Squashed 'vendor/ruvector/' content from commit b64c2172
git-subtree-dir: vendor/ruvector git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900
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ruv
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// Neural Field Benchmark - Memory-mapped operations performance
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use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion, Throughput};
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use demand_paged_cognition::*;
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use tempfile::NamedTempFile;
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fn bench_hash_address(c: &mut Criterion) {
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let temp = NamedTempFile::new().unwrap();
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let field = MmapNeuralField::new(
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temp.path(),
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1024 * 1024 * 1024, // 1 GB
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Some(4 * 1024 * 1024), // 4 MB pages
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)
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.unwrap();
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let mut group = c.benchmark_group("hash_address");
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for size in [4, 16, 64, 256, 1024].iter() {
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group.throughput(Throughput::Elements(*size as u64));
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group.bench_with_input(BenchmarkId::from_parameter(size), size, |b, &size| {
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let concept = vec![0.1f32; size];
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b.iter(|| field.hash_address(black_box(&concept)));
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});
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}
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group.finish();
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}
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fn bench_read_write(c: &mut Criterion) {
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let temp = NamedTempFile::new().unwrap();
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let field = MmapNeuralField::new(
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temp.path(),
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1024 * 1024 * 1024, // 1 GB
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Some(4 * 1024 * 1024),
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)
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.unwrap();
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let mut group = c.benchmark_group("read_write");
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for size in [64, 256, 1024, 4096].iter() {
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group.throughput(Throughput::Bytes((*size * 4) as u64)); // f32 = 4 bytes
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// Write benchmark
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group.bench_with_input(BenchmarkId::new("write", size), size, |b, &size| {
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let data = vec![1.0f32; size];
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b.iter(|| field.write(black_box(0), black_box(&data)).unwrap());
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});
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// Read benchmark
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field.write(0, &vec![1.0f32; *size]).unwrap();
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group.bench_with_input(BenchmarkId::new("read", size), size, |b, &size| {
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b.iter(|| field.read(black_box(0), black_box(size)).unwrap());
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});
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}
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group.finish();
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}
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fn bench_lazy_layer_forward(c: &mut Criterion) {
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let temp = NamedTempFile::new().unwrap();
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let storage = std::sync::Arc::new(
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MmapNeuralField::new(temp.path(), 1024 * 1024 * 1024, Some(4096)).unwrap(),
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);
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let mut group = c.benchmark_group("lazy_layer");
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for (input_dim, output_dim) in [(10, 10), (100, 100), (256, 256), (512, 512)].iter() {
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// Initialize weights
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let weights = vec![0.1f32; input_dim * output_dim];
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let bias = vec![0.01f32; *output_dim];
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storage.write(0, &weights).unwrap();
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storage.write((weights.len() * 4) as u64, &bias).unwrap();
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let mut layer = LazyLayer::new(
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0,
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(weights.len() * 4) as u64,
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*input_dim,
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*output_dim,
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storage.clone(),
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);
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group.throughput(Throughput::Elements((*input_dim * *output_dim) as u64));
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group.bench_with_input(
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BenchmarkId::new("forward", format!("{}x{}", input_dim, output_dim)),
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&(*input_dim, *output_dim),
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|b, &(input_dim, _)| {
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let input = vec![1.0f32; input_dim];
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b.iter(|| layer.forward(black_box(&input)).unwrap());
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},
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);
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}
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group.finish();
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}
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fn bench_tiered_memory(c: &mut Criterion) {
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let mut group = c.benchmark_group("tiered_memory");
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// Promotion benchmark
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group.bench_function("promote_l4_to_l1", |b| {
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b.iter_with_setup(
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|| {
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let mut memory = TieredMemory::new();
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let page = Page::new(1, vec![1.0; 1024], Tier::L4Hdd);
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memory.insert(page).unwrap();
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memory
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},
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|mut memory| memory.promote(1, Tier::L1Dram, "bench").unwrap(),
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);
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});
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// Load benchmark (includes promotion)
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group.bench_function("load_page", |b| {
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b.iter_with_setup(
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|| {
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let mut memory = TieredMemory::new();
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let page = Page::new(1, vec![1.0; 1024], Tier::L4Hdd);
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memory.insert(page).unwrap();
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memory
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},
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|mut memory| memory.load(1).unwrap(),
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);
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});
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group.finish();
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}
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fn bench_prefetch_prediction(c: &mut Criterion) {
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let mut group = c.benchmark_group("prefetch");
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// Hoeffding Tree prediction
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group.bench_function("hoeffding_predict", |b| {
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let predictor = HoeffdingTreePredictor::new();
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// Train with some data
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for i in 0..100 {
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let page = (i % 10) as u64;
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let features = AccessFeatures::new(page);
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predictor.update(page, &features);
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}
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let features = AccessFeatures::new(5);
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b.iter(|| predictor.predict(black_box(&features), black_box(10)));
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});
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// Markov prediction
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group.bench_function("markov_predict", |b| {
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let predictor = MarkovPredictor::new();
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// Build transition pattern
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for _ in 0..10 {
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predictor.update(1, 2);
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predictor.update(2, 3);
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predictor.update(3, 1);
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}
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b.iter(|| predictor.predict(black_box(1), black_box(10)));
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});
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// Coordinator
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group.bench_function("coordinator_predict", |b| {
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let coordinator = PrefetchCoordinator::new();
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let context = vec![0.1, 0.2, 0.3];
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// Record some history
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for i in 0..50 {
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coordinator.record_access(i, &context);
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}
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b.iter(|| coordinator.predict_and_queue(black_box(50), black_box(&context), black_box(5)));
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});
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group.finish();
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}
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fn bench_dpnc_system(c: &mut Criterion) {
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let mut group = c.benchmark_group("dpnc_system");
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group.sample_size(50); // Reduce sample size for expensive operations
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group.bench_function("full_query", |b| {
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b.iter_with_setup(
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|| {
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let temp = NamedTempFile::new().unwrap();
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let config = DPNCConfig::default();
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DPNC::new(temp.path(), config).unwrap()
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},
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|mut dpnc| {
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let concept = vec![0.1, 0.2, 0.3, 0.4];
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dpnc.query(black_box(&concept)).unwrap()
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},
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);
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});
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group.finish();
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}
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criterion_group!(
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benches,
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bench_hash_address,
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bench_read_write,
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bench_lazy_layer_forward,
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bench_tiered_memory,
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bench_prefetch_prediction,
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bench_dpnc_system
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);
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criterion_main!(benches);
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@@ -0,0 +1,139 @@
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// Prefetch Prediction Benchmark - Accuracy and performance metrics
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use criterion::{black_box, criterion_group, criterion_main, Criterion, BenchmarkId};
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use demand_paged_cognition::*;
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fn bench_prefetch_accuracy(c: &mut Criterion) {
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let mut group = c.benchmark_group("prefetch_accuracy");
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// Sequential pattern
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group.bench_function("sequential_pattern", |b| {
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b.iter_with_setup(
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|| PrefetchCoordinator::new(),
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|coordinator| {
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let context = vec![0.1, 0.2, 0.3];
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// Build sequential pattern
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for i in 0..100 {
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coordinator.record_access(i, &context);
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}
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// Predict next
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let predictions = coordinator.predict_and_queue(100, &context, 10);
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black_box(predictions)
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},
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);
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});
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// Random pattern
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group.bench_function("random_pattern", |b| {
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b.iter_with_setup(
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|| {
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use std::collections::hash_map::DefaultHasher;
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use std::hash::{Hash, Hasher};
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let coordinator = PrefetchCoordinator::new();
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let context = vec![0.1, 0.2, 0.3];
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// Build pseudo-random pattern
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for i in 0..100 {
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let mut hasher = DefaultHasher::new();
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i.hash(&mut hasher);
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let page = (hasher.finish() % 1000) as u64;
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coordinator.record_access(page, &context);
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}
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coordinator
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},
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|coordinator| {
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let context = vec![0.1, 0.2, 0.3];
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let predictions = coordinator.predict_and_queue(500, &context, 10);
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black_box(predictions)
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},
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);
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});
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// Cyclic pattern
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group.bench_function("cyclic_pattern", |b| {
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b.iter_with_setup(
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|| {
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let coordinator = PrefetchCoordinator::new();
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let context = vec![0.1, 0.2, 0.3];
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// Build cyclic pattern: 1->2->3->4->1
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for _ in 0..25 {
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coordinator.record_access(1, &context);
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coordinator.record_access(2, &context);
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coordinator.record_access(3, &context);
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coordinator.record_access(4, &context);
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}
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coordinator
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},
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|coordinator| {
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let context = vec![0.1, 0.2, 0.3];
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let predictions = coordinator.predict_and_queue(4, &context, 5);
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black_box(predictions)
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},
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);
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});
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group.finish();
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}
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fn bench_streaming_learning(c: &mut Criterion) {
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let mut group = c.benchmark_group("streaming_learning");
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// Hoeffding Tree update
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group.bench_function("hoeffding_update", |b| {
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let predictor = HoeffdingTreePredictor::new();
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let features = AccessFeatures::new(42);
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b.iter(|| {
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predictor.update(black_box(42), black_box(&features))
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});
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});
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// Markov update
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group.bench_function("markov_update", |b| {
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let predictor = MarkovPredictor::new();
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b.iter(|| {
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predictor.update(black_box(1), black_box(2))
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});
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});
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group.finish();
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}
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fn bench_feature_extraction(c: &mut Criterion) {
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let mut group = c.benchmark_group("feature_extraction");
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for history_len in [10, 50, 100].iter() {
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group.bench_with_input(
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BenchmarkId::from_parameter(history_len),
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history_len,
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|b, &history_len| {
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let history: Vec<u64> = (0..history_len).collect();
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let context = vec![0.1, 0.2, 0.3, 0.4, 0.5];
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b.iter(|| {
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let features = AccessFeatures::from_history(
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black_box(&history),
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black_box(&context),
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);
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black_box(features.to_vector())
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});
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},
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);
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}
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group.finish();
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}
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criterion_group!(
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benches,
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bench_prefetch_accuracy,
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bench_streaming_learning,
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bench_feature_extraction
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);
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criterion_main!(benches);
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