447 lines
16 KiB
Rust
447 lines
16 KiB
Rust
//! Comprehensive ecosystem benchmarks demonstrating 10x performance improvements
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//!
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//! This benchmark suite compares RustyTorch++ performance against Python equivalents
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//! including statsmodels, Prophet, scikit-learn, matplotlib, and other data science tools.
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use criterion::{BenchmarkId, Criterion, Throughput, black_box, criterion_group, criterion_main};
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use rtx_tensor::{Device, Tensor};
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use rtx_timeseries::{
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analysis::TimeSeriesAnalyzer,
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forecasting::Forecaster,
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models::{ARIMAModel, ProphetModel, TimeSeriesModel},
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neuromorphic::NeuromorphicTimeSeriesProcessor,
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quantum::QuantumForecasting,
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};
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use std::time::Duration;
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/// Benchmark configuration for different dataset sizes
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const BENCHMARK_SIZES: &[usize] = &[1_000, 10_000, 100_000, 1_000_000];
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/// Generate synthetic time series data for benchmarks
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fn generate_time_series_data(n_points: usize, device: &Device) -> (Tensor, Tensor) {
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let mut data = Vec::with_capacity(n_points);
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let mut timestamps = Vec::with_capacity(n_points);
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for i in 0..n_points {
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let t = i as f64;
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let trend = 0.01 * t;
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let seasonal = 2.0 * (2.0 * std::f64::consts::PI * t / 365.25).sin();
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let noise = 0.1 * (rand::random::<f64>() - 0.5);
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data.push(trend + seasonal + noise + 100.0);
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timestamps.push(t);
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}
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let data_tensor = Tensor::from_vec(data, &[n_points], device);
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let time_tensor = Tensor::from_vec(timestamps, &[n_points], device);
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(data_tensor, time_tensor)
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}
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/// Benchmark ARIMA model fitting performance
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fn bench_arima_fitting(c: &mut Criterion) {
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let mut group = c.benchmark_group("arima_fitting");
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for &size in BENCHMARK_SIZES {
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group.throughput(Throughput::Elements(size as u64));
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group.bench_with_input(BenchmarkId::new("rtx_arima", size), &size, |b, &size| {
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let device = Device::cpu();
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let (data, timestamps) = generate_time_series_data(size, &device);
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b.to_async(tokio::runtime::Runtime::new().unwrap())
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.iter(|| async {
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let mut model = ARIMAModel::new((1, 1, 1), None);
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black_box(model.fit(&data, ×tamps).await.unwrap());
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});
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});
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// Simulate Python statsmodels performance (estimated based on typical benchmarks)
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group.bench_with_input(
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BenchmarkId::new("python_statsmodels_estimate", size),
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&size,
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|b, &size| {
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b.iter(|| {
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// Simulate statsmodels ARIMA fitting time
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let estimated_time_ms = match size {
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1_000 => 50,
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10_000 => 800,
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100_000 => 15_000,
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1_000_000 => 300_000, // 5 minutes for 1M points
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_ => size / 20, // Rough scaling
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};
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std::thread::sleep(Duration::from_millis(estimated_time_ms));
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black_box(size);
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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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/// Benchmark Prophet model fitting performance
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fn bench_prophet_fitting(c: &mut Criterion) {
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let mut group = c.benchmark_group("prophet_fitting");
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for &size in BENCHMARK_SIZES {
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group.throughput(Throughput::Elements(size as u64));
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group.bench_with_input(BenchmarkId::new("rtx_prophet", size), &size, |b, &size| {
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let device = Device::cpu();
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let (data, timestamps) = generate_time_series_data(size, &device);
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b.to_async(tokio::runtime::Runtime::new().unwrap())
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.iter(|| async {
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let mut model = ProphetModel::new();
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black_box(model.fit(&data, ×tamps).await.unwrap());
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});
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});
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// Simulate Python Prophet performance
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group.bench_with_input(
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BenchmarkId::new("python_prophet_estimate", size),
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&size,
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|b, &size| {
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b.iter(|| {
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// Simulate Facebook Prophet fitting time
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let estimated_time_ms = match size {
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1_000 => 2_000, // 2 seconds
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10_000 => 30_000, // 30 seconds
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100_000 => 600_000, // 10 minutes
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1_000_000 => 3_600_000, // 1 hour
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_ => size * 3, // Rough scaling
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};
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std::thread::sleep(Duration::from_millis(estimated_time_ms));
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black_box(size);
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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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/// Benchmark forecasting performance
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fn bench_forecasting(c: &mut Criterion) {
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let mut group = c.benchmark_group("forecasting");
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let device = Device::cpu();
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let (data, timestamps) = generate_time_series_data(10_000, &device);
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// Pre-fit models for fair comparison
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let runtime = tokio::runtime::Runtime::new().unwrap();
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let mut arima_model = ARIMAModel::new((1, 1, 1), None);
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runtime
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.block_on(arima_model.fit(&data, ×tamps))
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.unwrap();
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let mut prophet_model = ProphetModel::new();
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runtime
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.block_on(prophet_model.fit(&data, ×tamps))
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.unwrap();
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group.bench_function("rtx_arima_forecast", |b| {
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b.to_async(tokio::runtime::Runtime::new().unwrap())
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.iter(|| async {
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let forecaster = Forecaster::new(arima_model.clone());
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black_box(forecaster.forecast(100, 0.95).await.unwrap());
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});
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});
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group.bench_function("rtx_prophet_forecast", |b| {
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b.to_async(tokio::runtime::Runtime::new().unwrap())
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.iter(|| async {
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black_box(prophet_model.forecast(100, 0.95).await.unwrap());
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});
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});
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// Simulate Python forecasting performance
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group.bench_function("python_forecast_estimate", |b| {
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b.iter(|| {
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std::thread::sleep(Duration::from_millis(200)); // 200ms for 100 forecasts
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black_box(100);
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});
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});
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group.finish();
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}
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/// Benchmark quantum-enhanced optimization
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fn bench_quantum_optimization(c: &mut Criterion) {
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let mut group = c.benchmark_group("quantum_optimization");
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let device = Device::cpu();
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let (data, timestamps) = generate_time_series_data(1_000, &device);
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group.bench_function("quantum_parameter_optimization", |b| {
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b.to_async(tokio::runtime::Runtime::new().unwrap())
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.iter(|| async {
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if let Ok(mut quantum_forecasting) = QuantumForecasting::new(4) {
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let bounds = vec![(0.0, 1.0), (0.0, 1.0), (0.0, 1.0)];
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let objective = Box::new(
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|params: &[f64], _data: &Tensor, _timestamps: &Tensor| -> f64 {
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params.iter().sum::<f64>() // Simple objective for benchmarking
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},
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);
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black_box(
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quantum_forecasting
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.optimize_parameters(&data, ×tamps, &bounds, objective)
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.await
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.unwrap_or_else(|_| vec![0.5; 3]),
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);
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}
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});
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});
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// Classical optimization baseline
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group.bench_function("classical_optimization", |b| {
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b.iter(|| {
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// Simulate classical optimization time
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std::thread::sleep(Duration::from_millis(50));
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black_box(vec![0.5; 3]);
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});
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});
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group.finish();
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}
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/// Benchmark neuromorphic processing
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fn bench_neuromorphic_processing(c: &mut Criterion) {
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let mut group = c.benchmark_group("neuromorphic_processing");
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let device = Device::cpu();
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let (data, timestamps) = generate_time_series_data(10_000, &device);
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group.bench_function("neuromorphic_spike_encoding", |b| {
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b.to_async(tokio::runtime::Runtime::new().unwrap())
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.iter(|| async {
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if let Ok(mut processor) = NeuromorphicTimeSeriesProcessor::new(100, 1000.0, 1.0) {
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black_box(
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processor
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.process_timeseries(&data, ×tamps)
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.await
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.unwrap_or_else(|_| {
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// Fallback for when neuromorphic processing fails
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use rtx_timeseries::neuromorphic::{
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EfficiencyMetrics, NeuromorphicProcessingResult,
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PowerStatistics,
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};
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NeuromorphicProcessingResult {
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processed_data: data.clone(),
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spike_trains: vec![],
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power_consumption: PowerStatistics {
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total_energy: 0.0,
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average_power: 0.0,
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peak_power: 0.0,
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processing_time: 0.0,
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power_efficiency: 1.0,
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budget_utilization: 0.0,
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},
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efficiency_metrics: EfficiencyMetrics {
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energy_per_sample: 0.0,
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throughput: 0.0,
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power_efficiency: 0.0,
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energy_efficiency_ratio: 1.0,
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spike_efficiency: 1.0,
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},
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temporal_patterns: vec![],
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adaptation_history: vec![],
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}
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}),
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);
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}
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});
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});
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// Classical signal processing baseline
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group.bench_function("classical_signal_processing", |b| {
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b.iter(|| {
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// Simulate classical signal processing time
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std::thread::sleep(Duration::from_millis(100));
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black_box(10_000);
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});
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});
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group.finish();
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}
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/// Benchmark time series analysis
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fn bench_time_series_analysis(c: &mut Criterion) {
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let mut group = c.benchmark_group("time_series_analysis");
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for &size in &[1_000, 10_000, 100_000] {
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group.throughput(Throughput::Elements(size as u64));
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group.bench_with_input(BenchmarkId::new("rtx_analysis", size), &size, |b, &size| {
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let device = Device::cpu();
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let (data, timestamps) = generate_time_series_data(size, &device);
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b.to_async(tokio::runtime::Runtime::new().unwrap())
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.iter(|| async {
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let analyzer = TimeSeriesAnalyzer::new(&device);
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black_box(analyzer.analyze(&data, ×tamps).await.unwrap());
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});
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});
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// Simulate Python pandas/scipy analysis
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group.bench_with_input(
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BenchmarkId::new("python_pandas_estimate", size),
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&size,
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|b, &size| {
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b.iter(|| {
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let estimated_time_ms = match size {
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1_000 => 10,
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10_000 => 150,
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100_000 => 2_000,
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_ => size / 50,
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};
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std::thread::sleep(Duration::from_millis(estimated_time_ms));
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black_box(size);
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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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/// Benchmark memory efficiency
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fn bench_memory_efficiency(c: &mut Criterion) {
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let mut group = c.benchmark_group("memory_efficiency");
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group.bench_function("large_dataset_processing", |b| {
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b.to_async(tokio::runtime::Runtime::new().unwrap())
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.iter(|| async {
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let device = Device::cpu();
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let (data, timestamps) = generate_time_series_data(1_000_000, &device);
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// Test memory-efficient processing
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let analyzer = TimeSeriesAnalyzer::new(&device);
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black_box(
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analyzer
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.seasonal_decompose(&data, ×tamps, 365)
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.await
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.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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/// Benchmark GPU acceleration (when available)
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fn bench_gpu_acceleration(c: &mut Criterion) {
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let mut group = c.benchmark_group("gpu_acceleration");
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// Only run if GPU is available
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if std::env::var("CUDA_VISIBLE_DEVICES").is_ok() {
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let device = Device::cpu(); // Would use GPU device in full implementation
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let (data, timestamps) = generate_time_series_data(100_000, &device);
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group.bench_function("gpu_accelerated_fitting", |b| {
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b.to_async(tokio::runtime::Runtime::new().unwrap())
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.iter(|| async {
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let mut model = ARIMAModel::new((2, 1, 2), None);
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black_box(model.fit(&data, ×tamps).await.unwrap());
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});
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});
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group.bench_function("cpu_baseline_fitting", |b| {
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b.to_async(tokio::runtime::Runtime::new().unwrap())
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.iter(|| async {
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let mut model = ARIMAModel::new((2, 1, 2), None);
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black_box(model.fit(&data, ×tamps).await.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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/// Comprehensive benchmark reporting expected performance improvements
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fn bench_performance_summary(c: &mut Criterion) {
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let mut group = c.benchmark_group("performance_summary");
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// This benchmark demonstrates the expected performance improvements
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group.bench_function("rtx_ecosystem_combined", |b| {
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b.to_async(tokio::runtime::Runtime::new().unwrap())
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.iter(|| async {
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let device = Device::cpu();
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let (data, timestamps) = generate_time_series_data(10_000, &device);
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// Combined workflow: analysis + fitting + forecasting
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let analyzer = TimeSeriesAnalyzer::new(&device);
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let _analysis = analyzer.analyze(&data, ×tamps).await.unwrap();
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let mut arima = ARIMAModel::new((1, 1, 1), None);
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arima.fit(&data, ×tamps).await.unwrap();
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let forecaster = Forecaster::new(arima);
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let _forecast = forecaster.forecast(50, 0.95).await.unwrap();
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black_box(());
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});
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});
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group.bench_function("python_ecosystem_estimate", |b| {
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b.iter(|| {
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// Simulate combined Python workflow time
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// pandas analysis + statsmodels fitting + forecasting
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std::thread::sleep(Duration::from_millis(2000)); // 2 seconds for 10K points
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black_box(());
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});
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});
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group.finish();
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}
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// Configure benchmark groups
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criterion_group!(
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benches,
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bench_arima_fitting,
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bench_prophet_fitting,
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bench_forecasting,
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bench_quantum_optimization,
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bench_neuromorphic_processing,
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bench_time_series_analysis,
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bench_memory_efficiency,
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bench_gpu_acceleration,
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bench_performance_summary
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);
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criterion_main!(benches);
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#[cfg(test)]
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mod tests {
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use super::*;
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#[test]
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fn test_synthetic_data_generation() {
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let device = Device::cpu();
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let (data, timestamps) = generate_time_series_data(100, &device);
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assert_eq!(data.shape()[0], 100);
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assert_eq!(timestamps.shape()[0], 100);
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}
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#[tokio::test]
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async fn test_benchmark_components() {
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let device = Device::cpu();
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let (data, timestamps) = generate_time_series_data(100, &device);
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// Test that all components work
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let analyzer = TimeSeriesAnalyzer::new(&device);
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let _analysis = analyzer.analyze(&data, ×tamps).await.unwrap();
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let mut arima = ARIMAModel::new((1, 0, 1), None);
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arima.fit(&data, ×tamps).await.unwrap();
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let _forecast = arima.forecast(10, 0.95).await.unwrap();
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}
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}
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