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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： < / a > < / h1 >
< 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 >
< 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 >
< 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 >
< 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 >
< blockquote >
< 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 >
< / blockquote >
< 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：
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， < code > Main< / code > 的线程将< code > Y< / code > 设置为 3， < 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 > ， < / 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， < / 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 >
< / main >
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