f = 0; f < kFrames; ++f) {
for (u64 k = 0; k < kFrame; ++k) s.push(f32((kSaturate + f * kFrame + k) % 977));
s.acquire();
}
const f64 ms =
std::chrono::duration<f64>(std::chrono::steady_clock::now() - t0).count() * 1.0e3;
PLOT_CHECK(s.size() == 1u << 20);
PLOT_CHECK(ms < 250.0);
}
PLOT_TEST(stream_series_pyramid_memory_is_bounded_by_the_window) {
// 50M samples through a 1M-sample ring: the pyramid's footprint must track the
// retained window (n/64 level-0 buckets plus the levels over them), not the
// samples ever pushed.
constexpr u64 kCapacity = 1u << 20;
constexpr u64 kTotal = 50u * (1u << 20);
constexpr u64 kChunk = 1u << 19;
plot::data::StreamSeries s(kCapacity, 0.0, 1.0);
u64 entries_after_10m = 0;
for (u64 p = 0; p < kTotal; p += kChunk) {
for (u64 i = 0; i < kChunk; ++i) s.push(f32((p + i) % 251));
s.acquire();
if (p + kChunk == 10u * (1u << 20)) entries_after_10m = pyramid_entries(s.pyramid());
}
const u64 entries = pyramid_entries(s.pyramid());
const u64 bound = 2 * (kCapacity / kBucket) + kBucket;
PLOT_CHECK(s.produced() == kTotal);
PLOT_CHECK(s.size() == kCapacity);
PLOT_CHECK(entries <= bound);
// 5x the samples pushed, same footprint: levels deeper than one chunk can
// differ by a bucket each, hence the slack.
PLOT_CHECK(entries <= entries_after_10m + kBucket);
}