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Lookup is as described above. If lookup finds no equivalent element,
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In architectures without SIMD support, the logical layout stays the same, but the metadata word is codified using a technique we call _bit interleaving_: this layout allows us to emulate SIMD with reasonably good performance using only standard arithmetic and logical operations.
在不支持 SIMD 的架构中，逻辑布局保持不变，但元数据字采用一种称为 _位交错_ 的技术进行编码：这种布局允许我们仅使用标准算术和逻辑运算，以相当不错的性能来模拟 SIMD。
image::foa-metadata-interleaving.png[align=center]
image::foa-metadata-interleaving.png[align=center]
A more detailed description of Boost.Unordered's open-addressing implementation is given in an https://bannalia.blogspot.com/2022/11/inside-boostunorderedflatmap.html[external article]. For more information on implementation rationale, read the xref:rationale.adoc#rationale_open_addresing_containers[corresponding section].
关于 Boost.Unordered 开放寻址实现的更详细描述，请参阅 https://bannalia.blogspot.com/2022/11/inside-boostunorderedflatmap.html[外部文章]。有关实现原理的更多信息，请阅读 xref:rationale.adoc#rationale_open_addresing_containers[相应章节]。
Concurrent Containers
并发容器
`boost::concurrent_flat_set`/`boost::concurrent_node_set` and `boost::concurrent_flat_map`/`boost::concurrent_node_map` use the basic xref:structures.adoc#structures_open_addressing_containers[open-addressing layout] described above augmented with synchronization mechanisms.
`boost::concurrent_flat_set`/`boost::concurrent_node_set` 和 `boost::concurrent_flat_map`/`boost::concurrent_node_map` 使用了上述基本的 xref:structures.adoc#structures_open_addressing_containers[开放寻址布局]，并增加了同步机制。
image::cfoa.png[align=center]
image::cfoa.png[align=center]
Two levels of synchronization are used:
采用两级同步机制：
Container level: A read-write mutex is used to control access from any operation
容器层面：使用读写互斥锁来控制来自任何操作的访问。
to the container. Typically, such access is in read mode (that is, concurrent) even for modifying operations, so for most practical purposes there is no thread contention at this level. Access is only in write mode (blocking) when rehashing or performing container-wide operations such as swapping or assignment.
对容器的访问。通常情况下，即使是修改操作，此类访问也处于读模式（即并发的），因此在实际使用中，此层面几乎不会发生线程争用。仅在重哈希或执行交换、赋值等容器级操作时，访问才会处于写模式（阻塞）。
Group level: Each 15-slot group is equipped with an 8-byte word containing:
组级：每个包含15个槽位的组都配备一个8字节的字，其中包含：
** A read-write spinlock for synchronized access to any element in the group. ** An atomic _insertion counter_ used for optimistic insertion as described below.
- 一个读写自旋锁，用于同步对组内任何元素的访问。
- 一个原子 _插入计数器_，用于下文所述的乐观插入。
By using atomic operations to access the group metadata, lookup is (group-level) lock-free up to the point where an actual comparison needs to be done with an element that has been previously SIMD-matched: only then is the group's spinlock used.
通过使用原子操作访问组元数据，查找操作在（组级别）上是无锁的，直到需要与实际进行 SIMD 匹配的元素进行比较时才会使用组的自旋锁。
Insertion uses the following _optimistic algorithm_:
插入操作使用以下 _乐观算法_：
The value of the insertion counter for the initial group in the probe
探测起始组的插入计数器值
sequence is locally recorded (let's call this value `c0`).
序列会被本地记录（我们称此值为 `c0`）。
Lookup is as described above. If lookup finds no equivalent element,
查找过程如上所述。如果查找未找到等效元素，
search for an available slot for insertion successively locks/unlocks each group in the probing sequence.
则会依次对探测序列中的每个组进行加锁/解锁，以搜索可用于插入的空槽位。
When an available slot is located, it is preemptively occupied (its
当定位到一个可用槽位时，该槽位会被预先占用（其
reduced hash value is set) and the insertion counter is atomically incremented: if no other thread has incremented the counter during the whole operation (which is checked by comparing with `c0`), then we're good to go and complete the insertion, otherwise we roll back and start over.
缩减哈希值被设置），并且插入计数器被原子地递增：如果在整个操作过程中没有其他线程递增该计数器（通过与 `c0` 比较来检查），则插入操作可以顺利完成，否则将回滚并重新开始。
This algorithm has very low contention both at the lookup and actual insertion phases in exchange for the possibility that computations have to be started over if some other thread interferes in the process by performing a succesful insertion beginning at the same group. In practice, the start-over frequency is extremely small, measured in the range of parts per million for some of our benchmarks.
该算法在查找和实际插入阶段都具有非常低的争用，代价是如果其他线程在同一个组上成功执行了插入操作，则当前计算可能需要重新开始。在实际应用中，重新开始的频率极低，根据我们的一些基准测试，其量级在百万分之一以内。
For more information on implementation rationale, read the xref:rationale.adoc#rationale_concurrent_containers[corresponding section].
如需了解实现原理的更多内容，请参阅 xref:rationale.adoc#rationale_concurrent_containers [对应章节]。
English Chinese (Simplified Han script) Actions
[#structures] = Data Structures
[#structures]= 数据结构
:idprefix: structures_
:idprefix: structures_
Closed-addressing Containers
闭寻址容器
image::foa-metadata-interleaving.png[align=center]
image::foa-metadata-interleaving.png[align=center]
The value of the insertion counter for the initial group in the probe
探测起始组的插入计数器值
Lookup is as described above. If lookup finds no equivalent element,
查找过程如上所述。如果查找未找到等效元素，
With a simple bucket group layout, this is all that must be done: ```c++ auto const idx = get_bucket_idx(hash_function(key)); node* n = buckets[idx]; // first load if (n) { value_type const& v = *n; // second load // ... } ```
使用简单的桶组布局，只需执行以下操作：```c++auto const idx = get_bucket_idx(hash_function(key));node* n = buckets[idx]; // 第一次加载if (n) { value_type const& v = *n; // 第二次加载 // ...}```
image::fca.png[align=center]
image::fca.png[align=center]
image::foa-metadata.png[align=center]
image::foa-metadata.png[align=center]
Concurrent Containers
并发容器
When an available slot is located, it is preemptively occupied (its
当定位到一个可用槽位时，该槽位会被预先占用（其
For more information on implementation rationale, read the xref:rationale.adoc#rationale_concurrent_containers[corresponding section].
如需了解实现原理的更多内容，请参阅 xref:rationale.adoc#rationale_concurrent_containers [对应章节]。
Boost.Unordered sports one of the fastest implementations of closed addressing, also commonly known as https://en.wikipedia.org/wiki/Hash_table#Separate_chaining[separate chaining]. An example figure representing the data structure is below:
Boost.Unordered 提供了速度最快的闭寻址实现之一，该结构通常被称为 https://en.wikipedia.org/wiki/Hash_table#Separate_chaining[分离链接法] 。其数据结构示例如下：
image::bucket-groups.png[align=center]
image::bucket-groups.png[align=center]
An array of "buckets" is allocated and each bucket in turn points to its own individual linked list. This makes meeting the standard requirements of bucket iteration straight-forward. Unfortunately, iteration of the entire container is often times slow using this layout as each bucket must be examined for occupancy, yielding a time complexity of `O(bucket_count() + size())` when the standard requires complexity to be `O(size())`.
系统分配一个"桶"数组，其中每个桶分别指向其专属的链表。这种结构使得桶迭代能直接满足标准要求。但采用此布局进行整体容器迭代通常较慢，因为需检查每个桶的占用状态，其时间复杂度为 `O(bucket++_++count() {plus} size())` ，而标准要求复杂度应为 `O(size())` 。
Canonical standard implementations will wind up looking like the diagram below:
典型的标准库实现最终会形成如下图所示的结构：
image::singly-linked.png[align=center,link=_images/singly-linked.png,window=_blank]
image::singly-linked.png[align=center,link=_images/singly-linked.png,window=_blank]
It's worth noting that this approach is only used by pass:[libc++] and pass:[libstdc++]; the MSVC Dinkumware implementation uses a different one. A more detailed analysis of the standard containers can be found http://bannalia.blogspot.com/2013/10/implementation-of-c-unordered.html[here].
值得注意的是，这种方法仅被 pass:[libc++] 和 pass:[libstdc++] 所采用；MSVC 的 Dinkumware 实现则使用了不同的方法。关于标准容器的更详细分析可参见 http://bannalia.blogspot.com/2013/10/implementation-of-c-unordered.html[此处]。
This unusually laid out data structure is chosen to make iteration of the entire container efficient by inter-connecting all of the nodes into a singly-linked list. One might also notice that buckets point to the node _before_ the start of the bucket's elements. This is done so that removing elements from the list can be done efficiently without introducing the need for a doubly-linked list. Unfortunately, this data structure introduces a guaranteed extra indirection. For example, to access the first element of a bucket, something like this must be done:
这种非常规数据结构通过将所有节点连成单向链表来实现高效全局迭代。需注意：桶指向其元素起始节点的__前驱节点__，这样设计的目的是在保持高效元素删除的同时，无需引入双向链表。但这种数据结构不可避免地带来了额外的间接访问开销。例如，要访问某个桶的第一个元素，必须执行类似以下操作：
```c++ auto const idx = get_bucket_idx(hash_function(key)); node* p = buckets[idx]; // first load node* n = p->next; // second load if (n && is_in_bucket(n, idx)) { value_type const& v = *n; // third load // ... } ```
```c++ auto const idx = get_bucket_idx(hash_function(key)); node* p = buckets[idx]; // first load node* n = p->next; // second load if (n && is_in_bucket(n, idx)) { value_type const& v = *n; // third load // ... } ```

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Lookup is as described above. If lookup finds no equivalent element,
The value of the insertion counter for the initial group in the probe查找过程如上所述。如果查找未找到等效元素，
06/05/2026
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Lookup is as described above. If lookup finds no equivalent element,
The value of the insertion counter for the initial group in the probe
06/05/2026
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doc/modules/ROOT/pages/structures_zh_Hans.adoc, string 42