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https://github.com/logos-storage/plonky2.git
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7d6c0a448d
* Halo2 style lookup arguments in System Zero It's a really nice and simple protocol, particularly for the verifier since the constraints are trivial (aside from the underlying batched permutation checks, which we already support). See the [Halo2 book](https://zcash.github.io/halo2/design/proving-system/lookup.html) and this [talk](https://www.youtube.com/watch?v=YlTt12s7vGE&t=5237s) by @daira. Previously we generated the whole trace in row-wise form, but it's much more efficient to generate these "permuted" columns column-wise. So I changed our STARK framework to accept the trace in column-wise form. STARK impls now have the flexibility to do some generation row-wise and some column-wise (without extra costs; there's a single transpose as before). * sorting * fixes * PR feedback * into_iter * timing
31 lines
1.2 KiB
Rust
31 lines
1.2 KiB
Rust
use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion};
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use itertools::Itertools;
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use plonky2::field::field_types::Field;
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use plonky2::field::goldilocks_field::GoldilocksField;
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use rand::{thread_rng, Rng};
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use system_zero::lookup::permuted_cols;
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type F = GoldilocksField;
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fn criterion_benchmark(c: &mut Criterion) {
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let mut group = c.benchmark_group("lookup-permuted-cols");
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for size_log in [16, 17, 18] {
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let size = 1 << size_log;
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group.bench_with_input(BenchmarkId::from_parameter(size), &size, |b, _| {
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// We could benchmark a table of random values with
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// let table = F::rand_vec(size);
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// But in practice we currently use tables that are pre-sorted, which makes
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// permuted_cols cheaper since it will sort the table.
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let table = (0..size).map(F::from_canonical_usize).collect_vec();
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let input = (0..size)
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.map(|_| table[thread_rng().gen_range(0..size)])
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.collect_vec();
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b.iter(|| permuted_cols(&input, &table));
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});
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}
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}
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criterion_group!(benches, criterion_benchmark);
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criterion_main!(benches);
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