Building the Right Environment to Support AI, Machine Learning and Deep Learning
This article presents a benchmark application which pits the red-black binary tree containers(map, set, etc) against hash containers. This article is not a tutorial on the data structures. For more information on how a red-black binary tree works, you can refer to this wikipedia link. For hash containers, you can read this excellent Codeguru article by Marius Bancila.
For the map and multimap benchmark, the key in key-value pair is a word(string) in English dictionary while the value in key-value pair is a random number. This is okay because the value is not used in the search. For the set and multiset benchmark, the element is a word(string) in English dictionary. Set is a data structure similar to map without the value pair. The multimap and multiset, unlike map and set, allows the same key/element to be insert more than once and doesn't enforce the uniqueness. The reason I use std::wstring for the key/element because hash containers has a predefined hash function written for std::wstring. If I use my own custom data types, I may have to write my own custom hash function.
For map, multimap, set and multiset insertion, I called the insert method. For multiset and multimap insertion, I insert the same item twice. For map and set search, I called the find method. For multiset and multimap search, I used equal_range method.
For the benchmark, we will benchmark STL, legacy hash container(more on this later), Visual C++ 10 unordered containers and Boost 1.44 unordered containers. Legacy hash containers which I have just mentioned, exists in the stdext namespace since Visual Studio 2003. We will benchmark them as well to see how they fare against the C++0x unordered containers. By the way, in case you are not aware, C++0x and Boost hash containers are called unordered_xxx(eg, unordered_map). The benchmark application will populate the containers before running the benchmark if it hasn't been populated but population time is not included in the benchmark results. The reason I did not benchmark the container population is that I found the population to be quite fast.
The benchmark used a random number to index into the std::vector for an item(string) to search in the above containers. You can change the random number generation seed in the benchmark application.
The benchmark are done over 5 million searches. The lower the score, the better it is.
We can see that the hash containers performs 2 times better than STL map. The timing for the hash containers are roughly the same.
The benchmark are also done over 5 million searches. The lower the score, the better it is.
We can see Boost unordered multimap timing is 4 times better than STL multimap.