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The erasure benchmarks erase all `n` elements randomly until the container is empty. Erasure by key uses `erase(const key_type&)` to remove entire groups of equivalent elements in each operation.
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:idprefix: benchmarks_
:idprefix: benchmarks_
= Benchmarks
= 基准测试
boost::unordered_[multi]set
boost::unordered_[multi]set
All benchmarks were created using `unordered_set<unsigned int>` (non-duplicate) and `unordered_multiset<unsigned int>` (duplicate). The source code can be https://github.com/boostorg/boost_unordered_benchmarks/tree/boost_unordered_set[found here^].
所有基准测试均使用 `unordered++_++set++&lt;++unsigned int++&gt;++`(非重复元素） 和 `unordered++_++multiset++&lt;++unsigned int++&gt;++` （重复元素）创建。源代码可 https://github.com/boostorg/boost_unordered_benchmarks/tree/boost_unordered_set[在此处获取] 。
The insertion benchmarks insert `n` random values, where `n` is between 10,000 and 3 million. For the duplicated benchmarks, the same random values are repeated an average of 5 times.
插入基准测试会插入 `n` 个随机值，其中 `n` 的范围在10,000至300万之间。在重复值测试场景中，同一组随机值会平均重复插入 5 次。
The erasure benchmarks erase all `n` elements randomly until the container is empty. Erasure by key uses `erase(const key_type&)` to remove entire groups of equivalent elements in each operation.
在擦除基准测试中，随机删除所有 `n` 个元素直至容器为空。通过键擦除操作使用 `erase(const key++_++type&amp;)` 方法，每次调用将移除整个等效元素组。
:14
The successful lookup benchmarks are done by looking up all `n` values, in their original insertion order.
成功查找基准测试通过按照元素的原始插入顺序查找全部 `n` 个值来完成。
:16
The unsuccessful lookup benchmarks use `n` randomly generated integers but using a different seed value.
未命中查找基准测试使用不同种子值生成的 `n` 个随机整数进行。
GCC 12 + libstdc++-v3, x64
GCC 12 + libstdc++-v3, x64
:20
Insertion
插入
image::benchmarks-set/gcc/running insertion.xlsx.practice.png[width=250,link=_images/benchmarks-set/gcc/running insertion.xlsx.practice.png,window=_blank]
image::benchmarks-set/gcc/running insertion.xlsx.practice.png[width=250,link=_images/benchmarks-set/gcc/running insertion.xlsx.practice.png,window=_blank]
image::benchmarks-set/gcc/running insertion.xlsx.practice non-unique.png[width=250,link=_images/benchmarks-set/gcc/running insertion.xlsx.practice non-unique.png,window=_blank]
image::benchmarks-set/gcc/running insertion.xlsx.practice non-unique.png[width=250,link=_images/benchmarks-set/gcc/running insertion.xlsx.practice non-unique.png,window=_blank]
image::benchmarks-set/gcc/running insertion.xlsx.practice non-unique 5.png[width=250,link=_images/benchmarks-set/gcc/running insertion.xlsx.practice non-unique 5.png,window=_blank]
image::benchmarks-set/gcc/running insertion.xlsx.practice non-unique 5.png[width=250,link=_images/benchmarks-set/gcc/running insertion.xlsx.practice non-unique 5.png,window=_blank]
h|non-duplicate elements h|duplicate elements h|duplicate elements, + max load factor 5 |===
h|非重复元素 h|重复元素 h|重复元素, + 最大负载因子 5 |===
image::benchmarks-set/gcc/running insertion.xlsx.practice norehash.png[width=250,link=_images/benchmarks-set/gcc/running insertion.xlsx.practice norehash.png,window=_blank]
image::benchmarks-set/gcc/running insertion.xlsx.practice norehash.png[width=250,link=_images/benchmarks-set/gcc/running insertion.xlsx.practice norehash.png,window=_blank]
image::benchmarks-set/gcc/running insertion.xlsx.practice norehash non-unique.png[width=250,link=_images/benchmarks-set/gcc/running insertion.xlsx.practice norehash non-unique.png,window=_blank]
image::benchmarks-set/gcc/running insertion.xlsx.practice norehash non-unique.png[width=250,link=_images/benchmarks-set/gcc/running insertion.xlsx.practice norehash non-unique.png,window=_blank]
image::benchmarks-set/gcc/running insertion.xlsx.practice norehash non-unique 5.png[width=250,link=_images/benchmarks-set/gcc/running insertion.xlsx.practice norehash non-unique 5.png,window=_blank]
image::benchmarks-set/gcc/running insertion.xlsx.practice norehash non-unique 5.png[width=250,link=_images/benchmarks-set/gcc/running insertion.xlsx.practice norehash non-unique 5.png,window=_blank]
:44
h|non-duplicate elements, + prior `reserve` h|duplicate elements, + prior `reserve` h|duplicate elements, + max load factor 5, + prior `reserve`
h|非重复元素, + 预先 `reserve` h|重复元素, + 预先 `reserve` h|重复元素, + 最大负载因子 5, + 预先 `reserve`
:54
Erasure
擦除
image::benchmarks-set/gcc/scattered erasure.xlsx.practice.png[width=250,link=_images/benchmarks-set/gcc/scattered erasure.xlsx.practice.png,window=_blank]
image::benchmarks-set/gcc/scattered erasure.xlsx.practice.png[width=250,link=_images/benchmarks-set/gcc/scattered erasure.xlsx.practice.png,window=_blank]
image::benchmarks-set/gcc/scattered erasure.xlsx.practice non-unique.png[width=250,link=_images/benchmarks-set/gcc/scattered erasure.xlsx.practice non-unique.png,window=_blank]
image::benchmarks-set/gcc/scattered erasure.xlsx.practice non-unique.png[width=250,link=_images/benchmarks-set/gcc/scattered erasure.xlsx.practice non-unique.png,window=_blank]
English Chinese (Simplified Han script) Actions
:idprefix: benchmarks_
:idprefix: benchmarks_
= Benchmarks
= 基准测试
boost::unordered_[multi]set
boost::unordered_[multi]set
All benchmarks were created using `unordered_set<unsigned int>` (non-duplicate) and `unordered_multiset<unsigned int>` (duplicate). The source code can be https://github.com/boostorg/boost_unordered_benchmarks/tree/boost_unordered_set[found here^].
所有基准测试均使用 `unordered++_++set++&lt;++unsigned int++&gt;++`(非重复元素） 和 `unordered++_++multiset++&lt;++unsigned int++&gt;++` （重复元素）创建。源代码可 https://github.com/boostorg/boost_unordered_benchmarks/tree/boost_unordered_set[在此处获取] 。
The insertion benchmarks insert `n` random values, where `n` is between 10,000 and 3 million. For the duplicated benchmarks, the same random values are repeated an average of 5 times.
插入基准测试会插入 `n` 个随机值，其中 `n` 的范围在10,000至300万之间。在重复值测试场景中，同一组随机值会平均重复插入 5 次。
The erasure benchmarks erase all `n` elements randomly until the container is empty. Erasure by key uses `erase(const key_type&)` to remove entire groups of equivalent elements in each operation.
在擦除基准测试中，随机删除所有 `n` 个元素直至容器为空。通过键擦除操作使用 `erase(const key++_++type&amp;)` 方法，每次调用将移除整个等效元素组。
`https://github.com/greg7mdp/gtl/blob/main/docs/phmap.md[gtl::parallel_flat_hash_map^]<int, int>` with 64 submaps
`https://github.com/greg7mdp/gtl/blob/main/docs/phmap.md[gtl::parallel_flat_hash_map^]<int, int="">` （含64个子映射）</int,>
`boost::concurrent_flat_map<int, int>`
`boost::concurrent_flat_map<int, int="">`</int,>
`boost::concurrent_node_map<int, int>`
`boost::concurrent_node_map<int, int="">`</int,>
The source code can be https://github.com/boostorg/boost_unordered_benchmarks/tree/boost_concurrent_flat_map[found here^].
源代码可 https://github.com/boostorg/boost_unordered_benchmarks/tree/boost_concurrent_flat_map[在此处获取] 。
The benchmarks exercise a number of threads _T_ (between 1 and 16) concurrently performing operations randomly chosen among **update**, **successful lookup** and **unsuccessful lookup**. The keys used in the operations follow a https://en.wikipedia.org/wiki/Zipf%27s_law#Formal_definition[Zipf distribution^] with different _skew_ parameters: the higher the skew, the more concentrated are the keys in the lower values of the covered range.
基准测试使用__T__个线程（数量1至16之间）并发执行随机选择的操作，这些操作包括**更新**、**成功查找**和**未命中查找**三种类型。操作使用的键遵循 https://en.wikipedia.org/wiki/Zipf%27s_law#Formal_definition[齐夫分布] ，并采用不同的__偏斜__参数：偏斜值越高，键的分布越集中在取值区间的较低数值区域。
`boost::concurrent_flat_map` and `boost::concurrent_node_map` are exercised using both regular and xref:concurrent.adoc#concurrent_bulk_visitation[bulk visitation]: in the latter case, lookup keys are buffered in a local array and then processed at once each time the buffer reaches `xref:reference/concurrent_flat_map.adoc#concurrent_flat_map_constants[bulk_visit_size]`.
`boost::concurrent_flat_map` 和 `boost::concurrent_node_map` 分别通过常规访问和 xref:concurrent.adoc#concurrent_bulk_visitation[批量访问] 进行演练：在批量访问的情况下，查找键会缓存在本地数组中，每当缓冲区大小达到 `xref:reference/concurrent_flat_map.adoc#concurrent_flat_map_constants[bulk_visit_size]` 时，再一次性处理这些键。
image::benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.500k, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.500k, 0.01.png"]
image::benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.500k, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.500k, 0.01.png"]
image::benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.500k, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.500k, 0.5.png"]
image::benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.500k, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.500k, 0.5.png"]
image::benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.500k, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.500k, 0.99.png"]
image::benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.500k, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.500k, 0.99.png"]
image::benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.5M, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.5M, 0.01.png"]
image::benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.5M, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.5M, 0.01.png"]
image::benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.5M, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.5M, 0.5.png"]
image::benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.5M, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.5M, 0.5.png"]
image::benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.5M, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.5M, 0.99.png"]
image::benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.5M, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/gcc-x64/Parallel workload.xlsx.5M, 0.99.png"]
image::benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.99.png"]
image::benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.99.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.99.png"]
image::benchmarks-concurrent_map/vs-x64/Parallel workload.xlsx.500k, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x64/Parallel workload.xlsx.500k, 0.01.png"]
image::benchmarks-concurrent_map/vs-x64/Parallel workload.xlsx.500k, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x64/Parallel workload.xlsx.500k, 0.01.png"]
image::benchmarks-concurrent_map/vs-x64/Parallel workload.xlsx.500k, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x64/Parallel workload.xlsx.500k, 0.5.png"]
image::benchmarks-concurrent_map/vs-x64/Parallel workload.xlsx.500k, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/vs-x64/Parallel workload.xlsx.500k, 0.5.png"]

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The erasure benchmarks erase all `n` elements randomly until the container is empty. Erasure by key uses `erase(const key_type&)` to remove entire groups of equivalent elements in each operation.
The erasure benchmarks erase all `n` elements randomly until the container is empty. Erasure by key uses `erase(const key_type&)` to remove entire groups of equivalent elements in each operation.在擦除基准测试中，随机删除所有 `n` 个元素直至容器为空。通过键擦除操作使用 `erase(const key++_++type&amp;)` 方法，每次调用将移除整个等效元素组。
06/05/2026
User avatar admin

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The erasure benchmarks erase all `n` elements randomly until the container is empty. Erasure by key uses `erase(const key_type&)` to remove entire groups of equivalent elements in each operation.
The erasure benchmarks erase all `n` elements randomly until the container is empty. Erasure by key uses `erase(const key_type&)` to remove entire groups of equivalent elements in each operation.
06/05/2026
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safe-html, strict-same
Unit identifier
50579
Source string location
:12
String age
06/05/2026
Last updated
06/05/2026
Source string age
06/05/2026
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doc/modules/ROOT/pages/benchmarks_zh_Hans.adoc, string 6 pending