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In the example above, threads access `m` without synchronization, just as we'd do in a single-threaded scenario. In an ideal setting, if a given workload is distributed among _N_ threads, execution is _N_ times faster than with one thread —this limit is never attained in practice due to synchronization overheads and _contention_ (one thread waiting for another to leave a locked portion of the map), but Boost.Unordered concurrent containers are designed to perform with very little overhead and typically achieve _linear scaling_ (that is, performance is proportional to the number of threads up to the number of logical cores in the CPU).
In the example above, threads access `m` without synchronization, just as we'd do in a single-threaded scenario. In an ideal setting, if a given workload is distributed among _N_ threads, execution is _N_ times faster than with one thread —this limit is never attained in practice due to synchronization overheads and _contention_ (one thread waiting for another to leave a locked portion of the map), but Boost.Unordered concurrent containers are designed to perform with very little overhead and typically achieve _linear scaling_ (that is, performance is proportional to the number of threads up to the number of logical cores in the CPU).
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In the example above, threads access `m` without synchronization, just as we'd do in a single-threaded scenario. In an ideal setting, if a given workload is distributed among _N_ threads, execution is _N_ times faster than with one thread —this limit is never attained in practice due to synchronization overheads and _contention_ (one thread waiting for another to leave a locked portion of the map), but Boost.Unordered concurrent containers are designed to perform with very little overhead and typically achieve _linear scaling_ (that is, performance is proportional to the number of threads up to the number of logical cores in the CPU).在上例中，线程无需同步即可访问 `m` ，这与单线程场景中的操作方式一致。在理想情况下，若将给定工作负载分配给 _N_ 个线程，其执行速度相较单线程提升 _N_ 倍——由于同步开销与__争用__的存在（某线程等待其他线程离开容器的锁定区域），实践中无法达到此理论极限。但 Boost.Unordered 并发容器设计为以极低开销运行，通常可实现__线性扩展__（即性能提升与线程数量成正比，直至达到 CPU 的逻辑核心数）。