Benchmarks are fine but they will only be loosely correlated with the measured performance for any specific use case.
There is still substantial performance to be gained by creating bespoke hashmap designs at every point of use in code. The high dimensionality of the algorithm optimization space makes it improbable that any specific hashmap algorithm implementation will optimally capture the characteristics of a use case or set of use cases. The variance can be relatively high.
It isn't uncommon to find several independent hashmap designs inside performance-engineered code bases. The sensitivity to small details makes it difficult to build excellent hashmap abstractions with broad scope.
It's also the case that performance of a hashmap, or anything, in a small-scale benchmark may not reflect the performance in a large system that does things other than manage maps. There are side effects like how many icache lines are visited during a map operation. These don't hurt microbenchmarks but they can matter in real systems. It may not be completely pointless to microbenchmark a data structure but it can be ultimately misleading.
Note that this benchmark does not include boost::unordered_flat_map. This is an open addressing variant of boost::unordered_map which has only been released in December 2022.
I wanted to mention this because boost::unordered_flat_map and boost::unordered_flat_set are among the fastest open addressing hash containers in C++ land. Internally, they use lots of cool SIMD tricks. If anyone is interested in the details, here's a nice blog post by the developer: https://bannalia.blogspot.com/2022/11/inside-boostunorderedf...
There is still substantial performance to be gained by creating bespoke hashmap designs at every point of use in code. The high dimensionality of the algorithm optimization space makes it improbable that any specific hashmap algorithm implementation will optimally capture the characteristics of a use case or set of use cases. The variance can be relatively high.
It isn't uncommon to find several independent hashmap designs inside performance-engineered code bases. The sensitivity to small details makes it difficult to build excellent hashmap abstractions with broad scope.
I wanted to mention this because boost::unordered_flat_map and boost::unordered_flat_set are among the fastest open addressing hash containers in C++ land. Internally, they use lots of cool SIMD tricks. If anyone is interested in the details, here's a nice blog post by the developer: https://bannalia.blogspot.com/2022/11/inside-boostunorderedf...
https://doc.qt.io/qt-6/qhash.html
> We encourage phmap users to switch to gtl if possible. gtl provides the same functionality as this repository, but requires C++20 or above.
And the benchmarks do include gtl.