C++ how to improve unordered_set perform with 5 million

To improve the performance of an unordered_set with 5 million elements in C++, you can consider the following optimizations:

1. Use a custom hash function: By providing a custom hash function that has a good distribution for your specific data, you can improve the performance of the unordered set. This can help reduce collisions and improve lookup times.

2. Reserve enough space: Before inserting elements into the unordered set, you can call the reserve function to allocate enough memory to avoid unnecessary rehashing. Since you know that you will have 5 million elements, reserving space for this number can improve performance.

3. Use emplace instead of insert: If you are inserting new elements into the unordered set, consider using the emplace function instead of insert. emplace avoids creating temporary objects and can be more efficient.

4. Choose the right load factor: The load factor is the ratio of elements to buckets in the unordered set. By carefully selecting the load factor, you can balance memory usage and lookup performance. A higher load factor may reduce memory usage but increase the chance of collisions, while a lower load factor may increase memory usage but improve lookup times.

5. Use a faster hash function: If the default hash function provided by the standard library is not performing well for your data, you can consider using a faster hash function implementation, such as xxHash or MurmurHash.

6. Parallelize operations: If your operations on the unordered set can be done in parallel, you can consider using parallel algorithms or multi-threading to improve performance. Be careful with thread safety and ensure proper synchronization.

7. Profile and optimize: Profile your code using performance analysis tools to identify bottlenecks and optimize the critical sections. Sometimes, small changes like avoiding unnecessary copies or optimizing loops can significantly improve performance.

Remember that the performance improvements may vary depending on the specific use case, data characteristics, and hardware. So it's essential to measure the impact of each optimization and choose the ones that provide the most significant benefit