Qyriad
b151773b0d
* merkle: add parent() helper function on NodeIndex * smt: add pairs_to_leaf() to trait * smt: add sorted_pairs_to_leaves() and test for it * smt: implement single subtree-8 hashing, w/ benchmarks & tests This will be composed into depth-8-subtree-based computation of entire sparse Merkle trees. * merkle: add a benchmark for constructing 256-balanced trees This is intended for comparison with the benchmarks from the previous commit. This benchmark represents the theoretical perfect-efficiency performance we could possibly (but impractically) get for computing depth-8 sparse Merkle subtrees. * smt: test that SparseMerkleTree::build_subtree() is composable * smt: test that subtree logic can correctly construct an entire tree This commit ensures that `SparseMerkleTree::build_subtree()` can correctly compose into building an entire sparse Merkle tree, without yet getting into potential complications concurrency introduces. * smt: implement test for basic parallelized subtree computation w/ rayon Building on the previous commit, this commit implements a test proving that `SparseMerkleTree::build_subtree()` can be composed into itself not just concurrently, but in parallel, without issue. * smt: add from_raw_parts() to trait interface This commit adds a new required method to the SparseMerkleTree trait, to allow generic construction from pre-computed parts. This will be used to add a generic version of `with_entries()` in a later commit. * smt: add parallel constructors to Smt and SimpleSmt What the previous few commits have been leading up to: SparseMerkleTree now has a function to construct the tree from existing data in parallel. This is significantly faster than the singlethreaded equivalent. Benchmarks incoming! --------- Co-authored-by: krushimir <krushimir@reilabs.co> Co-authored-by: krushimir <kresimir.grofelnik@reilabs.io>
66 lines
2.5 KiB
Rust
66 lines
2.5 KiB
Rust
//! Benchmark for building a [`miden_crypto::merkle::MerkleTree`]. This is intended to be compared
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//! with the results from `benches/smt-subtree.rs`, as building a fully balanced Merkle tree with
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//! 256 leaves should indicate the *absolute best* performance we could *possibly* get for building
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//! a depth-8 sparse Merkle subtree, though practically speaking building a fully balanced Merkle
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//! tree will perform better than the sparse version. At the time of this writing (2024/11/24), this
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//! benchmark is about four times more efficient than the equivalent benchmark in
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//! `benches/smt-subtree.rs`.
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use std::{hint, mem, time::Duration};
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use criterion::{criterion_group, criterion_main, BatchSize, Criterion};
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use miden_crypto::{merkle::MerkleTree, Felt, Word, ONE};
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use rand_utils::prng_array;
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fn balanced_merkle_even(c: &mut Criterion) {
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c.bench_function("balanced-merkle-even", |b| {
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b.iter_batched(
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|| {
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let entries: Vec<Word> =
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(0..256).map(|i| [Felt::new(i), ONE, ONE, Felt::new(i)]).collect();
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assert_eq!(entries.len(), 256);
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entries
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},
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|leaves| {
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let tree = MerkleTree::new(hint::black_box(leaves)).unwrap();
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assert_eq!(tree.depth(), 8);
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},
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BatchSize::SmallInput,
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);
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});
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}
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fn balanced_merkle_rand(c: &mut Criterion) {
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let mut seed = [0u8; 32];
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c.bench_function("balanced-merkle-rand", |b| {
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b.iter_batched(
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|| {
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let entries: Vec<Word> = (0..256).map(|_| generate_word(&mut seed)).collect();
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assert_eq!(entries.len(), 256);
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entries
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},
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|leaves| {
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let tree = MerkleTree::new(hint::black_box(leaves)).unwrap();
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assert_eq!(tree.depth(), 8);
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},
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BatchSize::SmallInput,
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);
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});
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}
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criterion_group! {
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name = smt_subtree_group;
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config = Criterion::default()
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.measurement_time(Duration::from_secs(20))
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.configure_from_args();
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targets = balanced_merkle_even, balanced_merkle_rand
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}
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criterion_main!(smt_subtree_group);
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// HELPER FUNCTIONS
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// --------------------------------------------------------------------------------------------
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fn generate_word(seed: &mut [u8; 32]) -> Word {
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mem::swap(seed, &mut prng_array(*seed));
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let nums: [u64; 4] = prng_array(*seed);
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[Felt::new(nums[0]), Felt::new(nums[1]), Felt::new(nums[2]), Felt::new(nums[3])]
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}
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