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The source code can be https://github.com/boostorg/boost_unordered_benchmarks/tree/boost_concurrent_flat_map[found here^].
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image::benchmarks-flat_map/clang-x86/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x86/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/clang-x86/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x86/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/clang-x86/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x86/Scattered unsuccessful looukp.xlsx.plot.png]
image::benchmarks-flat_map/clang-x86/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x86/Scattered unsuccessful looukp.xlsx.plot.png]
:435
h|running insertion h|running erasure h|successful lookup h|unsuccessful lookup
h|插入的执行 h|擦除的执行 h|成功查找 h|未命中查找
:442
Visual Studio 2022, x86
Visual Studio 2022, x86
image::benchmarks-flat_map/vs-x86/Running insertion.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x86/Running insertion.xlsx.plot.png]
image::benchmarks-flat_map/vs-x86/Running insertion.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x86/Running insertion.xlsx.plot.png]
image::benchmarks-flat_map/vs-x86/Running erasure.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x86/Running erasure.xlsx.plot.png]
image::benchmarks-flat_map/vs-x86/Running erasure.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x86/Running erasure.xlsx.plot.png]
image::benchmarks-flat_map/vs-x86/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x86/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/vs-x86/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x86/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/vs-x86/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x86/Scattered unsuccessful looukp.xlsx.plot.png]
image::benchmarks-flat_map/vs-x86/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x86/Scattered unsuccessful looukp.xlsx.plot.png]
:454
h|running insertion h|running erasure h|successful lookup h|unsuccessful lookup
h|插入的执行 h|擦除的执行 h|成功查找 h|未命中查找
boost::concurrent_(flat|node)_map
boost::concurrent_(flat|node)_map
:463
All benchmarks were created using:
所有基准测试均为使用以下链接来创建的：
`https://spec.oneapi.io/versions/latest/elements/oneTBB/source/containers/concurrent_hash_map_cls.html[oneapi::tbb::concurrent_hash_map^]<int, int>`
`https://spec.oneapi.io/versions/latest/elements/oneTBB/source/containers/concurrent_hash_map_cls.html[oneapi::tbb::concurrent_hash_map^]<int, int="">`</int,>
`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。
:482
GCC 12, x64
GCC 12, x64
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"]
:493
h|500k updates, 4.5M lookups + skew=0.01 h|500k updates, 4.5M lookups + skew=0.5 h|500k updates, 4.5M lookups + skew=0.99 |===
h|50万次更新, 450万次查找 + 偏斜=0.01 h|50万次更新, 450万次查找 + 偏斜=0.5 h|50万次更新, 450万次查找 + 偏斜=0.99 |===
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"]
:509
h|5M updates, 45M lookups + skew=0.01 h|5M updates, 45M lookups + skew=0.5 h|5M updates, 45M lookups + skew=0.99 |===
h|500万次更新, 4500万次查找 + 偏斜=0.01 h|500万次更新, 4500万次查找 + 偏斜=0.5 h|500万次更新, 4500万次查找 + 偏斜=0.99 |===
:517
Clang 15, x64
Clang 15, x64
image::benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.01.png"]
image::benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.01.png"]
image::benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.5.png"]
image::benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.5.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"]
English Chinese (Simplified Han script) Actions
image::benchmarks-flat_map/clang-x64/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x64/Scattered unsuccessful looukp.xlsx.plot.png]
image::benchmarks-flat_map/clang-x64/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x64/Scattered unsuccessful looukp.xlsx.plot.png]
image::benchmarks-flat_map/vs-x64/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x64/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/vs-x64/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x64/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/vs-x64/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x64/Scattered unsuccessful looukp.xlsx.plot.png]
image::benchmarks-flat_map/vs-x64/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x64/Scattered unsuccessful looukp.xlsx.plot.png]
image::benchmarks-flat_map/clang-arm64/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-arm64/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/clang-arm64/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-arm64/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/clang-arm64/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-arm64/Scattered unsuccessful looukp.xlsx.plot.png]
image::benchmarks-flat_map/clang-arm64/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-arm64/Scattered unsuccessful looukp.xlsx.plot.png]
image::benchmarks-flat_map/gcc-x86/Running insertion.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x86/Running insertion.xlsx.plot.png]
image::benchmarks-flat_map/gcc-x86/Running insertion.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x86/Running insertion.xlsx.plot.png]
image::benchmarks-flat_map/gcc-x86/Running erasure.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x86/Running erasure.xlsx.plot.png]
image::benchmarks-flat_map/gcc-x86/Running erasure.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x86/Running erasure.xlsx.plot.png]
image::benchmarks-flat_map/gcc-x86/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x86/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/gcc-x86/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x86/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/gcc-x86/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x86/Scattered unsuccessful looukp.xlsx.plot.png]
image::benchmarks-flat_map/gcc-x86/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/gcc-x86/Scattered unsuccessful looukp.xlsx.plot.png]
image::benchmarks-flat_map/clang-x86/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x86/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/clang-x86/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x86/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/clang-x86/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x86/Scattered unsuccessful looukp.xlsx.plot.png]
image::benchmarks-flat_map/clang-x86/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/clang-x86/Scattered unsuccessful looukp.xlsx.plot.png]
image::benchmarks-flat_map/vs-x86/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x86/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/vs-x86/Scattered successful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x86/Scattered successful looukp.xlsx.plot.png]
image::benchmarks-flat_map/vs-x86/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x86/Scattered unsuccessful looukp.xlsx.plot.png]
image::benchmarks-flat_map/vs-x86/Scattered unsuccessful looukp.xlsx.plot.png[width=250,window=_blank,link=_images/benchmarks-flat_map/vs-x86/Scattered unsuccessful looukp.xlsx.plot.png]
`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_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, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.01.png"]
image::benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.01.png"]
image::benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.5.png"]
image::benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.5.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.500k, 0.5.png"]
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image::benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.5M, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.5M, 0.01.png"]
image::benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.5M, 0.01.png[width=250,window=_blank,link="_images/benchmarks-concurrent_map/clang-x64/Parallel workload.xlsx.5M, 0.01.png"]
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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"]

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The source code can be https://github.com/boostorg/boost_unordered_benchmarks/tree/boost_concurrent_flat_map[found here^].
The source code can be源代码可 https://github.com/boostorg/boost_unordered_benchmarks/tree/boost_concurrent_flat_map[found here^].在此处获取] 。
06/05/2026
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The source code can be https://github.com/boostorg/boost_unordered_benchmarks/tree/boost_concurrent_flat_map[found here^].
The source code can be https://github.com/boostorg/boost_unordered_benchmarks/tree/boost_concurrent_flat_map[found here^].
06/05/2026
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safe-html, strict-same
Unit identifier
50725
Source string location
:470
String age
06/05/2026
Last updated
06/06/2026
Source string age
06/05/2026
Translation file AsciiDoc file
doc/modules/ROOT/pages/benchmarks_zh_Hans.adoc, string 152