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<main>
<h1 id="线程"><a class="header" href="#线程">线程</a></h1>
<h3 id="生成一个临时性的线程"><a class="header" href="#生成一个临时性的线程">生成一个临时性的线程</a></h3>
<p>下面例子用到了 <a href="cookbook/cocurrency/intro.html">crossbeam</a> 包，它提供了非常实用的、用于并发和并行编程的数据结构和函数。</p>
<p><a href="https://docs.rs/crossbeam/*/crossbeam/thread/struct.Scope.html#method.spawn">Scope::spawn</a> 会生成一个被限定了作用域的线程，该线程最大的特点就是：它会在传给 <a href="https://docs.rs/crossbeam/0.8.1/crossbeam/fn.scope.html">crossbeam::scope</a> 的闭包函数返回前先行结束。得益于这个特点，子线程的创建使用就像是本地闭包函数调用，因此生成的线程内部可以使用外部环境中的变量！</p>
<pre><pre class="playground"><code class="language-rust editable edition2021">fn main() {
let arr = &amp;[1, 25, -4, 10];
let max = find_max(arr);
assert_eq!(max, Some(25));
}
// 将数组分成两个部分，并使用新的线程对它们进行处理
fn find_max(arr: &amp;[i32]) -&gt; Option&lt;i32&gt; {
const THRESHOLD: usize = 2;
if arr.len() &lt;= THRESHOLD {
return arr.iter().cloned().max();
}
let mid = arr.len() / 2;
let (left, right) = arr.split_at(mid);
crossbeam::scope(|s| {
let thread_l = s.spawn(|_| find_max(left));
let thread_r = s.spawn(|_| find_max(right));
let max_l = thread_l.join().unwrap()?;
let max_r = thread_r.join().unwrap()?;
Some(max_l.max(max_r))
}).unwrap()
}
</code></pre></pre>
<h3 id="创建并行流水线"><a class="header" href="#创建并行流水线">创建并行流水线</a></h3>
<p>下面我们使用 <a href="https://docs.rs/crossbeam/latest/crossbeam/">crossbeam</a> 和 <a href="https://docs.rs/crossbeam-channel/*/crossbeam_channel/index.html">crossbeam-channel</a> 来创建一个并行流水线：流水线的两端分别是数据源和数据下沉( sink )，在流水线中间，有两个工作线程会从源头接收数据，对数据进行并行处理，最后将数据下沉。</p>
<ul>
<li>消息通道( channel )是 <a href="https://docs.rs/crossbeam-channel/0.5.4/crossbeam_channel/fn.bounded.html">crossbeam_channel::bounded</a>，它只能缓存一条消息。当缓存满后，发送者继续调用 [crossbeam_channel::Sender::send] 发送消息时会阻塞，直到一个工作线程( 消费者 ) 拿走这条消息</li>
<li>消费者获取消息时先到先得的策略，因此两个工作线程只有一个能取到消息，保证消息不会被重复消费、处理</li>
<li>通过迭代器 <a href="https://docs.rs/crossbeam-channel/*/crossbeam_channel/struct.Receiver.html#method.iter">crossbeam_channel::Receiver::iter</a> 读取消息会阻塞当前线程，直到新消息的到来或 channel 关闭</li>
<li>channel 只有在所有的发送者或消费者关闭后，才能被关闭。而其中一个消费者 <code>rcv2</code> 处于阻塞读取状态，无比被关闭，因此我们必须要关闭所有发送者: <code>drop(snd1);</code> <code>drop(snd2)</code> ，这样 channel 关闭后，主线程的 <code>rcv2</code> 才能从阻塞状态退出，最后整个程序结束。大家还是迷惑的话，可以看看这篇<a href="https://course.rs/practice/pitfalls/main-with-channel-blocked.html">文章</a>。</li>
</ul>
<pre><pre class="playground"><code class="language-rust editable edition2021">extern crate crossbeam;
extern crate crossbeam_channel;
use std::thread;
use std::time::Duration;
use crossbeam_channel::bounded;
fn main() {
let (snd1, rcv1) = bounded(1);
let (snd2, rcv2) = bounded(1);
let n_msgs = 4;
let n_workers = 2;
crossbeam::scope(|s| {
// 生产者线程
s.spawn(|_| {
for i in 0..n_msgs {
snd1.send(i).unwrap();
println!(&quot;Source sent {}&quot;, i);
}
// 关闭其中一个发送者 snd1
// 该关闭操作对于结束最后的循环是必须的
drop(snd1);
});
// 通过两个线程并行处理
for _ in 0..n_workers {
// 从数据源接收数据，然后发送到下沉端
let (sendr, recvr) = (snd2.clone(), rcv1.clone());
// 生成单独的工作线程
s.spawn(move |_| {
thread::sleep(Duration::from_millis(500));
// 等待通道的关闭
for msg in recvr.iter() {
println!(&quot;Worker {:?} received {}.&quot;,
thread::current().id(), msg);
sendr.send(msg * 2).unwrap();
}
});
}
// 关闭通道，如果不关闭，下沉端将永远无法结束循环
drop(snd2);
// 下沉端
for msg in rcv2.iter() {
println!(&quot;Sink received {}&quot;, msg);
}
}).unwrap();
}
</code></pre></pre>
<h3 id="线程间传递数据"><a class="header" href="#线程间传递数据">线程间传递数据</a></h3>
<p>下面我们来看看 <a href="https://docs.rs/crossbeam-channel/*/crossbeam_channel/index.html">crossbeam-channel</a> 的单生产者单消费者( SPSC ) 使用场景。</p>
<pre><pre class="playground"><code class="language-rust editable edition2021">use std::{thread, time};
use crossbeam_channel::unbounded;
fn main() {
// unbounded 意味着 channel 可以存储任意多的消息
let (snd, rcv) = unbounded();
let n_msgs = 5;
crossbeam::scope(|s| {
s.spawn(|_| {
for i in 0..n_msgs {
snd.send(i).unwrap();
thread::sleep(time::Duration::from_millis(100));
}
});
}).unwrap();
for _ in 0..n_msgs {
let msg = rcv.recv().unwrap();
println!(&quot;Received {}&quot;, msg);
}
}
</code></pre></pre>
<h3 id="维护全局可变的状态"><a class="header" href="#维护全局可变的状态">维护全局可变的状态</a></h3>
<p><a href="">lazy_static</a> 会创建一个全局的静态引用( static ref )，该引用使用了 <code>Mutex</code> 以支持可变性，因此我们可以在代码中对其进行修改。<code>Mutex</code> 能保证该全局状态同时只能被一个线程所访问。</p>
<pre><pre class="playground"><code class="language-rust editable edition2021">use error_chain::error_chain;
use lazy_static::lazy_static;
use std::sync::Mutex;
error_chain!{ }
lazy_static! {
static ref FRUIT: Mutex&lt;Vec&lt;String&gt;&gt; = Mutex::new(Vec::new());
}
fn insert(fruit: &amp;str) -&gt; Result&lt;()&gt; {
let mut db = FRUIT.lock().map_err(|_| &quot;Failed to acquire MutexGuard&quot;)?;
db.push(fruit.to_string());
Ok(())
}
fn main() -&gt; Result&lt;()&gt; {
insert(&quot;apple&quot;)?;
insert(&quot;orange&quot;)?;
insert(&quot;peach&quot;)?;
{
let db = FRUIT.lock().map_err(|_| &quot;Failed to acquire MutexGuard&quot;)?;
db.iter().enumerate().for_each(|(i, item)| println!(&quot;{}: {}&quot;, i, item));
}
insert(&quot;grape&quot;)?;
Ok(())
}
</code></pre></pre>
<h3 id="并行计算-iso-文件的-sha256"><a class="header" href="#并行计算-iso-文件的-sha256">并行计算 iso 文件的 SHA256</a></h3>
<p>下面的示例将为当前目录中的每一个 .iso 文件都计算一个 SHA256 sum。其中线程池中会初始化和 CPU 核心数一致的线程数，其中核心数是通过 <a href="https://docs.rs/num_cpus/*/num_cpus/fn.get.html">num_cpus::get</a> 函数获取。</p>
<p><code>Walkdir::new</code> 可以遍历当前的目录，然后调用 <code>execute</code> 来执行读操作和 SHA256 哈希计算。</p>
<pre><pre class="playground"><code class="language-rust editable edition2021">
use walkdir::WalkDir;
use std::fs::File;
use std::io::{BufReader, Read, Error};
use std::path::Path;
use threadpool::ThreadPool;
use std::sync::mpsc::channel;
use ring::digest::{Context, Digest, SHA256};
// Verify the iso extension
fn is_iso(entry: &amp;Path) -&gt; bool {
match entry.extension() {
Some(e) if e.to_string_lossy().to_lowercase() == &quot;iso&quot; =&gt; true,
_ =&gt; false,
}
}
fn compute_digest&lt;P: AsRef&lt;Path&gt;&gt;(filepath: P) -&gt; Result&lt;(Digest, P), Error&gt; {
let mut buf_reader = BufReader::new(File::open(&amp;filepath)?);
let mut context = Context::new(&amp;SHA256);
let mut buffer = [0; 1024];
loop {
let count = buf_reader.read(&amp;mut buffer)?;
if count == 0 {
break;
}
context.update(&amp;buffer[..count]);
}
Ok((context.finish(), filepath))
}
fn main() -&gt; Result&lt;(), Error&gt; {
let pool = ThreadPool::new(num_cpus::get());
let (tx, rx) = channel();
for entry in WalkDir::new(&quot;/home/user/Downloads&quot;)
.follow_links(true)
.into_iter()
.filter_map(|e| e.ok())
.filter(|e| !e.path().is_dir() &amp;&amp; is_iso(e.path())) {
let path = entry.path().to_owned();
let tx = tx.clone();
pool.execute(move || {
let digest = compute_digest(path);
tx.send(digest).expect(&quot;Could not send data!&quot;);
});
}
drop(tx);
for t in rx.iter() {
let (sha, path) = t?;
println!(&quot;{:?} {:?}&quot;, sha, path);
}
Ok(())
}
</code></pre></pre>
<h3 id="使用线程池来绘制分形"><a class="header" href="#使用线程池来绘制分形">使用线程池来绘制分形</a></h3>
<p>下面例子中将基于 <a href="">Julia Set</a> 来绘制一个分形图片，其中使用到了线程池来做分布式计算。</p>
<img src="https://cloud.githubusercontent.com/assets/221000/26546700/9be34e80-446b-11e7-81dc-dd9871614ea1.png" />
<pre><pre class="playground"><code class="language-rust edtiable edition2021"><span class="boring">use error_chain::error_chain;
</span>use std::sync::mpsc::{channel, RecvError};
use threadpool::ThreadPool;
use num::complex::Complex;
use image::{ImageBuffer, Pixel, Rgb};
<span class="boring">
</span><span class="boring">error_chain! {
</span><span class="boring"> foreign_links {
</span><span class="boring"> MpscRecv(RecvError);
</span><span class="boring"> Io(std::io::Error);
</span><span class="boring"> }
</span><span class="boring">}
</span><span class="boring">
</span><span class="boring">// Function converting intensity values to RGB
</span><span class="boring">// Based on http://www.efg2.com/Lab/ScienceAndEngineering/Spectra.htm
</span><span class="boring">fn wavelength_to_rgb(wavelength: u32) -&gt; Rgb&lt;u8&gt; {
</span><span class="boring"> let wave = wavelength as f32;
</span><span class="boring">
</span><span class="boring"> let (r, g, b) = match wavelength {
</span><span class="boring"> 380..=439 =&gt; ((440. - wave) / (440. - 380.), 0.0, 1.0),
</span><span class="boring"> 440..=489 =&gt; (0.0, (wave - 440.) / (490. - 440.), 1.0),
</span><span class="boring"> 490..=509 =&gt; (0.0, 1.0, (510. - wave) / (510. - 490.)),
</span><span class="boring"> 510..=579 =&gt; ((wave - 510.) / (580. - 510.), 1.0, 0.0),
</span><span class="boring"> 580..=644 =&gt; (1.0, (645. - wave) / (645. - 580.), 0.0),
</span><span class="boring"> 645..=780 =&gt; (1.0, 0.0, 0.0),
</span><span class="boring"> _ =&gt; (0.0, 0.0, 0.0),
</span><span class="boring"> };
</span><span class="boring">
</span><span class="boring"> let factor = match wavelength {
</span><span class="boring"> 380..=419 =&gt; 0.3 + 0.7 * (wave - 380.) / (420. - 380.),
</span><span class="boring"> 701..=780 =&gt; 0.3 + 0.7 * (780. - wave) / (780. - 700.),
</span><span class="boring"> _ =&gt; 1.0,
</span><span class="boring"> };
</span><span class="boring">
</span><span class="boring"> let (r, g, b) = (normalize(r, factor), normalize(g, factor), normalize(b, factor));
</span><span class="boring"> Rgb::from_channels(r, g, b, 0)
</span><span class="boring">}
</span><span class="boring">
</span><span class="boring">// Maps Julia set distance estimation to intensity values
</span><span class="boring">fn julia(c: Complex&lt;f32&gt;, x: u32, y: u32, width: u32, height: u32, max_iter: u32) -&gt; u32 {
</span><span class="boring"> let width = width as f32;
</span><span class="boring"> let height = height as f32;
</span><span class="boring">
</span><span class="boring"> let mut z = Complex {
</span><span class="boring"> // scale and translate the point to image coordinates
</span><span class="boring"> re: 3.0 * (x as f32 - 0.5 * width) / width,
</span><span class="boring"> im: 2.0 * (y as f32 - 0.5 * height) / height,
</span><span class="boring"> };
</span><span class="boring">
</span><span class="boring"> let mut i = 0;
</span><span class="boring"> for t in 0..max_iter {
</span><span class="boring"> if z.norm() &gt;= 2.0 {
</span><span class="boring"> break;
</span><span class="boring"> }
</span><span class="boring"> z = z * z + c;
</span><span class="boring"> i = t;
</span><span class="boring"> }
</span><span class="boring"> i
</span><span class="boring">}
</span><span class="boring">
</span><span class="boring">// Normalizes color intensity values within RGB range
</span><span class="boring">fn normalize(color: f32, factor: f32) -&gt; u8 {
</span><span class="boring"> ((color * factor).powf(0.8) * 255.) as u8
</span><span class="boring">}
</span>
fn main() -&gt; Result&lt;()&gt; {
let (width, height) = (1920, 1080);
// 为指定宽高的输出图片分配内存
let mut img = ImageBuffer::new(width, height);
let iterations = 300;
let c = Complex::new(-0.8, 0.156);
let pool = ThreadPool::new(num_cpus::get());
let (tx, rx) = channel();
for y in 0..height {
let tx = tx.clone();
// execute 将每个像素作为单独的作业接收
pool.execute(move || for x in 0..width {
let i = julia(c, x, y, width, height, iterations);
let pixel = wavelength_to_rgb(380 + i * 400 / iterations);
tx.send((x, y, pixel)).expect(&quot;Could not send data!&quot;);
});
}
for _ in 0..(width * height) {
let (x, y, pixel) = rx.recv()?;
// 使用数据来设置像素的颜色
img.put_pixel(x, y, pixel);
}
// 输出图片内容到指定文件中
let _ = img.save(&quot;output.png&quot;)?;
Ok(())
}
</code></pre></pre>
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