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<main>
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<h1 id="线程同步atomic-原子类型与内存顺序"><a class="header" href="#线程同步atomic-原子类型与内存顺序">线程同步：Atomic 原子类型与内存顺序</a></h1>
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<p><code>Mutex</code>用起来简单，但是无法并发读，<code>RwLock</code>可以并发读，但是使用场景较为受限且性能不够，那么有没有一种全能性选手呢？ 欢迎我们的<code>Atomic</code>闪亮登场。</p>
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<p>从 Rust1.34 版本后，就正式支持原子类型。原子指的是一系列不可被 CPU 上下文交换的机器指令，这些指令组合在一起就形成了原子操作。在多核 CPU 下，当某个 CPU 核心开始运行原子操作时，会先暂停其它 CPU 内核对内存的操作，以保证原子操作不会被其它 CPU 内核所干扰。</p>
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<p>由于原子操作是通过指令提供的支持，因此它的性能相比锁和消息传递会好很多。相比较于锁而言，原子类型不需要开发者处理加锁和释放锁的问题，同时支持修改，读取等操作，还具备较高的并发性能，几乎所有的语言都支持原子类型。</p>
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<p>可以看出原子类型是无锁类型，但是无锁不代表无需等待，因为原子类型内部使用了<code>CAS</code>循环，当大量的冲突发生时，该等待还是得<a href="https://course.rs/advance/concurrency-with-threads/thread.html#多线程的开销">等待</a>！但是总归比锁要好。</p>
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<blockquote>
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<p>CAS 全称是 Compare and swap, 它通过一条指令读取指定的内存地址，然后判断其中的值是否等于给定的前置值，如果相等，则将其修改为新的值</p>
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</blockquote>
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<h2 id="使用-atomic-作为全局变量"><a class="header" href="#使用-atomic-作为全局变量">使用 Atomic 作为全局变量</a></h2>
|
|
|
<p>原子类型的一个常用场景，就是作为全局变量来使用:</p>
|
|
|
<pre class="playground"><code class="language-rust edition2021">use std::ops::Sub;
|
|
|
use std::sync::atomic::{AtomicU64, Ordering};
|
|
|
use std::thread::{self, JoinHandle};
|
|
|
use std::time::Instant;
|
|
|
|
|
|
const N_TIMES: u64 = 10000000;
|
|
|
const N_THREADS: usize = 10;
|
|
|
|
|
|
static R: AtomicU64 = AtomicU64::new(0);
|
|
|
|
|
|
fn add_n_times(n: u64) -> JoinHandle<()> {
|
|
|
thread::spawn(move || {
|
|
|
for _ in 0..n {
|
|
|
R.fetch_add(1, Ordering::Relaxed);
|
|
|
}
|
|
|
})
|
|
|
}
|
|
|
|
|
|
fn main() {
|
|
|
let s = Instant::now();
|
|
|
let mut threads = Vec::with_capacity(N_THREADS);
|
|
|
|
|
|
for _ in 0..N_THREADS {
|
|
|
threads.push(add_n_times(N_TIMES));
|
|
|
}
|
|
|
|
|
|
for thread in threads {
|
|
|
thread.join().unwrap();
|
|
|
}
|
|
|
|
|
|
assert_eq!(N_TIMES * N_THREADS as u64, R.load(Ordering::Relaxed));
|
|
|
|
|
|
println!("{:?}",Instant::now().sub(s));
|
|
|
}</code></pre>
|
|
|
<p>以上代码启动了数个线程，每个线程都在疯狂对全局变量进行加 1 操作, 最后将它与<code>线程数 * 加1次数</code>进行比较，如果发生了因为多个线程同时修改导致了脏数据，那么这两个必将不相等。好在，它没有让我们失望，不仅快速的完成了任务，而且保证了 100%的并发安全性。</p>
|
|
|
<p>当然以上代码的功能其实也可以通过<code>Mutex</code>来实现，但是后者的强大功能是建立在额外的性能损耗基础上的，因此性能会逊色不少:</p>
|
|
|
<pre><code class="language-console">Atomic实现：673ms
|
|
|
Mutex实现: 1136ms
|
|
|
</code></pre>
|
|
|
<p>可以看到<code>Atomic</code>实现会比<code>Mutex</code>快<strong>41%</strong>，实际上在复杂场景下还能更快(甚至达到 4 倍的性能差距)！</p>
|
|
|
<p>还有一点值得注意: <strong>和<code>Mutex</code>一样，<code>Atomic</code>的值具有内部可变性</strong>，你无需将其声明为<code>mut</code>：</p>
|
|
|
<pre class="playground"><code class="language-rust edition2021">use std::sync::Mutex;
|
|
|
use std::sync::atomic::{Ordering, AtomicU64};
|
|
|
|
|
|
struct Counter {
|
|
|
count: u64
|
|
|
}
|
|
|
|
|
|
fn main() {
|
|
|
let n = Mutex::new(Counter {
|
|
|
count: 0
|
|
|
});
|
|
|
|
|
|
n.lock().unwrap().count += 1;
|
|
|
|
|
|
let n = AtomicU64::new(0);
|
|
|
|
|
|
n.fetch_add(0, Ordering::Relaxed);
|
|
|
}</code></pre>
|
|
|
<p>这里有一个奇怪的枚举成员<code>Ordering::Relaxed</code>, 看上去很像是排序作用，但是我们并没有做排序操作啊？实际上它用于控制原子操作使用的<strong>内存顺序</strong>。</p>
|
|
|
<h2 id="内存顺序"><a class="header" href="#内存顺序">内存顺序</a></h2>
|
|
|
<p>内存顺序是指 CPU 在访问内存时的顺序，该顺序可能受以下因素的影响：</p>
|
|
|
<ul>
|
|
|
<li>代码中的先后顺序</li>
|
|
|
<li>编译器优化导致在编译阶段发生改变(内存重排序 reordering)</li>
|
|
|
<li>运行阶段因 CPU 的缓存机制导致顺序被打乱</li>
|
|
|
</ul>
|
|
|
<h4 id="编译器优化导致内存顺序的改变"><a class="header" href="#编译器优化导致内存顺序的改变">编译器优化导致内存顺序的改变</a></h4>
|
|
|
<p>对于第二点，我们举个例子：</p>
|
|
|
<pre class="playground"><code class="language-rust edition2021">static mut X: u64 = 0;
|
|
|
static mut Y: u64 = 1;
|
|
|
|
|
|
fn main() {
|
|
|
... // A
|
|
|
|
|
|
unsafe {
|
|
|
... // B
|
|
|
X = 1;
|
|
|
... // C
|
|
|
Y = 3;
|
|
|
... // D
|
|
|
X = 2;
|
|
|
... // E
|
|
|
}
|
|
|
}</code></pre>
|
|
|
<p>假如在<code>C</code>和<code>D</code>代码片段中，根本没有用到<code>X = 1</code>，那么编译器很可能会将<code>X = 1</code>和<code>X = 2</code>进行合并:</p>
|
|
|
<pre class="playground"><code class="language-rust edition2021"><span class="boring">#![allow(unused)]
|
|
|
</span><span class="boring">fn main() {
|
|
|
</span> ... // A
|
|
|
|
|
|
unsafe {
|
|
|
... // B
|
|
|
X = 2;
|
|
|
... // C
|
|
|
Y = 3;
|
|
|
... // D
|
|
|
... // E
|
|
|
}
|
|
|
<span class="boring">}</span></code></pre>
|
|
|
<p>若代码<code>A</code>中创建了一个新的线程用于读取全局静态变量<code>X</code>，则该线程将无法读取到<code>X = 1</code>的结果，因为在编译阶段就已经被优化掉。</p>
|
|
|
<h4 id="cpu-缓存导致的内存顺序的改变"><a class="header" href="#cpu-缓存导致的内存顺序的改变">CPU 缓存导致的内存顺序的改变</a></h4>
|
|
|
<p>假设之前的<code>X = 1</code>没有被优化掉，并且在代码片段<code>A</code>中有一个新的线程:</p>
|
|
|
<pre><code class="language-console">initial state: X = 0, Y = 1
|
|
|
|
|
|
THREAD Main THREAD A
|
|
|
X = 1; if X == 1 {
|
|
|
Y = 3; Y *= 2;
|
|
|
X = 2; }
|
|
|
</code></pre>
|
|
|
<p>我们来讨论下以上线程状态，<code>Y</code>最终的可能值(可能性依次降低):</p>
|
|
|
<ul>
|
|
|
<li><code>Y = 3</code>: 线程<code>Main</code>运行完后才运行线程<code>A</code>，或者线程<code>A</code>运行完后再运行线程<code>Main</code></li>
|
|
|
<li><code>Y = 6</code>: 线程<code>Main</code>的<code>Y = 3</code>运行完，但<code>X = 2</code>还没被运行， 此时线程 A 开始运行<code>Y *= 2</code>, 最后才运行<code>Main</code>线程的<code>X = 2</code></li>
|
|
|
<li><code>Y = 2</code>: 线程<code>Main</code>正在运行<code>Y = 3</code>还没结束，此时线程<code>A</code>正在运行<code>Y *= 2</code>, 因此<code>Y</code>取到了值 1，然后<code>Main</code>的线程将<code>Y</code>设置为 3， 紧接着就被线程<code>A</code>的<code>Y = 2</code>所覆盖</li>
|
|
|
<li><code>Y = 2</code>: 上面的还只是一般的数据竞争，这里虽然产生了相同的结果<code>2</code>，但是背后的原理大相径庭: 线程<code>Main</code>运行完<code>Y = 3</code>，但是 CPU 缓存中的<code>Y = 3</code>还没有被同步到其它 CPU 缓存中，此时线程<code>A</code>中的<code>Y *= 2</code>就开始读取<code>Y</code>，结果读到了值<code>1</code>，最终计算出结果<code>2</code></li>
|
|
|
</ul>
|
|
|
<p>甚至更改成:</p>
|
|
|
<pre><code class="language-console">initial state: X = 0, Y = 1
|
|
|
|
|
|
THREAD Main THREAD A
|
|
|
X = 1; if X == 2 {
|
|
|
Y = 3; Y *= 2;
|
|
|
X = 2; }
|
|
|
</code></pre>
|
|
|
<p>还是可能出现<code>Y = 2</code>，因为<code>Main</code>线程中的<code>X</code>和<code>Y</code>被同步到其它 CPU 缓存中的顺序未必一致。</p>
|
|
|
<h4 id="限定内存顺序的-5-个规则"><a class="header" href="#限定内存顺序的-5-个规则">限定内存顺序的 5 个规则</a></h4>
|
|
|
<p>在理解了内存顺序可能存在的改变后，你就可以明白为什么 Rust 提供了<code>Ordering::Relaxed</code>用于限定内存顺序了，事实上，该枚举有 5 个成员:</p>
|
|
|
<ul>
|
|
|
<li><strong>Relaxed</strong>， 这是最宽松的规则，它对编译器和 CPU 不做任何限制，可以乱序</li>
|
|
|
<li><strong>Release 释放</strong>，设定内存屏障(Memory barrier)，保证它之前的操作永远在它之前，但是它后面的操作可能被重排到它前面</li>
|
|
|
<li><strong>Acquire 获取</strong>, 设定内存屏障，保证在它之后的访问永远在它之后，但是它之前的操作却有可能被重排到它后面，往往和<code>Release</code>在不同线程中联合使用</li>
|
|
|
<li><strong>AcqRel</strong>, 是 <em>Acquire</em> 和 <em>Release</em> 的结合，同时拥有它们俩提供的保证。比如你要对一个 <code>atomic</code> 自增 1，同时希望该操作之前和之后的读取或写入操作不会被重新排序</li>
|
|
|
<li><strong>SeqCst 顺序一致性</strong>， <code>SeqCst</code>就像是<code>AcqRel</code>的加强版，它不管原子操作是属于读取还是写入的操作，只要某个线程有用到<code>SeqCst</code>的原子操作，线程中该<code>SeqCst</code>操作前的数据操作绝对不会被重新排在该<code>SeqCst</code>操作之后，且该<code>SeqCst</code>操作后的数据操作也绝对不会被重新排在<code>SeqCst</code>操作前。</li>
|
|
|
</ul>
|
|
|
<p>这些规则由于是系统提供的，因此其它语言提供的相应规则也大同小异，大家如果不明白可以看看其它语言的相关解释。</p>
|
|
|
<h4 id="内存屏障的例子"><a class="header" href="#内存屏障的例子">内存屏障的例子</a></h4>
|
|
|
<p>下面我们以<code>Release</code>和<code>Acquire</code>为例，使用它们构筑出一对内存屏障，防止编译器和 CPU 将屏障前(Release)和屏障后(Acquire)中的数据操作重新排在屏障围成的范围之外:</p>
|
|
|
<pre class="playground"><code class="language-rust edition2021">use std::thread::{self, JoinHandle};
|
|
|
use std::sync::atomic::{Ordering, AtomicBool};
|
|
|
|
|
|
static mut DATA: u64 = 0;
|
|
|
static READY: AtomicBool = AtomicBool::new(false);
|
|
|
|
|
|
fn reset() {
|
|
|
unsafe {
|
|
|
DATA = 0;
|
|
|
}
|
|
|
READY.store(false, Ordering::Relaxed);
|
|
|
}
|
|
|
|
|
|
fn producer() -> JoinHandle<()> {
|
|
|
thread::spawn(move || {
|
|
|
unsafe {
|
|
|
DATA = 100; // A
|
|
|
}
|
|
|
READY.store(true, Ordering::Release); // B: 内存屏障 ↑
|
|
|
})
|
|
|
}
|
|
|
|
|
|
fn consumer() -> JoinHandle<()> {
|
|
|
thread::spawn(move || {
|
|
|
while !READY.load(Ordering::Acquire) {} // C: 内存屏障 ↓
|
|
|
|
|
|
assert_eq!(100, unsafe { DATA }); // D
|
|
|
})
|
|
|
}
|
|
|
|
|
|
|
|
|
fn main() {
|
|
|
loop {
|
|
|
reset();
|
|
|
|
|
|
let t_producer = producer();
|
|
|
let t_consumer = consumer();
|
|
|
|
|
|
t_producer.join().unwrap();
|
|
|
t_consumer.join().unwrap();
|
|
|
}
|
|
|
}</code></pre>
|
|
|
<p>原则上，<code>Acquire</code>用于读取，而<code>Release</code>用于写入。但是由于有些原子操作同时拥有读取和写入的功能，此时就需要使用<code>AcqRel</code>来设置内存顺序了。在内存屏障中被写入的数据，都可以被其它线程读取到，不会有 CPU 缓存的问题。</p>
|
|
|
<p><strong>内存顺序的选择</strong></p>
|
|
|
<ol>
|
|
|
<li>不知道怎么选择时，优先使用<code>SeqCst</code>，虽然会稍微减慢速度，但是慢一点也比出现错误好</li>
|
|
|
<li>多线程只计数<code>fetch_add</code>而不使用该值触发其他逻辑分支的简单使用场景，可以使用<code>Relaxed</code><br>参考 <a href="https://stackoverflow.com/questions/30407121/which-stdsyncatomicordering-to-use">Which std::sync::atomic::Ordering to use?</a></li>
|
|
|
</ol>
|
|
|
<h2 id="多线程中使用-atomic"><a class="header" href="#多线程中使用-atomic">多线程中使用 Atomic</a></h2>
|
|
|
<p>在多线程环境中要使用<code>Atomic</code>需要配合<code>Arc</code>：</p>
|
|
|
<pre class="playground"><code class="language-rust edition2021">use std::sync::Arc;
|
|
|
use std::sync::atomic::{AtomicUsize, Ordering};
|
|
|
use std::{hint, thread};
|
|
|
|
|
|
fn main() {
|
|
|
let spinlock = Arc::new(AtomicUsize::new(1));
|
|
|
|
|
|
let spinlock_clone = Arc::clone(&spinlock);
|
|
|
let thread = thread::spawn(move|| {
|
|
|
spinlock_clone.store(0, Ordering::SeqCst);
|
|
|
});
|
|
|
|
|
|
// 等待其它线程释放锁
|
|
|
while spinlock.load(Ordering::SeqCst) != 0 {
|
|
|
hint::spin_loop();
|
|
|
}
|
|
|
|
|
|
if let Err(panic) = thread.join() {
|
|
|
println!("Thread had an error: {:?}", panic);
|
|
|
}
|
|
|
}</code></pre>
|
|
|
<h2 id="atomic-能替代锁吗"><a class="header" href="#atomic-能替代锁吗">Atomic 能替代锁吗</a></h2>
|
|
|
<p>那么原子类型既然这么全能，它可以替代锁吗？答案是不行：</p>
|
|
|
<ul>
|
|
|
<li>对于复杂的场景下，锁的使用简单粗暴，不容易有坑</li>
|
|
|
<li><code>std::sync::atomic</code>包中仅提供了数值类型的原子操作：<code>AtomicBool</code>, <code>AtomicIsize</code>, <code>AtomicUsize</code>, <code>AtomicI8</code>, <code>AtomicU16</code>等，而锁可以应用于各种类型</li>
|
|
|
<li>在有些情况下，必须使用锁来配合，例如上一章节中使用<code>Mutex</code>配合<code>Condvar</code></li>
|
|
|
</ul>
|
|
|
<h2 id="atomic-的应用场景"><a class="header" href="#atomic-的应用场景">Atomic 的应用场景</a></h2>
|
|
|
<p>事实上，<code>Atomic</code>虽然对于用户不太常用，但是对于高性能库的开发者、标准库开发者都非常常用，它是并发原语的基石，除此之外，还有一些场景适用：</p>
|
|
|
<ul>
|
|
|
<li>无锁(lock free)数据结构</li>
|
|
|
<li>全局变量，例如全局自增 ID, 在后续章节会介绍</li>
|
|
|
<li>跨线程计数器，例如可以用于统计指标</li>
|
|
|
</ul>
|
|
|
<p>以上列出的只是<code>Atomic</code>适用的部分场景，具体场景需要大家未来根据自己的需求进行权衡选择。</p>
|
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|
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|
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