#![allow(non_snake_case)] extern crate rand; use rand::rngs::OsRng; use rand::thread_rng; #[macro_use] extern crate criterion; use criterion::measurement::Measurement; use criterion::BatchSize; use criterion::Criterion; use criterion::{BenchmarkGroup, BenchmarkId}; extern crate noah_curve25519_dalek; use noah_curve25519_dalek::constants; use noah_curve25519_dalek::scalar::Scalar; static BATCH_SIZES: [usize; 5] = [1, 2, 4, 8, 16]; static MULTISCALAR_SIZES: [usize; 13] = [1, 2, 4, 8, 16, 32, 64, 128, 256, 384, 512, 768, 1024]; mod edwards_benches { use super::*; use noah_curve25519_dalek::edwards::EdwardsPoint; fn compress(c: &mut Criterion) { let B = &constants::ED25519_BASEPOINT_POINT; c.bench_function("EdwardsPoint compression", move |b| b.iter(|| B.compress())); } fn decompress(c: &mut Criterion) { let B_comp = &constants::ED25519_BASEPOINT_COMPRESSED; c.bench_function("EdwardsPoint decompression", move |b| { b.iter(|| B_comp.decompress().unwrap()) }); } fn consttime_fixed_base_scalar_mul(c: &mut Criterion) { let B = &constants::ED25519_BASEPOINT_TABLE; let s = Scalar::from(897987897u64).invert(); c.bench_function("Constant-time fixed-base scalar mul", move |b| { b.iter(|| B * &s) }); } fn consttime_variable_base_scalar_mul(c: &mut Criterion) { let B = &constants::ED25519_BASEPOINT_POINT; let s = Scalar::from(897987897u64).invert(); c.bench_function("Constant-time variable-base scalar mul", move |b| { b.iter(|| B * s) }); } fn vartime_double_base_scalar_mul(c: &mut Criterion) { c.bench_function("Variable-time aA+bB, A variable, B fixed", |bench| { let mut rng = thread_rng(); let A = &Scalar::random(&mut rng) * &constants::ED25519_BASEPOINT_TABLE; bench.iter_batched( || (Scalar::random(&mut rng), Scalar::random(&mut rng)), |(a, b)| EdwardsPoint::vartime_double_scalar_mul_basepoint(&a, &A, &b), BatchSize::SmallInput, ); }); } criterion_group! { name = edwards_benches; config = Criterion::default(); targets = compress, decompress, consttime_fixed_base_scalar_mul, consttime_variable_base_scalar_mul, vartime_double_base_scalar_mul, } } mod multiscalar_benches { use super::*; use noah_curve25519_dalek::edwards::EdwardsPoint; use noah_curve25519_dalek::edwards::VartimeEdwardsPrecomputation; use noah_curve25519_dalek::traits::MultiscalarMul; use noah_curve25519_dalek::traits::VartimeMultiscalarMul; use noah_curve25519_dalek::traits::VartimePrecomputedMultiscalarMul; fn construct_scalars(n: usize) -> Vec { let mut rng = thread_rng(); (0..n).map(|_| Scalar::random(&mut rng)).collect() } fn construct_points(n: usize) -> Vec { let mut rng = thread_rng(); (0..n) .map(|_| &Scalar::random(&mut rng) * &constants::ED25519_BASEPOINT_TABLE) .collect() } fn consttime_multiscalar_mul(c: &mut BenchmarkGroup) { for multiscalar_size in &MULTISCALAR_SIZES { c.bench_with_input( BenchmarkId::new( "Constant-time variable-base multiscalar multiplication", *multiscalar_size, ), &multiscalar_size, |b, &&size| { let points = construct_points(size); // This is supposed to be constant-time, but we might as well // rerandomize the scalars for every call just in case. b.iter_batched( || construct_scalars(size), |scalars| EdwardsPoint::multiscalar_mul(&scalars, &points), BatchSize::SmallInput, ); }, ); } } fn vartime_multiscalar_mul(c: &mut BenchmarkGroup) { for multiscalar_size in &MULTISCALAR_SIZES { c.bench_with_input( BenchmarkId::new( "Variable-time variable-base multiscalar multiplication", *multiscalar_size, ), &multiscalar_size, |b, &&size| { let points = construct_points(size); // Rerandomize the scalars for every call to prevent // false timings from better caching (e.g., the CPU // cache lifts exactly the right table entries for the // benchmark into the highest cache levels). b.iter_batched( || construct_scalars(size), |scalars| EdwardsPoint::vartime_multiscalar_mul(&scalars, &points), BatchSize::SmallInput, ); }, ); } } fn vartime_precomputed_pure_static(c: &mut BenchmarkGroup) { for multiscalar_size in &MULTISCALAR_SIZES { c.bench_with_input( BenchmarkId::new( "Variable-time fixed-base multiscalar multiplication", &multiscalar_size, ), &multiscalar_size, move |b, &&total_size| { let static_size = total_size; let static_points = construct_points(static_size); let precomp = VartimeEdwardsPrecomputation::new(&static_points); // Rerandomize the scalars for every call to prevent // false timings from better caching (e.g., the CPU // cache lifts exactly the right table entries for the // benchmark into the highest cache levels). b.iter_batched( || construct_scalars(static_size), |scalars| precomp.vartime_multiscalar_mul(&scalars), BatchSize::SmallInput, ); }, ); } } fn vartime_precomputed_helper( c: &mut BenchmarkGroup, dynamic_fraction: f64, ) { for multiscalar_size in &MULTISCALAR_SIZES { let bench_id = BenchmarkId::new( "Variable-time mixed-base", format!( "(size: {:?}), ({:.0}pct dyn)", multiscalar_size, 100.0 * dynamic_fraction ), ); c.bench_with_input(bench_id, &multiscalar_size, move |b, &&total_size| { let dynamic_size = ((total_size as f64) * dynamic_fraction) as usize; let static_size = total_size - dynamic_size; let static_points = construct_points(static_size); let dynamic_points = construct_points(dynamic_size); let precomp = VartimeEdwardsPrecomputation::new(&static_points); // Rerandomize the scalars for every call to prevent // false timings from better caching (e.g., the CPU // cache lifts exactly the right table entries for the // benchmark into the highest cache levels). Timings // should be independent of points so we don't // randomize them. b.iter_batched( || { ( construct_scalars(static_size), construct_scalars(dynamic_size), ) }, |(static_scalars, dynamic_scalars)| { precomp.vartime_mixed_multiscalar_mul( &static_scalars, &dynamic_scalars, &dynamic_points, ) }, BatchSize::SmallInput, ); }); } } fn multiscalar_multiplications(c: &mut Criterion) { let mut group: BenchmarkGroup<_> = c.benchmark_group("Multiscalar multiplications"); consttime_multiscalar_mul(&mut group); vartime_multiscalar_mul(&mut group); vartime_precomputed_pure_static(&mut group); let dynamic_fracs = [0.0, 0.2, 0.5]; for frac in dynamic_fracs.iter() { vartime_precomputed_helper(&mut group, *frac); } group.finish(); } criterion_group! { name = multiscalar_benches; // Lower the sample size to run the benchmarks faster config = Criterion::default().sample_size(15); targets = multiscalar_multiplications, } } mod ristretto_benches { use super::*; use noah_curve25519_dalek::ristretto::RistrettoPoint; fn compress(c: &mut Criterion) { c.bench_function("RistrettoPoint compression", |b| { let B = &constants::RISTRETTO_BASEPOINT_POINT; b.iter(|| B.compress()) }); } fn decompress(c: &mut Criterion) { c.bench_function("RistrettoPoint decompression", |b| { let B_comp = &constants::RISTRETTO_BASEPOINT_COMPRESSED; b.iter(|| B_comp.decompress().unwrap()) }); } fn double_and_compress_batch(c: &mut BenchmarkGroup) { for batch_size in &BATCH_SIZES { c.bench_with_input( BenchmarkId::new("Batch Ristretto double-and-encode", *batch_size), &batch_size, |b, &&size| { let mut rng = OsRng; let points: Vec = (0..size) .map(|_| RistrettoPoint::random(&mut rng)) .collect(); b.iter(|| RistrettoPoint::double_and_compress_batch(&points)); }, ); } } fn double_and_compress_group(c: &mut Criterion) { let mut group: BenchmarkGroup<_> = c.benchmark_group("double & compress batched"); double_and_compress_batch(&mut group); group.finish(); } criterion_group! { name = ristretto_benches; config = Criterion::default(); targets = compress, decompress, double_and_compress_group, } } mod montgomery_benches { use super::*; fn montgomery_ladder(c: &mut Criterion) { c.bench_function("Montgomery pseudomultiplication", |b| { let B = constants::X25519_BASEPOINT; let s = Scalar::from(897987897u64).invert(); b.iter(|| B * s); }); } criterion_group! { name = montgomery_benches; config = Criterion::default(); targets = montgomery_ladder, } } mod scalar_benches { use super::*; fn scalar_inversion(c: &mut Criterion) { c.bench_function("Scalar inversion", |b| { let s = Scalar::from(897987897u64).invert(); b.iter(|| s.invert()); }); } fn batch_scalar_inversion(c: &mut BenchmarkGroup) { for batch_size in &BATCH_SIZES { c.bench_with_input( BenchmarkId::new("Batch scalar inversion", *batch_size), &batch_size, |b, &&size| { let mut rng = OsRng; let scalars: Vec = (0..size).map(|_| Scalar::random(&mut rng)).collect(); b.iter(|| { let mut s = scalars.clone(); Scalar::batch_invert(&mut s); }); }, ); } } fn batch_scalar_inversion_group(c: &mut Criterion) { let mut group: BenchmarkGroup<_> = c.benchmark_group("batch scalar inversion"); batch_scalar_inversion(&mut group); group.finish(); } criterion_group! { name = scalar_benches; config = Criterion::default(); targets = scalar_inversion, batch_scalar_inversion_group, } } criterion_main!( scalar_benches::scalar_benches, montgomery_benches::montgomery_benches, ristretto_benches::ristretto_benches, edwards_benches::edwards_benches, multiscalar_benches::multiscalar_benches, );