Squashed 'vendor/ruvector/' content from commit b64c2172

git-subtree-dir: vendor/ruvector
git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900

examples/exo-ai-2025/research/08-meta-simulation-consciousness/benches/meta_sim_benchmarks.rs

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+use criterion::{black_box, criterion_group, criterion_main, BenchmarkId, Criterion};
+use meta_sim_consciousness::*;
+
+fn bench_closed_form_phi(c: &mut Criterion) {
+    let mut group = c.benchmark_group("closed_form_phi");
+
+    for n in [4, 6, 8, 10, 12].iter() {
+        group.bench_with_input(BenchmarkId::from_parameter(n), n, |b, &n| {
+            // Create cycle network
+            let mut adj = vec![vec![0.0; n]; n];
+            for i in 0..n {
+                adj[i][(i + 1) % n] = 1.0;
+            }
+            let nodes: Vec<u64> = (0..n as u64).collect();
+
+            let calculator = ClosedFormPhi::default();
+
+            b.iter(|| black_box(calculator.compute_phi_ergodic(&adj, &nodes)));
+        });
+    }
+
+    group.finish();
+}
+
+fn bench_cei_computation(c: &mut Criterion) {
+    let mut group = c.benchmark_group("cei_computation");
+
+    for n in [4, 6, 8, 10].iter() {
+        group.bench_with_input(BenchmarkId::from_parameter(n), n, |b, &n| {
+            let mut adj = vec![vec![0.0; n]; n];
+            for i in 0..n {
+                adj[i][(i + 1) % n] = 1.0;
+            }
+
+            let calculator = ClosedFormPhi::default();
+
+            b.iter(|| black_box(calculator.compute_cei(&adj, 1.0)));
+        });
+    }
+
+    group.finish();
+}
+
+fn bench_ergodicity_test(c: &mut Criterion) {
+    let mut group = c.benchmark_group("ergodicity_test");
+
+    for n in [4, 6, 8].iter() {
+        group.bench_with_input(BenchmarkId::from_parameter(n), n, |b, &n| {
+            let mut transition = vec![vec![0.0; n]; n];
+            for i in 0..n {
+                transition[i][(i + 1) % n] = 1.0;
+            }
+
+            let analyzer = ErgodicityAnalyzer::default();
+            let observable = |state: &[f64]| state[0];
+
+            b.iter(|| black_box(analyzer.test_ergodicity(&transition, observable)));
+        });
+    }
+
+    group.finish();
+}
+
+fn bench_hierarchical_phi(c: &mut Criterion) {
+    let mut group = c.benchmark_group("hierarchical_phi");
+
+    group.bench_function("batch_64_depth_3", |b| {
+        let param_space = ConsciousnessParameterSpace::new(4);
+        let networks: Vec<_> = param_space
+            .generate_networks()
+            .into_iter()
+            .take(64)
+            .collect();
+
+        b.iter(|| {
+            let mut batcher = HierarchicalPhiBatcher::new(64, 3, 4);
+            black_box(batcher.process_hierarchical_batch(&networks))
+        });
+    });
+
+    group.finish();
+}
+
+fn bench_meta_simulation(c: &mut Criterion) {
+    let mut group = c.benchmark_group("meta_simulation");
+
+    group.bench_function("small_config", |b| {
+        let config = MetaSimConfig {
+            network_size: 4,
+            hierarchy_depth: 2,
+            batch_size: 8,
+            num_cores: 1,
+            simd_width: 1,
+            bit_width: 1,
+        };
+
+        b.iter(|| {
+            let mut simulator = MetaConsciousnessSimulator::new(config.clone());
+            black_box(simulator.run_meta_simulation())
+        });
+    });
+
+    group.finish();
+}
+
+criterion_group!(
+    benches,
+    bench_closed_form_phi,
+    bench_cei_computation,
+    bench_ergodicity_test,
+    bench_hierarchical_phi,
+    bench_meta_simulation
+);
+criterion_main!(benches);