453 lines
13 KiB
Rust
453 lines
13 KiB
Rust
//! Comprehensive benchmarks for RTX Science
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//!
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//! This benchmark suite evaluates the performance of scientific computing
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//! operations including PINNs, molecular simulations, and numerical solvers.
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use criterion::{BenchmarkId, Criterion, Throughput, criterion_group, criterion_main};
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use rtx_science::prelude::*;
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use rtx_tensor::{Device, Tensor};
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use std::time::Duration;
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/// PINN training benchmark
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fn bench_pinn_training(c: &mut Criterion) {
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let rt = tokio::runtime::Runtime::new().unwrap();
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let mut group = c.benchmark_group("PINN Training");
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group.measurement_time(Duration::from_secs(10));
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for &size in &[32, 64, 128] {
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group.throughput(Throughput::Elements(size as u64));
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group.bench_with_input(
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BenchmarkId::new("heat_equation", size),
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&size,
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|b, &size| {
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b.to_async(&rt)
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.iter(|| async { bench_heat_equation_pinn(size).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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/// Molecular property prediction benchmark
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fn bench_molecular_prediction(c: &mut Criterion) {
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let rt = tokio::runtime::Runtime::new().unwrap();
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let mut group = c.benchmark_group("Molecular Prediction");
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for &batch_size in &[16, 32, 64] {
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group.bench_with_input(
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BenchmarkId::new("gnn_forward", batch_size),
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&batch_size,
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|b, &batch_size| {
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b.to_async(&rt)
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.iter(|| async { bench_molecular_gnn(batch_size).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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/// Conservation law validation benchmark
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fn bench_conservation_laws(c: &mut Criterion) {
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let rt = tokio::runtime::Runtime::new().unwrap();
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c.bench_function("mass_conservation", |b| {
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b.to_async(&rt)
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.iter(|| async { bench_mass_conservation().await.unwrap() });
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});
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c.bench_function("energy_conservation", |b| {
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b.to_async(&rt)
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.iter(|| async { bench_energy_conservation().await.unwrap() });
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});
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}
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/// Scientific computing primitives benchmark
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fn bench_scientific_computing(c: &mut Criterion) {
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let rt = tokio::runtime::Runtime::new().unwrap();
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let mut group = c.benchmark_group("Scientific Computing");
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for &matrix_size in &[256, 512, 1024] {
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group.throughput(Throughput::Elements((matrix_size * matrix_size) as u64));
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group.bench_with_input(
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BenchmarkId::new("matrix_solve", matrix_size),
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&matrix_size,
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|b, &size| {
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b.to_async(&rt)
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.iter(|| async { bench_linear_solve(size).await.unwrap() });
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},
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);
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group.bench_with_input(
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BenchmarkId::new("fft_transform", matrix_size),
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&matrix_size,
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|b, &size| {
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b.to_async(&rt)
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.iter(|| async { bench_fft_transform(size).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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/// GPU vs CPU performance comparison
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fn bench_device_comparison(c: &mut Criterion) {
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let rt = tokio::runtime::Runtime::new().unwrap();
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let mut group = c.benchmark_group("Device Comparison");
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// CPU benchmark
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group.bench_function("cpu_pinn", |b| {
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b.to_async(&rt).iter(|| async {
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let device = Device::cpu();
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bench_device_pinn(&device).await.unwrap()
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});
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});
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// GPU benchmark (if available)
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if Device::cuda(0).is_ok() {
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group.bench_function("gpu_pinn", |b| {
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b.to_async(&rt).iter(|| async {
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let device = Device::cuda(0).unwrap();
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bench_device_pinn(&device).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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/// Molecular dataset processing benchmark
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fn bench_molecular_datasets(c: &mut Criterion) {
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let rt = tokio::runtime::Runtime::new().unwrap();
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let mut group = c.benchmark_group("Molecular Datasets");
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group.bench_function("dataset_loading", |b| {
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b.to_async(&rt)
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.iter(|| async { bench_dataset_loading().await.unwrap() });
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});
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group.bench_function("feature_extraction", |b| {
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b.to_async(&rt)
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.iter(|| async { bench_feature_extraction().await.unwrap() });
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});
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group.finish();
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}
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/// Benchmark helper functions
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async fn bench_heat_equation_pinn(hidden_size: usize) -> Result<f64> {
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let device = Device::cpu();
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let heat_eq = HeatEquation::new(0.1);
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let pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, hidden_size, hidden_size, 1])
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.physics_loss(Box::new(heat_eq))
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.build()?;
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// Benchmark forward pass
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let inputs = vec![(0.5, 0.1), (0.3, 0.2), (0.8, 0.4), (0.1, 0.9)];
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let start = std::time::Instant::now();
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let _outputs = pinn.predict(&inputs).await?;
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let elapsed = start.elapsed().as_secs_f64();
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Ok(elapsed)
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}
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async fn bench_molecular_gnn(batch_size: usize) -> Result<f64> {
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let device = Device::cpu();
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// Create dummy molecular graphs
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let mut molecules = Vec::new();
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for i in 0..batch_size {
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let mol = Molecule::from_smiles(format!("mol_{}", i), "CCO")?;
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molecules.push(mol);
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}
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// Benchmark feature matrix creation
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let start = std::time::Instant::now();
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for mol in &molecules {
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let _features = mol.to_feature_matrix()?;
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}
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let elapsed = start.elapsed().as_secs_f64();
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Ok(elapsed)
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}
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async fn bench_mass_conservation() -> Result<f64> {
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let device = Device::cpu();
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let conservation = MassConservation::new(1e-6);
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// Create test data
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let coords = Tensor::randn([100, 2], &device)?;
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let solution = Variable::new(Tensor::randn([100], &device)?);
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let du_dx = Variable::new(Tensor::randn([100], &device)?);
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let du_dt = Variable::new(Tensor::randn([100], &device)?);
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let start = std::time::Instant::now();
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let _loss = conservation
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.compute_loss(&coords, &solution, &du_dx, &du_dt)
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.await?;
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let elapsed = start.elapsed().as_secs_f64();
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Ok(elapsed)
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}
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async fn bench_energy_conservation() -> Result<f64> {
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let device = Device::cpu();
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let conservation = EnergyConservation::new(0.1, 1e-6);
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// Create test data
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let coords = Tensor::randn([100, 2], &device)?;
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let solution = Variable::new(Tensor::randn([100], &device)?);
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let du_dx = Variable::new(Tensor::randn([100], &device)?);
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let du_dt = Variable::new(Tensor::randn([100], &device)?);
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let start = std::time::Instant::now();
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let _loss = conservation
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.compute_loss(&coords, &solution, &du_dx, &du_dt)
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.await?;
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let elapsed = start.elapsed().as_secs_f64();
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Ok(elapsed)
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}
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async fn bench_linear_solve(size: usize) -> Result<f64> {
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let device = Device::cpu();
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// Create random system Ax = b
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let a = Tensor::randn([size, size], &device)?;
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let b = Tensor::randn([size], &device)?;
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let start = std::time::Instant::now();
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// Placeholder for linear solve - would use actual solver
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let _x = a.matmul(&Tensor::eye([size, size], &device)?)?;
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let elapsed = start.elapsed().as_secs_f64();
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Ok(elapsed)
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}
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async fn bench_fft_transform(size: usize) -> Result<f64> {
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let device = Device::cpu();
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// Create random signal
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let signal = Tensor::randn([size], &device)?;
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let start = std::time::Instant::now();
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// Placeholder for FFT - would use actual FFT implementation
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let _spectrum = signal.multiply(&signal)?;
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let elapsed = start.elapsed().as_secs_f64();
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Ok(elapsed)
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}
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async fn bench_device_pinn(device: &Device) -> Result<f64> {
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let heat_eq = HeatEquation::new(0.1);
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let pinn = PINN::builder()
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.device(device)
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.layers(vec![2, 64, 64, 1])
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.physics_loss(Box::new(heat_eq))
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.build()?;
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let inputs = vec![(0.5, 0.1); 100]; // Batch of inputs
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let start = std::time::Instant::now();
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let _outputs = pinn.predict(&inputs).await?;
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let elapsed = start.elapsed().as_secs_f64();
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Ok(elapsed)
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}
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async fn bench_dataset_loading() -> Result<f64> {
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let start = std::time::Instant::now();
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let _dataset = MolecularDataset::load("benchmark_molecules.csv")?;
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let elapsed = start.elapsed().as_secs_f64();
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Ok(elapsed)
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}
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async fn bench_feature_extraction() -> Result<f64> {
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let dataset = MolecularDataset::load("benchmark_molecules.csv")?;
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let start = std::time::Instant::now();
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for mol in &dataset.molecules {
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let _features = mol.to_feature_matrix()?;
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let _adjacency = mol.adjacency_matrix();
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let _mw = mol.molecular_weight();
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}
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let elapsed = start.elapsed().as_secs_f64();
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Ok(elapsed)
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}
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/// Memory usage benchmark
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fn bench_memory_usage(c: &mut Criterion) {
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let rt = tokio::runtime::Runtime::new().unwrap();
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let mut group = c.benchmark_group("Memory Usage");
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group.bench_function("large_tensor_ops", |b| {
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b.to_async(&rt)
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.iter(|| async { bench_large_tensor_operations().await.unwrap() });
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});
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group.bench_function("molecular_graph_memory", |b| {
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b.to_async(&rt)
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.iter(|| async { bench_molecular_graph_memory().await.unwrap() });
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});
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group.finish();
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}
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async fn bench_large_tensor_operations() -> Result<f64> {
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let device = Device::cpu();
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let start = std::time::Instant::now();
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// Create large tensors
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let a = Tensor::randn([1000, 1000], &device)?;
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let b = Tensor::randn([1000, 1000], &device)?;
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// Perform operations
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let _c = a.matmul(&b)?;
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let _d = a.add(&b)?;
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let _e = a.multiply(&b)?;
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let elapsed = start.elapsed().as_secs_f64();
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Ok(elapsed)
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}
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async fn bench_molecular_graph_memory() -> Result<f64> {
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let start = std::time::Instant::now();
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// Create large molecular dataset
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let mut molecules = Vec::new();
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for i in 0..1000 {
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let mol = Molecule::from_smiles(
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format!("large_mol_{}", i),
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"CCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCCC",
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)?;
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molecules.push(mol);
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}
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// Extract features
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for mol in &molecules {
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let _features = mol.to_feature_matrix()?;
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}
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let elapsed = start.elapsed().as_secs_f64();
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Ok(elapsed)
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}
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/// Accuracy vs speed trade-off benchmark
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fn bench_accuracy_speed_tradeoff(c: &mut Criterion) {
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let rt = tokio::runtime::Runtime::new().unwrap();
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let mut group = c.benchmark_group("Accuracy vs Speed");
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for &precision in &["single", "double"] {
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group.bench_with_input(
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BenchmarkId::new("pinn_precision", precision),
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&precision,
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|b, &precision| {
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b.to_async(&rt)
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.iter(|| async { bench_precision_pinn(precision).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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async fn bench_precision_pinn(precision: &str) -> Result<f64> {
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let device = match precision {
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"single" => Device::cpu(),
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"double" => Device::cpu(), // Would use double precision if available
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_ => Device::cpu(),
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};
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let heat_eq = HeatEquation::new(0.1);
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let pinn = PINN::builder()
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.device(&device)
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.layers(vec![2, 64, 64, 1])
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.physics_loss(Box::new(heat_eq))
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.build()?;
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let inputs = vec![(0.5, 0.1); 50];
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let start = std::time::Instant::now();
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let _outputs = pinn.predict(&inputs).await?;
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let elapsed = start.elapsed().as_secs_f64();
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Ok(elapsed)
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}
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/// Parallel scaling benchmark
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fn bench_parallel_scaling(c: &mut Criterion) {
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let rt = tokio::runtime::Runtime::new().unwrap();
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let mut group = c.benchmark_group("Parallel Scaling");
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for &num_threads in &[1, 2, 4, 8] {
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group.bench_with_input(
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BenchmarkId::new("molecular_parallel", num_threads),
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&num_threads,
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|b, &num_threads| {
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b.to_async(&rt).iter(|| async {
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bench_molecular_parallel_processing(num_threads)
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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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}
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group.finish();
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}
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async fn bench_molecular_parallel_processing(num_threads: usize) -> Result<f64> {
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// Set number of threads (placeholder)
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std::env::set_var("RAYON_NUM_THREADS", num_threads.to_string());
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let start = std::time::Instant::now();
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// Create molecules to process in parallel
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let molecules: Vec<_> = (0..100)
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.map(|i| Molecule::from_smiles(format!("mol_{}", i), "CCCCCCCC").unwrap())
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.collect();
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// Process in parallel using rayon
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use rayon::prelude::*;
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let _results: Vec<_> = molecules
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.par_iter()
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.map(|mol| mol.molecular_weight())
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.collect();
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let elapsed = start.elapsed().as_secs_f64();
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Ok(elapsed)
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}
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criterion_group!(
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benches,
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bench_pinn_training,
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bench_molecular_prediction,
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bench_conservation_laws,
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bench_scientific_computing,
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bench_device_comparison,
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bench_molecular_datasets,
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bench_memory_usage,
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bench_accuracy_speed_tradeoff,
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bench_parallel_scaling
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);
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criterion_main!(benches);
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