Type conversion and casting - rust

I am beginner in rust. I have simple script but I have to use too much type casting.
Point of script: search clusters of neighbour cels with the same value in matrix (using flood fill algo with queue https://en.wikipedia.org/wiki/Flood_fill).
This is full code:
fn find_clusters(playground: [[u8; SIZE]; SIZE]) -> Vec<Cluster> {
let directions_cluster: [[i8; 2]; 4] = [[0, 1], [0, -1], [1, 0], [-1, 0]];
let mut clusters: Vec<Cluster> = Vec::new();
let mut queue: Vec<[usize; 2]> = Vec::new();
let mut marked_cells: [[u8; SIZE]; SIZE] = [[0; SIZE]; SIZE];
for i in 0..SIZE {
for j in 0..SIZE {
if marked_cells[i][j] == 1 { continue; }
let code = playground[i][j];
let mut cluster = Cluster::new();
queue.push([i, j]);
marked_cells[i][j] = 1;
while !queue.is_empty() {
let coords = queue.pop().unwrap();
cluster.coords.push(coords);
for direction in &directions_cluster {
let check_i = coords[0] as i8 + direction[0];
if check_i < 0 || check_i as usize >= SIZE {continue;}
let check_j = coords[1] as i8 + direction[1];
if check_j < 0 || check_j as usize >= SIZE {continue;}
let ni = check_i as usize;
let nj = check_j as usize;
if playground[ni][nj] == code && marked_cells[ni][nj] == 0 {
queue.push([ni, nj]);
marked_cells[ni][nj] = 1;
}
}
}
if cluster.coords.len() >= 5 {
cluster.code = code;
clusters.push(cluster);
}
};
};
return clusters;
}
But I don't like this part:
for direction in &directions_cluster {
let check_i = coords[0] as i8 + direction[0];
if check_i < 0 || check_i as usize >= SIZE {continue;}
let check_j = coords[1] as i8 + direction[1];
if check_j < 0 || check_j as usize >= SIZE {continue;}
let ni = check_i as usize;
let nj = check_j as usize;
if playground[ni][nj] == code && marked_cells[ni][nj] == 0 {
queue.push([ni, nj]);
marked_cells[ni][nj] = 1;
}
}
I even had to define additional variables (check_i, check_j) to not use casting for ni/nj each time later.
What the best way of type casting in may case?

You can use the TryInto trait from the standard library to abstract away from the overflow checking. This is how I would implement it:
use std::convert::TryInto;
type Pos = [usize; 2];
fn add_to_coords(coords: Pos, offset: [i8; 2]) -> Option<Pos> {
let ni: usize = (coords[0] as i8 + direction[0]).try_into().ok()?;
let nj: usize = (coords[1] as i8 + direction[1]).try_into().ok()?;
[ni, nj]
}
// ...
for [ni, nj] in directions.flat_map(|dir| add_to_coords(coords, dir)) {
// ...
}
// ...
The call to flat_map filters out all the return values that were None, in case you were wondering where the continue went.

Related

Calculate UDP Checksum in Rust?

I am trying to manually build packets and am having trouble calculating a correct UDP checksum. Can someone tell me what I am doing wrong in the below code? The packet being passed in is the complete packet to be sent with a placeholder for the UDP Checksum currently of 0x0000, but I sum the psuedoheader, udp header, and udp payload, but according to wireshark my UDP checksums are incorrect. (Mine: 0x9f4c vs Wireshark: 0x2b7b for example)
fn udp_checksum (packet: &Vec<u8>) -> [u8; 2] {
let mut idx = 0;
let mut idx_end = 2;
let mut payload = &packet[42..];
let payload_len = payload.len();
if payload_len % 2 != 0 {
payload.to_vec().push(0);
}
let source_ip_1 = BigEndian::read_u16(&packet[26..28]); //source ip 1 of 2
let source_ip_2 = BigEndian::read_u16(&packet[28..30]); //source ip 2 of 2
let dest_ip_1 = BigEndian::read_u16(&packet[30..32]); //dest ip 1 of 2
let dest_ip_2 = BigEndian::read_u16(&packet[32..34]); //dest ip 2 of 2
let udp_len = BigEndian::read_u16(&packet[38..40]);
let source_port = BigEndian::read_u16(&packet[34..36]);
let dest_port = BigEndian::read_u16(&packet[36..38]);
let mut header_sum = UDP_PROTO as u32 + source_ip_1 as u32 + source_ip_2 as u32 + dest_ip_1 as u32 + dest_ip_2 as u32 + udp_len as u32 + source_port as u32 + dest_port as u32 + udp_len as u32;
// println!("Payload Len: {:?}", &payload.len());
// println!("Payload: {:?}", &payload);
// println!("First Payload Slice: {:?}", &payload[idx..idx_end]);
// println!("First BE U32: {:?}", BigEndian::read_u16(&payload[idx..idx_end]) as u32);
while idx < &payload.len() - 2 {
header_sum += BigEndian::read_u16(&payload[idx..idx_end]) as u32;
println!("Header Sum: {:0x?}", &header_sum);
idx += 2;
idx_end += 2;
}
while header_sum > 0xffff {
header_sum -= 0xffff;
header_sum += 1;
}
let udp_csum = 0xffff - (header_sum as u16);
let csum_one: u8 = header_sum as u8;
let csum_two: u8 = (header_sum >> 8) as u8;
println!("Calculated CSUM: {:?}", udp_csum);
println!("Checksum: {:0x}{:0x}", csum_one, csum_two);
return [csum_one, csum_two];
}```
It's maybe only a partial solution to the problem.
payload.to_vec() creates a new vector.
Extending it has no influence on payload.
Making payload mutable will enable working on the extended vector if necessary.
Here is a minimal example.
fn main() {
let v1 = vec![9, 1, 2, 3];
let mut v2 = Vec::new(); // empty for now
let mut sl = &v1[1..]; // could be reassigned to v2
println!("before v1: {:?}", v1);
println!("before v2: {:?}", v2);
println!("before sl: {:?}", sl);
if sl.len() % 2 != 0 {
v2 = sl.to_vec();
v2.push(0);
sl = &v2[..];
}
println!("after v1: {:?}", v1);
println!("after v2: {:?}", v2);
println!("after sl: {:?}", sl);
}
/*
before v1: [9, 1, 2, 3]
before v2: []
before sl: [1, 2, 3]
after v1: [9, 1, 2, 3]
after v2: [1, 2, 3, 0]
after sl: [1, 2, 3, 0]
*/
Another solution, avoiding the copy, would be to stop one byte earlier (if payload.len() is odd) the loop, then deal with the remaining byte.

Severe performance degredation over time in multi-threading: what am I missing?

In my application a method runs quickly once started but begins to continuously degrade in performance upon nearing completion, this seems to be even irrelevant of the amount of work (the number of iterations of a function each thread has to perform). Once it reaches near the end it slows to an incredibly slow pace compared to earlier (worth noting this is not just a result of fewer threads remaining incomplete, it seems even each thread slows down).
I cannot figure out why this occurs, so I'm asking. What am I doing wrong?
An overview of CPU usage:
A slideshow of the problem
Worth noting that CPU temperature remains low throughout.
This stage varies with however much work is set, more work produces a better appearance with all threads constantly near 100%. Still, at this moment this appears good.
Here we see the continued performance of earlier,
Here we see it start to degrade. I do not know why this occurs.
After some period of chaos most of the threads have finished their work and the remaining threads continue, at this point although it seems they are at 100% they in actually perform their remaining workload very slowly. I cannot understand why this occurs.
Printing progress
I have written a multi-threaded random_search (documentation link) function for optimization. Most of the complexity in this function comes from printing data passing data between threads, this supports giving outputs showing progress like:
2300
565 (24.57%) 00:00:11 / 00:00:47 [25.600657363049734] { [563.0ns, 561.3ms, 125.0ns, 110.0ns] [2.0µs, 361.8ms, 374.0ns, 405.0ns] [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1] }
I have been trying to use this output to figure out whats gone wrong, but I have no idea.
This output describes:
The total number of iterations 2300.
The total number of current iterations 565.
The time running 00:00:11 (mm:ss:ms).
The estimated time remaining 00:00:47 (mm:ss:ms).
The current best value [25.600657363049734].
The most recently measured times between execution positions (effectively time taken for thread to go from some line, to another line (defined specifically with update_execution_position in code below) [563.0ns, 561.3ms, 125.0ns, 110.0ns].
The averages times between execution positions (this is average across entire runtime rather than since last measured) [2.0µs, 361.8ms, 374.0ns, 405.0ns].
The execution positions of threads (0 is when a thread is completed, rest represent a thread having hit some line, which triggered this setting, but yet to hit next line which changes it, effectively being between 2 positions) [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
The random_search code:
Given I have tested implementations with the other methods in my library grid_search and simulated_annealing it would suggest to me the problem does not atleast entirely reside in random_search.rs.
random_search.rs:
pub fn random_search<
A: 'static + Send + Sync,
T: 'static + Copy + Send + Sync + Default + SampleUniform + PartialOrd,
const N: usize,
>(
// Generics
ranges: [Range<T>; N],
f: fn(&[T; N], Option<Arc<A>>) -> f64,
evaluation_data: Option<Arc<A>>,
polling: Option<Polling>,
// Specifics
iterations: u64,
) -> [T; N] {
// Gets cpu data
let cpus = num_cpus::get() as u64;
let search_cpus = cpus - 1; // 1 cpu is used for polling, this one.
let remainder = iterations % search_cpus;
let per = iterations / search_cpus;
let ranges_arc = Arc::new(ranges);
let (best_value, best_params) = search(
// Generics
ranges_arc.clone(),
f,
evaluation_data.clone(),
// Since we are doing this on the same thread, we don't need to use these
Arc::new(AtomicU64::new(Default::default())),
Arc::new(Mutex::new(Default::default())),
Arc::new(AtomicBool::new(false)),
Arc::new(AtomicU8::new(0)),
Arc::new([
Mutex::new((Duration::new(0, 0), 0)),
Mutex::new((Duration::new(0, 0), 0)),
Mutex::new((Duration::new(0, 0), 0)),
Mutex::new((Duration::new(0, 0), 0)),
]),
// Specifics
remainder,
);
let thread_exit = Arc::new(AtomicBool::new(false));
// (handles,(counters,thread_bests))
let (handles, links): (Vec<_>, Vec<_>) = (0..search_cpus)
.map(|_| {
let ranges_clone = ranges_arc.clone();
let counter = Arc::new(AtomicU64::new(0));
let thread_best = Arc::new(Mutex::new(f64::MAX));
let thread_execution_position = Arc::new(AtomicU8::new(0));
let thread_execution_time = Arc::new([
Mutex::new((Duration::new(0, 0), 0)),
Mutex::new((Duration::new(0, 0), 0)),
Mutex::new((Duration::new(0, 0), 0)),
Mutex::new((Duration::new(0, 0), 0)),
]);
let counter_clone = counter.clone();
let thread_best_clone = thread_best.clone();
let thread_exit_clone = thread_exit.clone();
let evaluation_data_clone = evaluation_data.clone();
let thread_execution_position_clone = thread_execution_position.clone();
let thread_execution_time_clone = thread_execution_time.clone();
(
thread::spawn(move || {
search(
// Generics
ranges_clone,
f,
evaluation_data_clone,
counter_clone,
thread_best_clone,
thread_exit_clone,
thread_execution_position_clone,
thread_execution_time_clone,
// Specifics
per,
)
}),
(
counter,
(
thread_best,
(thread_execution_position, thread_execution_time),
),
),
)
})
.unzip();
let (counters, links): (Vec<Arc<AtomicU64>>, Vec<_>) = links.into_iter().unzip();
let (thread_bests, links): (Vec<Arc<Mutex<f64>>>, Vec<_>) = links.into_iter().unzip();
let (thread_execution_positions, thread_execution_times) = links.into_iter().unzip();
if let Some(poll_data) = polling {
poll(
poll_data,
counters,
remainder,
iterations,
thread_bests,
thread_exit,
thread_execution_positions,
thread_execution_times,
);
}
let joins: Vec<_> = handles.into_iter().map(|h| h.join().unwrap()).collect();
let (_, best_params) = joins
.into_iter()
.fold((best_value, best_params), |(bv, bp), (v, p)| {
if v < bv {
(v, p)
} else {
(bv, bp)
}
});
return best_params;
fn search<
A: 'static + Send + Sync,
T: 'static + Copy + Send + Sync + Default + SampleUniform + PartialOrd,
const N: usize,
>(
// Generics
ranges: Arc<[Range<T>; N]>,
f: fn(&[T; N], Option<Arc<A>>) -> f64,
evaluation_data: Option<Arc<A>>,
counter: Arc<AtomicU64>,
best: Arc<Mutex<f64>>,
thread_exit: Arc<AtomicBool>,
thread_execution_position: Arc<AtomicU8>,
thread_execution_times: Arc<[Mutex<(Duration, u64)>; 4]>,
// Specifics
iterations: u64,
) -> (f64, [T; N]) {
let mut execution_position_timer = Instant::now();
let mut rng = thread_rng();
let mut params = [Default::default(); N];
let mut best_value = f64::MAX;
let mut best_params = [Default::default(); N];
for _ in 0..iterations {
// Gen random values
for (range, param) in ranges.iter().zip(params.iter_mut()) {
*param = rng.gen_range(range.clone());
}
// Update execution position
execution_position_timer = update_execution_position(
1,
execution_position_timer,
&thread_execution_position,
&thread_execution_times,
);
// Run function
let new_value = f(&params, evaluation_data.clone());
// Update execution position
execution_position_timer = update_execution_position(
2,
execution_position_timer,
&thread_execution_position,
&thread_execution_times,
);
// Check best
if new_value < best_value {
best_value = new_value;
best_params = params;
*best.lock().unwrap() = best_value;
}
// Update execution position
execution_position_timer = update_execution_position(
3,
execution_position_timer,
&thread_execution_position,
&thread_execution_times,
);
counter.fetch_add(1, Ordering::SeqCst);
// Update execution position
execution_position_timer = update_execution_position(
4,
execution_position_timer,
&thread_execution_position,
&thread_execution_times,
);
if thread_exit.load(Ordering::SeqCst) {
break;
}
}
// Update execution position
// 0 represents ended state
thread_execution_position.store(0, Ordering::SeqCst);
return (best_value, best_params);
}
}
util.rs:
pub fn update_execution_position<const N: usize>(
i: usize,
execution_position_timer: Instant,
thread_execution_position: &Arc<AtomicU8>,
thread_execution_times: &Arc<[Mutex<(Duration, u64)>; N]>,
) -> Instant {
{
let mut data = thread_execution_times[i - 1].lock().unwrap();
data.0 += execution_position_timer.elapsed();
data.1 += 1;
}
thread_execution_position.store(i as u8, Ordering::SeqCst);
Instant::now()
}
pub struct Polling {
pub poll_rate: u64,
pub printing: bool,
pub early_exit_minimum: Option<f64>,
pub thread_execution_reporting: bool,
}
impl Polling {
const DEFAULT_POLL_RATE: u64 = 10;
pub fn new(printing: bool, early_exit_minimum: Option<f64>) -> Self {
Self {
poll_rate: Polling::DEFAULT_POLL_RATE,
printing,
early_exit_minimum,
thread_execution_reporting: false,
}
}
}
pub fn poll<const N: usize>(
data: Polling,
// Current count of each thread.
counters: Vec<Arc<AtomicU64>>,
offset: u64,
// Final total iterations.
iterations: u64,
// Best values of each thread.
thread_bests: Vec<Arc<Mutex<f64>>>,
// Early exit switch.
thread_exit: Arc<AtomicBool>,
// Current positions of execution of each thread.
thread_execution_positions: Vec<Arc<AtomicU8>>,
// Current average times between execution positions for each thread
thread_execution_times: Vec<Arc<[Mutex<(Duration, u64)>; N]>>,
) {
let start = Instant::now();
let mut stdout = stdout();
let mut count = offset
+ counters
.iter()
.map(|c| c.load(Ordering::SeqCst))
.sum::<u64>();
if data.printing {
println!("{:20}", iterations);
}
let mut poll_time = Instant::now();
let mut held_best: f64 = f64::MAX;
let mut held_average_execution_times: [(Duration, u64); N] =
vec![(Duration::new(0, 0), 0); N].try_into().unwrap();
let mut held_recent_execution_times: [Duration; N] =
vec![Duration::new(0, 0); N].try_into().unwrap();
while count < iterations {
if data.printing {
// loop {
let percent = count as f32 / iterations as f32;
// If count == 0, give 00... for remaining time as placeholder
let remaining_time_estimate = if count == 0 {
Duration::new(0, 0)
} else {
start.elapsed().div_f32(percent)
};
print!(
"\r{:20} ({:.2}%) {} / {} [{}] {}\t",
count,
100. * percent,
print_duration(start.elapsed(), 0..3),
print_duration(remaining_time_estimate, 0..3),
if held_best == f64::MAX {
String::from("?")
} else {
format!("{}", held_best)
},
if data.thread_execution_reporting {
let (average_execution_times, recent_execution_times): (
Vec<String>,
Vec<String>,
) = (0..thread_execution_times[0].len())
.map(|i| {
let (mut sum, mut num) = (Duration::new(0, 0), 0);
for n in 0..thread_execution_times.len() {
{
let mut data = thread_execution_times[n][i].lock().unwrap();
sum += data.0;
held_average_execution_times[i].0 += data.0;
num += data.1;
held_average_execution_times[i].1 += data.1;
*data = (Duration::new(0, 0), 0);
}
}
if num > 0 {
held_recent_execution_times[i] = sum.div_f64(num as f64);
}
(
if held_average_execution_times[i].1 > 0 {
format!(
"{:.1?}",
held_average_execution_times[i]
.0
.div_f64(held_average_execution_times[i].1 as f64)
)
} else {
String::from("?")
},
if held_recent_execution_times[i] > Duration::new(0, 0) {
format!("{:.1?}", held_recent_execution_times[i])
} else {
String::from("?")
},
)
})
.unzip();
let execution_positions: Vec<u8> = thread_execution_positions
.iter()
.map(|pos| pos.load(Ordering::SeqCst))
.collect();
format!(
"{{ [{}] [{}] {:.?} }}",
recent_execution_times.join(", "),
average_execution_times.join(", "),
execution_positions
)
} else {
String::from("")
}
);
stdout.flush().unwrap();
}
// Updates best and does early exiting
match (data.early_exit_minimum, data.printing) {
(Some(early_exit), true) => {
for thread_best in thread_bests.iter() {
let thread_best_temp = *thread_best.lock().unwrap();
if thread_best_temp < held_best {
held_best = thread_best_temp;
if thread_best_temp <= early_exit {
thread_exit.store(true, Ordering::SeqCst);
println!();
return;
}
}
}
}
(None, true) => {
for thread_best in thread_bests.iter() {
let thread_best_temp = *thread_best.lock().unwrap();
if thread_best_temp < held_best {
held_best = thread_best_temp;
}
}
}
(Some(early_exit), false) => {
for thread_best in thread_bests.iter() {
if *thread_best.lock().unwrap() <= early_exit {
thread_exit.store(true, Ordering::SeqCst);
return;
}
}
}
(None, false) => {}
}
thread::sleep(saturating_sub(
Duration::from_millis(data.poll_rate),
poll_time.elapsed(),
));
poll_time = Instant::now();
count = offset
+ counters
.iter()
.map(|c| c.load(Ordering::SeqCst))
.sum::<u64>();
}
if data.printing {
println!(
"\r{:20} (100.00%) {} / {} [{}] {}\t",
count,
print_duration(start.elapsed(), 0..3),
print_duration(start.elapsed(), 0..3),
held_best,
if data.thread_execution_reporting {
let (average_execution_times, recent_execution_times): (Vec<String>, Vec<String>) =
(0..thread_execution_times[0].len())
.map(|i| {
let (mut sum, mut num) = (Duration::new(0, 0), 0);
for n in 0..thread_execution_times.len() {
{
let mut data = thread_execution_times[n][i].lock().unwrap();
sum += data.0;
held_average_execution_times[i].0 += data.0;
num += data.1;
held_average_execution_times[i].1 += data.1;
*data = (Duration::new(0, 0), 0);
}
}
if num > 0 {
held_recent_execution_times[i] = sum.div_f64(num as f64);
}
(
if held_average_execution_times[i].1 > 0 {
format!(
"{:.1?}",
held_average_execution_times[i]
.0
.div_f64(held_average_execution_times[i].1 as f64)
)
} else {
String::from("?")
},
if held_recent_execution_times[i] > Duration::new(0, 0) {
format!("{:.1?}", held_recent_execution_times[i])
} else {
String::from("?")
},
)
})
.unzip();
let execution_positions: Vec<u8> = thread_execution_positions
.iter()
.map(|pos| pos.load(Ordering::SeqCst))
.collect();
format!(
"{{ [{}] [{}] {:.?} }}",
recent_execution_times.join(", "),
average_execution_times.join(", "),
execution_positions
)
} else {
String::from("")
}
);
stdout.flush().unwrap();
}
}
// Since `Duration::saturating_sub` is unstable this is an alternative.
fn saturating_sub(a: Duration, b: Duration) -> Duration {
if let Some(dur) = a.checked_sub(b) {
dur
} else {
Duration::new(0, 0)
}
}
main.rs
use std::{cmp,sync::Arc};
type Image = Vec<Vec<Pixel>>;
#[derive(Clone)]
pub struct Pixel {
pub luma: u8,
}
impl From<&u8> for Pixel {
fn from(x: &u8) -> Pixel {
Pixel { luma: *x }
}
}
fn main() {
// Setup
// -------------------------------------------
fn open_image(path: &str) -> Image {
let example = image::open(path).unwrap().to_rgb8();
let dims = example.dimensions();
let size = (dims.0 as usize, dims.1 as usize);
let example_vec = example.into_raw();
// Binarizes image
let img_vec = from_raw(&example_vec, size);
img_vec
}
println!("Started ...");
let example: Image = open_image("example.jpg");
let target: Image = open_image("target.jpg");
// let first_image = Some(Arc::new((examples[0].clone(), targets[0].clone())));
println!("Opened...");
let image = Some(Arc::new((example, target)));
// Running the optimization
// -------------------------------------------
println!("Started opt...");
let best = simple_optimization::random_search(
[0..255, 0..255, 0..255, 1..255, 1..255],
eval_one,
image,
Some(simple_optimization::Polling {
poll_rate: 100,
printing: true,
early_exit_minimum: None,
thread_execution_reporting: true,
}),
2300,
);
println!("{:.?}", best); // [34, 220, 43, 253, 168]
assert!(false);
fn eval_one(arr: &[u8; 5], opt: Option<Arc<(Image, Image)>>) -> f64 {
let bin_params = (
arr[0] as u8,
arr[1] as u8,
arr[2] as u8,
arr[3] as usize,
arr[4] as usize,
);
let arc = opt.unwrap();
// Gets average mean-squared-error
let binary_pixels = binarize_buffer(arc.0.clone(), bin_params);
mse(binary_pixels, &arc.1)
}
// Mean-squared-error
fn mse(prediction: Image, target: &Image) -> f64 {
let n = target.len() * target[0].len();
prediction
.iter()
.flatten()
.zip(target.iter().flatten())
.map(|(p, t)| difference(p, t).powf(2.))
.sum::<f64>()
/ (2. * n as f64)
}
#[rustfmt::skip]
fn difference(p: &Pixel, t: &Pixel) -> f64 {
p.luma as f64 - t.luma as f64
}
}
pub fn from_raw(raw: &[u8], (_i_size, j_size): (usize, usize)) -> Vec<Vec<Pixel>> {
(0..raw.len())
.step_by(j_size)
.map(|index| {
raw[index..index + j_size]
.iter()
.map(Pixel::from)
.collect::<Vec<Pixel>>()
})
.collect()
}
pub fn binarize_buffer(
mut img: Vec<Vec<Pixel>>,
(_, _, local_luma_boundary, local_field_reach, local_field_size): (u8, u8, u8, usize, usize),
) -> Vec<Vec<Pixel>> {
let (i_size, j_size) = (img.len(), img[0].len());
let i_chunks = (i_size as f32 / local_field_size as f32).ceil() as usize;
let j_chunks = (j_size as f32 / local_field_size as f32).ceil() as usize;
let mut local_luma: Vec<Vec<u8>> = vec![vec![u8::default(); j_chunks]; i_chunks];
// Gets average luma in local fields
// O((s+r)^2*(n/s)*(m/s)) : s = local field size, r = local field reach
for (i_chunk, i) in (0..i_size).step_by(local_field_size).enumerate() {
let i_range = zero_checked_sub(i, local_field_reach)
..cmp::min(i + local_field_size + local_field_reach, i_size);
let i_range_length = i_range.end - i_range.start;
for (j_chunk, j) in (0..j_size).step_by(local_field_size).enumerate() {
let j_range = zero_checked_sub(j, local_field_reach)
..cmp::min(j + local_field_size + local_field_reach, j_size);
let j_range_length = j_range.end - j_range.start;
let total: u32 = i_range
.clone()
.map(|i_range_indx| {
img[i_range_indx][j_range.clone()]
.iter()
.map(|p| p.luma as u32)
.sum::<u32>()
})
.sum();
local_luma[i_chunk][j_chunk] = (total / (i_range_length * j_range_length) as u32) as u8;
}
}
// Apply binarization
// O(nm)
for i in 0..i_size {
let i_group: usize = i / local_field_size; // == floor(i as f32 / local_field_size as f32) as usize
for j in 0..j_size {
let j_group: usize = j / local_field_size;
// Local average boundaries
// --------------------------------
if let Some(local) = local_luma[i_group][j_group].checked_sub(local_luma_boundary) {
if img[i][j].luma < local {
img[i][j].luma = 0;
continue;
}
}
if let Some(local) = local_luma[i_group][j_group].checked_add(local_luma_boundary) {
if img[i][j].luma > local {
img[i][j].luma = 255;
continue;
}
}
// White is the negative (false/0) colour in our binarization, thus this is our else case
img[i][j].luma = 255;
}
}
img
}
#[rustfmt::skip]
fn zero_checked_sub(a: usize, b: usize) -> usize { if a > b { a - b } else { 0 } }
Project zip (in case you'd rather not spend time setting it up).
Else, here are the images being used as /target.jpg and /example.jpg (it shouldn't matter it being specifically these images, any should work):
And Cargo.toml dependencies:
[dependencies]
rand = "0.8.4"
itertools = "0.10.1" # izip!
num_cpus = "1.13.0" # Multi-threading
print_duration = "1.0.0" # Printing progress
num = "0.4.0" # Generics
rand_distr = "0.4.1" # Normal distribution
image = "0.23.14"
serde = { version="1.0.118", features=["derive"] }
serde_json = "1.0.50"
I do feel rather reluctant to post such a large question and
inevitably require people to read a few hundred lines (especially given the project doesn't work in a playground), but I'm really lost here and can see no other way to communicate the whole area of the problem. Apologies for this.
As noted, I have tried for a while to figure out what is happening here, but I have come up short, any help would be really appreciate.
Some basic debugging (aka println! everywhere) shows that your performance problem is not related to the multithreading at all. It just happens randomly, and when there are 24 threads doing their job, the fact that one is randomly stalling is not noticeable, but when there is only one or two threads left, they stand out as slow.
But where is this performance bottleneck? Well, you are stating it yourself in the code: in binary_buffer you say:
// Gets average luma in local fields
// O((s+r)^2*(n/s)*(m/s)) : s = local field size, r = local field reach
The values of s and r seem to be random values between 0 and 255, while n is the length of a image row, in bytes 3984 * 3 = 11952, and m is the number of rows 2271.
Now, most of the times that O() is around a few millions, quite manageable. But if s happens to be small and r big, such as (3, 200) then the number of computations blows up to over 1e11!
Fortunately I think you can define the ranges of those values in the original call to random_search so a bit of tweaking there should send you back to reasonable complexity. Changing the ranges to:
[0..255, 0..255, 0..255, 1..255, 20..255],
// ^ here
seems to do the trick for me.
PS: These lines at the beginning of binary_buffer were key to discover this:
let o = (i_size / local_field_size) * (j_size / local_field_size) * (local_field_size + local_field_reach).pow(2);
println!("\nO() = {}", o);

Insertion sort algorithm gives overflow error

When trying to run the insertion sort algorithm as shown below in Rust 1.15.
fn main() {
println!("The sorted set is now: {:?}", insertion_sort(vec![5,2,4,6,1,3]));
}
fn insertion_sort(set: Vec<i32>) -> Vec<i32> {
let mut A = set.to_vec();
for j in 1..set.len() {
let key = A[j];
let mut i = j - 1;
while (i >= 0) && (A[i] > key) {
A[i + 1] = A[i];
i = i - 1;
}
A[i + 1] = key;
}
A
}
I get the error:
thread 'main' panicked at 'attempt to subtract with overflow', insertion_sort.rs:12
note: Run with `RUST_BACKTRACE=1` for a backtrace
Why does an overflow happen here and how is the problem alleviated?
The reason is you tried to calculate 0 - 1 in usize type, which is unsigned (nonnegative). This may lead to an error in Rust.
Why usize? Because Rust expects usize for lengths and indices. You can explicitly convert them into/from signed ones e.g. isize.
fn main() {
println!("The sorted set is now: {:?}", insertion_sort(vec![5,2,4,6,1,3]));
}
fn insertion_sort(set: Vec<i32>) -> Vec<i32> {
let mut A = set.to_vec();
for j in 1..set.len() as isize {
let key = A[j as usize];
let mut i = j - 1;
while (i >= 0) && (A[i as usize] > key) {
A[(i + 1) as usize] = A[i as usize];
i = i - 1;
}
A[(i + 1) as usize] = key;
}
A
}
Another solution, which I recommend, is to avoid negative indices at all. In this case you can use i + 1 instead of i like this:
fn main() {
println!("The sorted set is now: {:?}", insertion_sort(vec![5,2,4,6,1,3]));
}
fn insertion_sort(set: Vec<i32>) -> Vec<i32> {
let mut A = set.to_vec();
for j in 1..set.len() {
let key = A[j];
let mut i = j;
while (i > 0) && (A[i - 1] > key) {
A[i] = A[i - 1];
i = i - 1;
}
A[i] = key;
}
A
}

How to create a very large array? [duplicate]

I'm implementing combsort. I'd like to create fixed-size array on the stack, but it shows stack overflow. When I change it to be on the heap (Rust by Example says to allocate in the heap we must use Box), it still shows stack overflow.
fn new_gap(gap: usize) -> usize {
let ngap = ((gap as f64) / 1.3) as usize;
if ngap == 9 || ngap == 10 {
return 11;
}
if ngap < 1 {
return 1;
}
return ngap;
}
fn comb_sort(a: &mut Box<[f64]>) {
// previously: [f64]
let xlen = a.len();
let mut gap = xlen;
let mut swapped: bool;
let mut temp: f64;
loop {
swapped = false;
gap = new_gap(gap);
for i in 0..(xlen - gap) {
if a[i] > a[i + gap] {
swapped = true;
temp = a[i];
a[i] = a[i + gap];
a[i + gap] = temp;
}
}
if !(gap > 1 || swapped) {
break;
}
}
}
const N: usize = 10000000;
fn main() {
let mut arr: Box<[f64]> = Box::new([0.0; N]); // previously: [f64; N] = [0.0; N];
for z in 0..(N) {
arr[z] = (N - z) as f64;
}
comb_sort(&mut arr);
for z in 1..(N) {
if arr[z] < arr[z - 1] {
print!("!")
}
}
}
The output:
thread '<main>' has overflowed its stack
Illegal instruction (core dumped)
Or
thread 'main' has overflowed its stack
fatal runtime error: stack overflow
I know that my stack size is not enough, the same as C++ when creating a non-heap array that is too big inside a function, but this code is using heap but still shows stack overflow. What's really wrong with this code?
As far as I can tell, it seems like that code is still trying to allocate the array on the stack first, and then move it into the box after.
It works for me if I switch to Vec<f64> in place of Box<[f64]> like this:
fn new_gap(gap: usize) -> usize {
let ngap = ((gap as f64) / 1.3) as usize;
if ngap == 9 || ngap == 10 {
return 11;
}
if ngap < 1 {
return 1;
}
return ngap;
}
fn comb_sort(a: &mut [f64]) {
// previously: [f64]
let xlen = a.len();
let mut gap = xlen;
let mut swapped: bool;
let mut temp: f64;
loop {
swapped = false;
gap = new_gap(gap);
for i in 0..(xlen - gap) {
if a[i] > a[i + gap] {
swapped = true;
temp = a[i];
a[i] = a[i + gap];
a[i + gap] = temp;
}
}
if !(gap > 1 || swapped) {
break;
}
}
}
const N: usize = 10000000;
fn main() {
let mut arr: Vec<f64> = std::iter::repeat(0.0).take(N).collect();
//let mut arr: Box<[f64]> = Box::new([0.0; N]); // previously: [f64; N] = [0.0; N];
for z in 0..(N) {
arr[z] = (N - z) as f64;
}
comb_sort(arr.as_mut_slice());
for z in 1..(N) {
if arr[z] < arr[z - 1] {
print!("!")
}
}
}
In the future, the box syntax will be stabilized. When it is, it will support this large allocation, as no function call to Box::new will be needed, thus the array will never be placed on the stack. For example:
#![feature(box_syntax)]
fn main() {
let v = box [0i32; 5_000_000];
println!("{}", v[1_000_000])
}

Thread '<main>' has overflowed its stack when allocating a large array using Box

I'm implementing combsort. I'd like to create fixed-size array on the stack, but it shows stack overflow. When I change it to be on the heap (Rust by Example says to allocate in the heap we must use Box), it still shows stack overflow.
fn new_gap(gap: usize) -> usize {
let ngap = ((gap as f64) / 1.3) as usize;
if ngap == 9 || ngap == 10 {
return 11;
}
if ngap < 1 {
return 1;
}
return ngap;
}
fn comb_sort(a: &mut Box<[f64]>) {
// previously: [f64]
let xlen = a.len();
let mut gap = xlen;
let mut swapped: bool;
let mut temp: f64;
loop {
swapped = false;
gap = new_gap(gap);
for i in 0..(xlen - gap) {
if a[i] > a[i + gap] {
swapped = true;
temp = a[i];
a[i] = a[i + gap];
a[i + gap] = temp;
}
}
if !(gap > 1 || swapped) {
break;
}
}
}
const N: usize = 10000000;
fn main() {
let mut arr: Box<[f64]> = Box::new([0.0; N]); // previously: [f64; N] = [0.0; N];
for z in 0..(N) {
arr[z] = (N - z) as f64;
}
comb_sort(&mut arr);
for z in 1..(N) {
if arr[z] < arr[z - 1] {
print!("!")
}
}
}
The output:
thread '<main>' has overflowed its stack
Illegal instruction (core dumped)
Or
thread 'main' has overflowed its stack
fatal runtime error: stack overflow
I know that my stack size is not enough, the same as C++ when creating a non-heap array that is too big inside a function, but this code is using heap but still shows stack overflow. What's really wrong with this code?
As far as I can tell, it seems like that code is still trying to allocate the array on the stack first, and then move it into the box after.
It works for me if I switch to Vec<f64> in place of Box<[f64]> like this:
fn new_gap(gap: usize) -> usize {
let ngap = ((gap as f64) / 1.3) as usize;
if ngap == 9 || ngap == 10 {
return 11;
}
if ngap < 1 {
return 1;
}
return ngap;
}
fn comb_sort(a: &mut [f64]) {
// previously: [f64]
let xlen = a.len();
let mut gap = xlen;
let mut swapped: bool;
let mut temp: f64;
loop {
swapped = false;
gap = new_gap(gap);
for i in 0..(xlen - gap) {
if a[i] > a[i + gap] {
swapped = true;
temp = a[i];
a[i] = a[i + gap];
a[i + gap] = temp;
}
}
if !(gap > 1 || swapped) {
break;
}
}
}
const N: usize = 10000000;
fn main() {
let mut arr: Vec<f64> = std::iter::repeat(0.0).take(N).collect();
//let mut arr: Box<[f64]> = Box::new([0.0; N]); // previously: [f64; N] = [0.0; N];
for z in 0..(N) {
arr[z] = (N - z) as f64;
}
comb_sort(arr.as_mut_slice());
for z in 1..(N) {
if arr[z] < arr[z - 1] {
print!("!")
}
}
}
In the future, the box syntax will be stabilized. When it is, it will support this large allocation, as no function call to Box::new will be needed, thus the array will never be placed on the stack. For example:
#![feature(box_syntax)]
fn main() {
let v = box [0i32; 5_000_000];
println!("{}", v[1_000_000])
}

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