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19 :181

	English Traversal can be interrupted midway: 36/10000 · 36
20 :202

	English There is one last whole-table visitation operation, `erase_if`: 63/10000 · 63
21 :211

	English `visit_while` and `erase_if` can also be parallelized. Note that, in order to increase efficiency, whole-table visitation operations do not block the table during execution: this implies that elements may be inserted, modified or erased by other threads during visitation. It is advisable not to assume too much about the exact global state of a concurrent container at any point in your program. 396/10000 · 396
22 :217

	English Bulk visitation 15/10000 · 15
23 :219

	English Suppose you have an `std::array` of keys you want to look up for in a concurrent map: 85/10000 · 85
24 :230

	English _Bulk visitation_ allows us to pass all the keys in one operation: 66/10000 · 66
25 :237

	English This functionality is not provided for mere syntactic convenience, though: by processing all the keys at once, some internal optimizations can be applied that increase performance over the regular, one-at-a-time case (consult the xref:benchmarks.adoc#benchmarks_boostconcurrent_flatnode_map[benchmarks]). In fact, it may be beneficial to buffer incoming keys so that they can be bulk visited in chunks: 402/10000 · 402
26 :261

	English There's a latency/throughput tradeoff here: it will take longer for incoming keys to be processed (since they are buffered), but the number of processed keys per second is higher. `bulk_visit_size` is the recommended chunk size —smaller buffers may yield worse performance. 273/10000 · 273
27 :266

	English Blocking Operations 19/10000 · 19
28 :268

	English Concurrent containers can be copied, assigned, cleared and merged just like any other Boost.Unordered container. Unlike most other operations, these are _blocking_, that is, all other threads are prevented from accesing the tables involved while a copy, assignment, clear or merge operation is in progress. Blocking is taken care of automatically by the library and the user need not take any special precaution, but overall performance may be affected. 453/10000 · 453
29 :274

	English Another blocking operation is _rehashing_, which happens explicitly via `rehash`/`reserve` or during insertion when the table's load hits `max_load()`. As with non-concurrent containers, reserving space in advance of bulk insertions will generally speed up the process. 269/10000 · 269
30 :278

	English Interoperability with non-concurrent containers 47/10000 · 47
31 :280

	English As open-addressing and concurrent containers are based on the same internal data structure, they can be efficiently move-constructed from their non-concurrent counterpart, and vice versa. 187/10000 · 187
32 :286

	English ^\|`boost::concurrent_node_set` ^\|`boost::unordered_node_set` 60/10000 · 60
33 :289

	English ^\|`boost::concurrent_node_map` ^\|`boost::unordered_node_map` 60/10000 · 60
34 :292

	English ^\|`boost::concurrent_flat_set` ^\|`boost::unordered_flat_set` 60/10000 · 60
35 :295

	English ^\|`boost::concurrent_flat_map` ^\|`boost::unordered_flat_map` 60/10000 · 60
36 :300

	English This interoperability comes handy in multistage scenarios where parts of the data processing happen in parallel whereas other steps are non-concurrent (or non-modifying). In the following example, we want to construct a histogram from a huge input vector of words: the population phase can be done in parallel with `boost::concurrent_flat_map` and results then transferred to the final container. 396/10000 · 396
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