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Add missing polybench benchmarks #740

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merged 15 commits into from
Feb 23, 2025
Merged

Add missing polybench benchmarks #740

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1cf9fe6
jacobi-1d
ajpal Feb 17, 2025
f14da76
jacobi-2d
ajpal Feb 17, 2025
df495f7
adi and heat-3d
ajpal Feb 18, 2025
7e41cbf
Create seidel-2d.rs
ajpal Feb 18, 2025
31f6382
Create floyd-warshall.rs
ajpal Feb 19, 2025
cd4e3f0
Nussinov
ajpal Feb 19, 2025
780bec6
Create correlation.rs
ajpal Feb 19, 2025
a1acba2
Covariance
ajpal Feb 19, 2025
924f299
last two!
ajpal Feb 19, 2025
469747c
Merge branch 'main' into ajpal-polybench2
ajpal Feb 19, 2025
e421266
small instead of medium
ajpal Feb 20, 2025
2bd84f7
probably fix fdtd
ajpal Feb 22, 2025
7f2e9c1
make returns explicit
ajpal Feb 22, 2025
3019f9c
oops one more
ajpal Feb 22, 2025
bd5b0ea
oops
ajpal Feb 22, 2025
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166 changes: 166 additions & 0 deletions benchmarks/passing/polybench/datamining/correlation.rs
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Original file line number Diff line number Diff line change
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fn custom_sqrt(x: f64) -> f64 {
let mut guess: f64 = x / 2.0;
let mut i: i64 = 0;
while i < 10 {
guess = (guess + x / guess) / 2.0;
i += 1;
}
return guess;
}

fn init_array(m: i64, mf: f64, n: i64, data: &mut [[f64; 80]; 100]) {
let mut i: i64 = 0;
let mut fi: f64 = 0.0;
while i < n {
let mut j: i64 = 0;
let mut fj: f64 = 0.0;
while j < m {
let x: f64 = (fi * fj) / mf + fi;
data[i as usize][j as usize] = x;
j += 1;
fj += 1.0;
}
i += 1;
fi += 1.0;
}
}

fn sum_array(m: i64, corr: &[[f64; 80]; 80]) -> f64 {
let mut sum: f64 = 0.0;
let mut i: i64 = 0;
while i < m {
let mut j: i64 = 0;
while j < m {
let x: f64 = corr[i as usize][j as usize];
// Can't print multiple things on nightly.
// Uncomment to run locally to check output.
// println!("{} ", x);
sum += x;
j += 1;
}
i += 1;
}
return sum;
}

fn kernel_correlation(
m: i64,
n: i64,
nf: f64,
data: &mut [[f64; 80]; 100],
corr: &mut [[f64; 80]; 80],
mean: &mut [f64; 80],
stddev: &mut [f64; 80],
) {
let eps: f64 = 0.1;
let mut j: i64 = 0;
while j < m {
mean[j as usize] = 0.0;
let mut i: i64 = 0;
while i < n {
mean[j as usize] = mean[j as usize] + data[i as usize][j as usize];

i += 1;
}

mean[j as usize] = mean[j as usize] / nf;

j += 1;
}

j = 0;
while j < m {
stddev[j as usize] = 0.0;
let mut i: i64 = 0;
while i < n {
stddev[j as usize] = stddev[j as usize]
+ (data[i as usize][j as usize] - mean[j as usize])
* (data[i as usize][j as usize] - mean[j as usize]);
i += 1;
}
stddev[j as usize] = stddev[j as usize] / nf;
stddev[j as usize] = custom_sqrt(stddev[j as usize]);
if stddev[j as usize] <= eps {
stddev[j as usize] = 1.0;
}
j += 1;
}

let mut i: i64 = 0;
while i < n {
j = 0;
while j < m {
data[i as usize][j as usize] = data[i as usize][j as usize] - mean[j as usize];
data[i as usize][j as usize] =
data[i as usize][j as usize] / (custom_sqrt(nf) * stddev[j as usize]);
j += 1;
}
i += 1;
}

i = 0;
while i < m - 1 {
corr[i as usize][i as usize] = 1.0;
j = i + 1;
while j < m {
corr[i as usize][j as usize] = 0.0;
let mut k: i64 = 0;
while k < n {
corr[i as usize][j as usize] = corr[i as usize][j as usize]
+ data[k as usize][i as usize] * data[k as usize][j as usize];
k += 1;
}
corr[j as usize][i as usize] = corr[i as usize][j as usize];
j += 1;
}
i += 1;
}
corr[(m - 1) as usize][(m - 1) as usize] = 1.0;
}

fn main() {
let n: i64 = 100;
let nf: f64 = 100.0;
let m: i64 = 80;
let mf: f64 = 80.0;
let mut dummy: [f64; 80] = [0.0; 80];
let mut data: [[f64; 80]; 100] = [dummy; 100];
let mut corr: [[f64; 80]; 80] = [dummy; 80];
let mut mean: [f64; 80] = [0.0; 80];
let mut stddev: [f64; 80] = [0.0; 80];

// Init
let mut i: i64 = 0;
while i < n {
data[i] = [0.0; 80];
i += 1;
}
i = 0;
while i < m {
corr[i] = [0.0; 80];
i += 1;
}
drop(dummy);

init_array(m, mf, n, &mut data);
kernel_correlation(m, n, nf, &mut data, &mut corr, &mut mean, &mut stddev);
let res: f64 = sum_array(m, &corr);

// Drop
i = 0;
while i < n {
drop(data[i]);
i += 1;
}
i = 0;
while i < m {
drop(corr[i]);
i += 1;
}
drop(data);
drop(corr);
drop(mean);
drop(stddev);

println!("{}", res);
}
128 changes: 128 additions & 0 deletions benchmarks/passing/polybench/datamining/covariance.rs
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Original file line number Diff line number Diff line change
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fn init_array(m: i64, mf: f64, n: i64, data: &mut [[f64; 80]; 100]) {
let mut i: i64 = 0;
let mut fi: f64 = 0.0;
while i < n {
let mut j: i64 = 0;
let mut fj: f64 = 0.0;
while j < m {
data[i as usize][j as usize] = (fi * fj) / mf;
j += 1;
fj += 1.0;
}
i += 1;
fi += 1.0;
}
}

fn sum_array(m: i64, cov: &[[f64; 80]; 80]) -> f64 {
let mut sum: f64 = 0.0;
let mut i: i64 = 0;
while i < m {
let mut j: i64 = 0;
while j < m {
let x: f64 = cov[i as usize][j as usize];
// Can't print multiple things on nightly.
// Uncomment to run locally to check output.
// println!("{} ", x);
sum += x;
j += 1;
}
i += 1;
}
return sum;
}

fn kernel_covariance(
m: i64,
n: i64,
nf: f64,
data: &mut [[f64; 80]; 100],
cov: &mut [[f64; 80]; 80],
mean: &mut [f64; 80],
) {
let mut j: i64 = 0;
while j < m {
mean[j as usize] = 0.0;
let mut i: i64 = 0;
while i < n {
let dij: f64 = data[i as usize][j as usize];
mean[j as usize] = mean[j as usize] + dij;
i += 1;
}
mean[j as usize] = mean[j as usize] / nf;
j += 1;
}

let mut i: i64 = 0;
while i < n {
j = 0;
while j < m {
data[i as usize][j as usize] = data[i as usize][j as usize] - mean[j as usize];
j += 1;
}
i += 1;
}

i = 0;
while i < m {
j = i;
while j < m {
cov[i as usize][j as usize] = 0.0;
let mut k: i64 = 0;
while k < n {
cov[i as usize][j as usize] = cov[i as usize][j as usize]
+ data[k as usize][i as usize] * data[k as usize][j as usize];
k += 1;
}
cov[i as usize][j as usize] = cov[i as usize][j as usize] / (nf - 1.0);
cov[j as usize][i as usize] = cov[i as usize][j as usize];
j += 1;
}
i += 1;
}
}

fn main() {
let n: i64 = 100;
let nf: f64 = 100.0;
let m: i64 = 80;
let mf: f64 = 80.0;
let dummy: [f64; 80] = [0.0; 80];
let mut data: [[f64; 80]; 100] = [dummy; 100];
let mut cov: [[f64; 80]; 80] = [dummy; 80];
let mut mean: [f64; 80] = [0.0; 80];

// Init
let mut i: i64 = 0;
while i < n {
data[i] = [0.0; 80];
i += 1;
}
i = 0;
while i < m {
cov[i] = [0.0; 80];
i += 1;
}
drop(dummy);

init_array(m, mf, n, &mut data);
kernel_covariance(m, n, nf, &mut data, &mut cov, &mut mean);
let res: f64 = sum_array(m, &cov);

// Drop
i = 0;
while i < n {
drop(data[i]);
i += 1;
}
i = 0;
while i < m {
drop(cov[i]);
i += 1;
}
drop(data);
drop(cov);
drop(mean);

println!("{}", res);
}
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