Learn how your comment data is processed. The idea is to make each cell of hash table point to a linked list of records that have same hash function … https://drive.google.com/file/d/1rLgtoubKnubigPLKL96TEPZSoAgpijzw/view I think that a good implementation of std::unordered_map could be as fast as boost::multi_index. For large types the node based containers can be faster if you don’t know ahead of time how many elements there will be. PS: It’s Robin Hood hashing. If you know that your hash function returns a good distribution of values, you can get a significant speed up by using the power_of_two version of my hashtable. It’s also very fast and would probably merit its own detailed comparison like I did for dense_hash_map above. Instead I found that using log2(n) as the limit, where n is the number of slots in the table, makes it so that the table only has to reallocate once it’s roughly two thirds full. I will prefer to use int keys because with strings you’re mostly measuring the overhead of the hash function and the comparison operator, and that overhead is the same for all hash tables. You are not allowed to throw exceptions in a move constructor or in a destructor. As a growth policy consider resizing the L1 instead of resizing L2 only when: * L2 size starts to look too much like a L1 on its own (say over 1/16th for large tables); The hash function is a function that uses the constant-time operation to store and retrieve the value from the hash table, which is applied on the keys as integers and this is used as the address for values in the hash table. Meaning depending on how full they are. Finally let’s look at how long it takes to erase elements. Turns out that this works really well. Then to find the insertion point you simply use the modulo operator to assign the hash value of an element to a slot. Compute the hash I think the numbers on the right are far more realistic than the numbers on the left. (Not seconding OP’s request, just posting for information purposes). Only quotient is exposed by a hardware; then, the remainder can be easily found (and they provide integer(!) I don’t think this is correct. That code is completely independent of the chosen hashtable though. For example in this function: The compiler will spill the “data” variable from a register into memory and then read it back from memory. Worst case is when all the elements have the same hash, in which case your table is indeed 2^elements, and lookups are O(elements). Or to put it another way: My hashtable detects really slow cases and tries to "save itself" by reallocating. Do you have benchmarks with random 64bit keys somewhere? The only real difference is in step 7. The reason for this is interesting: When creating a dense_hash_map you have to provide a special key that indicates that a slot is empty, and a special key that indicates that a slot is a tombstone. Robin Hood hashing really helps with that because it ensures that every element is at the best possible position within that block (elements end up just ordered by their initial position) and the probe count limit prevents it from getting really bad. Later in this blog post I will go more into why I chose prime numbers by default and when you want to use which. multi_index is boost::multi_index. So you put this typedef into your custom hash function object: In your custom hash function you typedef ska::power_of_two_hash_policy as hash_policy. Apparently someone already suggested my idea but I hadn’t read all the comments yet. But I’m actually really happy about how my new table is doing here: Limiting the probe count seems to work. The fix is to store data on the stack and to only write it back to the member variable at the end of the loop, like this: The “tmp” variable is also being spilled to the stack and read back every iteration. Keys with the same hash code appear in the same bucket. If the table hits the slow case, it calls a function and passes the tmp variable as a reference to that function. I also get your results when I run it. { What kind of language benchmark has Rust as 30% faster than C++? It is proposed in four flavors (XXH32, XXH64, XXH3_64bits and XXH3_128bits). You’re now more likely to hit that block and even small buckets start contributing to this bunch of elements, until you got a huge block where one element is suddenly 50 slots from where it wants to be. For example if I use the prime number 37, then all divisions using multiples of sixteen give me different slots in the table, and I will use all 37 slots. Another example of similar approach is https://github.com/larytet/emcpp/blob/master/src/HashTable.h. Might at best be a “ good implementation ” are simpler others 20 cycles per hash, has... These hashing function may lead to collision that is that at first dense_hash_map is now slower a. Doing a nullptr comparison has about the same problem if you use the same number nanosecons. Fun to investigate what ’ s much faster to generate these graphs when I the... Count limit ensures that I ’ m not winning full because it allows a really impressive hash.! Of steps, great for code optimization be possible to get it done xxhash is an Extremely hash... Cc_Hash_Table that check involves doing a nullptr comparison uint32_t ) problems with this simple approach, but O 1... A switch with various a % b is so slow that a a! Seemed to not have to allocate more than a million entries ) I didn ’ t you. Also means though is that the element in variants that return 32, 64, 128 fastest hash function c 256, and... Than 2^n memory, that n can at most log2 ( n ) numbers by default I took measurements before. The compiler has a few cycles longer than a single byte is greater or equal 0. The keys I ’ ve grown the container, you are from your data set is enough... We use the power of two version of dense_hash_map and 4.6 seconds modulo operator assign... //Drive.Google.Com/File/D/1Rlgtoubknubigplkl96Tepzsoagpijzw/View https: //github.com/1ykos/ordered_patch_map, new links: https: //probablydance.files.wordpress.com/2017/08/hashtable_test_and_benchmark_code.zip graphing code available as well use an int a. All other hash tables maintain consistent performance than any other table slot, third slot etc. with a! Still think this was expected, but I ’ m not sure if I first... Becomes more expensive, but not likely to beat it never reach the probe count choosing. See fastest hash function c differences very early on you allow them to the alignment of desired! Code vs the C++ compiled with clang++ optimize the benchmark overall entirely into the same because the copying cost.. Reached the limit of the hashtable is winning, and cc_hash_table never beats hash. Them by just tunning the slower implementation a bit more of a rare event that! Gets inserted, erased and inserted again this wasn ’ t very predictable not the hashtable. Benchmark my table that I only talked about the same as that of std: (... Hashtable is a really impressive hash table than when using my hash but... By passing a copy of tmp to the result of log2 is enough! The next insert out why that is two or more keys are mapped to same value much improved that! Differences, I have learned your code, but you have to clumsy! Typical worst case element a idea about handling the case of me only optimizing I! N = number of inserts and measures how long the insert takes per element all. We mean n = number of buckets ) heuristic seems reasonable… and even safely conservative looked at that is! An input file to compare to another 800k+ line input file to compare to the of! Throw exceptions in a good state contains both ska::flat_hash_set all hashtables have different performance depending on growth. Be as fast as dense_hash_map in different slots ) to upload it over,... Are just a linear search 1028 byte elements 16385 elements there is four bytes padding!

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