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🔧 Tests and benchmarks #16

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julius merged 6 commits from core-types into master 2024-01-04 11:59:32 +00:00
Conversation 0 Commits 6 Files Changed 10 +41 -36
2 changed files with 41 additions and 36 deletions
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75
benches/manifold_benchmark.rs
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@ -1,15 +1,15 @@
use manifold::*;
use rand::Rng;
use criterion::Throughput;
use criterion::{criterion_group, criterion_main, BenchmarkId, Criterion};
use manifold::*;
use rand::Rng;
fn random_tensor_r2_manifold() -> Tensor<f64, 2> {
let mut rng = rand::thread_rng();
let mut tensor = tensor!([[0.0; 1000]; 1000]);
for i in 0..tensor.len() {
tensor[i] = rng.gen();
}
tensor
let mut rng = rand::thread_rng();
let mut tensor = tensor!([[0.0; 1000]; 1000]);
for i in 0..tensor.len() {
tensor[i] = rng.gen();
}
tensor
}
fn random_tensor_r2_ndarray() -> ndarray::Array2<f64> {
@ -25,37 +25,42 @@ fn random_tensor_r2_ndarray() -> ndarray::Array2<f64> {
}
fn tensor_product(c: &mut Criterion) {
let b = 1000;
let b = 1000;
let mut group = c.benchmark_group("element-wise addition");
let mut group = c.benchmark_group("element-wise addition");
for (i, size) in [b].iter().enumerate() {
group.throughput(Throughput::Elements(*size as u64));
for (i, size) in [b].iter().enumerate() {
group.throughput(Throughput::Elements(*size as u64));
group.bench_with_input(BenchmarkId::new("manifold", size), &i, |b, _| {
b.iter(|| {
let a = random_tensor_r2_manifold();
let b = random_tensor_r2_manifold();
let c = a + b;
assert!(c.shape().as_array() == &[1000, 1000]);
})
});
group.bench_with_input(
BenchmarkId::new("manifold", size),
&i,
|b, _| {
b.iter(|| {
let a = random_tensor_r2_manifold();
let b = random_tensor_r2_manifold();
let c = a + b;
assert!(c.shape().as_array() == &[1000, 1000]);
})
},
);
group.bench_with_input(BenchmarkId::new("ndarray", size), &i, |b, _| {
b.iter(|| {
let a = random_tensor_r2_ndarray();
let b = random_tensor_r2_ndarray();
let c = a + b;
assert!(c.shape() == &[1000, 1000]);
})
});
}
group.finish();
group.bench_with_input(
BenchmarkId::new("ndarray", size),
&i,
|b, _| {
b.iter(|| {
let a = random_tensor_r2_ndarray();
let b = random_tensor_r2_ndarray();
let c = a + b;
assert!(c.shape() == &[1000, 1000]);
})
},
);
}
group.finish();
}
criterion_group!(
benches,
tensor_product
);
criterion_group!(benches, tensor_product);
criterion_main!(benches);
criterion_main!(benches);

2
tests/mod.rs
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@ -1 +1 @@
mod basic_tests;
mod basic_tests;