miden-crypto/src/main.rs
polydez 589839fef1
feat: reverse mutations generation, mutations serialization (#355)
* feat: revert mutations generation, mutations serialization
* tests: check both `apply_mutations` and `apply_mutations_with_reversion`
* feat: add `num_leaves` method for `Smt`
* refactor: improve ad-hoc benchmarks
* chore: update crate version to v0.13.1
2024-12-26 18:16:38 -08:00

214 lines
6.7 KiB
Rust

use std::time::Instant;
use clap::Parser;
use miden_crypto::{
hash::rpo::{Rpo256, RpoDigest},
merkle::{MerkleError, Smt},
Felt, Word, EMPTY_WORD, ONE,
};
use rand::{prelude::IteratorRandom, thread_rng, Rng};
use rand_utils::rand_value;
#[derive(Parser, Debug)]
#[clap(name = "Benchmark", about = "SMT benchmark", version, rename_all = "kebab-case")]
pub struct BenchmarkCmd {
/// Size of the tree
#[clap(short = 's', long = "size")]
size: usize,
}
fn main() {
benchmark_smt();
}
/// Run a benchmark for [`Smt`].
pub fn benchmark_smt() {
let args = BenchmarkCmd::parse();
let tree_size = args.size;
// prepare the `leaves` vector for tree creation
let mut entries = Vec::new();
for i in 0..tree_size {
let key = rand_value::<RpoDigest>();
let value = [ONE, ONE, ONE, Felt::new(i as u64)];
entries.push((key, value));
}
let mut tree = construction(entries.clone(), tree_size).unwrap();
insertion(&mut tree).unwrap();
batched_insertion(&mut tree).unwrap();
batched_update(&mut tree, entries).unwrap();
proof_generation(&mut tree).unwrap();
}
/// Runs the construction benchmark for [`Smt`], returning the constructed tree.
pub fn construction(entries: Vec<(RpoDigest, Word)>, size: usize) -> Result<Smt, MerkleError> {
println!("Running a construction benchmark:");
let now = Instant::now();
let tree = Smt::with_entries(entries)?;
let elapsed = now.elapsed().as_secs_f32();
println!("Constructed a SMT with {size} key-value pairs in {elapsed:.1} seconds");
println!("Number of leaf nodes: {}\n", tree.leaves().count());
Ok(tree)
}
/// Runs the insertion benchmark for the [`Smt`].
pub fn insertion(tree: &mut Smt) -> Result<(), MerkleError> {
const NUM_INSERTIONS: usize = 1_000;
println!("Running an insertion benchmark:");
let size = tree.num_leaves();
let mut insertion_times = Vec::new();
for i in 0..NUM_INSERTIONS {
let test_key = Rpo256::hash(&rand_value::<u64>().to_be_bytes());
let test_value = [ONE, ONE, ONE, Felt::new((size + i) as u64)];
let now = Instant::now();
tree.insert(test_key, test_value);
let elapsed = now.elapsed();
insertion_times.push(elapsed.as_micros());
}
println!(
"An average insertion time measured by {NUM_INSERTIONS} inserts into an SMT with {size} leaves is {:.0} μs\n",
// calculate the average
insertion_times.iter().sum::<u128>() as f64 / (NUM_INSERTIONS as f64),
);
Ok(())
}
pub fn batched_insertion(tree: &mut Smt) -> Result<(), MerkleError> {
const NUM_INSERTIONS: usize = 1_000;
println!("Running a batched insertion benchmark:");
let size = tree.num_leaves();
let new_pairs: Vec<(RpoDigest, Word)> = (0..NUM_INSERTIONS)
.map(|i| {
let key = Rpo256::hash(&rand_value::<u64>().to_be_bytes());
let value = [ONE, ONE, ONE, Felt::new((size + i) as u64)];
(key, value)
})
.collect();
let now = Instant::now();
let mutations = tree.compute_mutations(new_pairs);
let compute_elapsed = now.elapsed().as_secs_f64() * 1000_f64; // time in ms
let now = Instant::now();
tree.apply_mutations(mutations)?;
let apply_elapsed = now.elapsed().as_secs_f64() * 1000_f64; // time in ms
println!(
"An average insert-batch computation time measured by a {NUM_INSERTIONS}-batch into an SMT with {size} leaves over {:.1} ms is {:.0} μs",
compute_elapsed,
compute_elapsed * 1000_f64 / NUM_INSERTIONS as f64, // time in μs
);
println!(
"An average insert-batch application time measured by a {NUM_INSERTIONS}-batch into an SMT with {size} leaves over {:.1} ms is {:.0} μs",
apply_elapsed,
apply_elapsed * 1000_f64 / NUM_INSERTIONS as f64, // time in μs
);
println!(
"An average batch insertion time measured by a 1k-batch into an SMT with {size} leaves totals to {:.1} ms",
(compute_elapsed + apply_elapsed),
);
println!();
Ok(())
}
pub fn batched_update(tree: &mut Smt, entries: Vec<(RpoDigest, Word)>) -> Result<(), MerkleError> {
const NUM_UPDATES: usize = 1_000;
const REMOVAL_PROBABILITY: f64 = 0.2;
println!("Running a batched update benchmark:");
let size = tree.num_leaves();
let mut rng = thread_rng();
let new_pairs =
entries
.into_iter()
.choose_multiple(&mut rng, NUM_UPDATES)
.into_iter()
.map(|(key, _)| {
let value = if rng.gen_bool(REMOVAL_PROBABILITY) {
EMPTY_WORD
} else {
[ONE, ONE, ONE, Felt::new(rng.gen())]
};
(key, value)
});
assert_eq!(new_pairs.len(), NUM_UPDATES);
let now = Instant::now();
let mutations = tree.compute_mutations(new_pairs);
let compute_elapsed = now.elapsed().as_secs_f64() * 1000_f64; // time in ms
let now = Instant::now();
tree.apply_mutations(mutations)?;
let apply_elapsed = now.elapsed().as_secs_f64() * 1000_f64; // time in ms
println!(
"An average update-batch computation time measured by a {NUM_UPDATES}-batch into an SMT with {size} leaves over {:.1} ms is {:.0} μs",
compute_elapsed,
compute_elapsed * 1000_f64 / NUM_UPDATES as f64, // time in μs
);
println!(
"An average update-batch application time measured by a {NUM_UPDATES}-batch into an SMT with {size} leaves over {:.1} ms is {:.0} μs",
apply_elapsed,
apply_elapsed * 1000_f64 / NUM_UPDATES as f64, // time in μs
);
println!(
"An average batch update time measured by a 1k-batch into an SMT with {size} leaves totals to {:.1} ms",
(compute_elapsed + apply_elapsed),
);
println!();
Ok(())
}
/// Runs the proof generation benchmark for the [`Smt`].
pub fn proof_generation(tree: &mut Smt) -> Result<(), MerkleError> {
const NUM_PROOFS: usize = 100;
println!("Running a proof generation benchmark:");
let mut insertion_times = Vec::new();
let size = tree.num_leaves();
for i in 0..NUM_PROOFS {
let test_key = Rpo256::hash(&rand_value::<u64>().to_be_bytes());
let test_value = [ONE, ONE, ONE, Felt::new((size + i) as u64)];
tree.insert(test_key, test_value);
let now = Instant::now();
let _proof = tree.open(&test_key);
insertion_times.push(now.elapsed().as_micros());
}
println!(
"An average proving time measured by {NUM_PROOFS} value proofs in an SMT with {size} leaves in {:.0} μs",
// calculate the average
insertion_times.iter().sum::<u128>() as f64 / (NUM_PROOFS as f64),
);
Ok(())
}