I can’t put a number on the certainty, but I ran a simple test where I built thousands of tables of all kinds of sizes and filled them with random integers. Meaning the table will grow when its half full, even when it hasn’t reached the limit of the probe count. Just after the main text but before comments, find the line: I’ve thought about changing my memory layout to solve this, but my worry is that then I would have two cache misses for each lookup instead of one cache miss. I’m kinda done with this benchmark. What we see here is that using a string is just moving all lines up a little. Lesson learned from this: If you have a large type, inserts will be equally slow in all tables, you should call reserve ahead of time, and the node based containers are a much more competitive option for large types than they are for small types. It’s definitely the fastest for lookups, and it’s also really fast for insert and erase operations. Except it gets slower when the table gets very big. Why do you want to iterate over buckets? Sure, if you don’t have too many collisions. Do you know why there is such a difference on the original test? And if you throw in a move constructor, I don’t know how to do that. Whenever a node is free’d, I reuse that node’s memory as a pointer in a linked list of available memory. You will also learn various concepts of hashing like hash table, hash function, etc. time(erase): 47 Intuitively you would expect that if you don’t have a very slow hash function, step 3 is the most expensive of these three. In hash table, the data is stored in an array format where each data value has its own unique index value. Then that cost gets amortized until the table has to reallocate again. Does that mean that I’m immune against problems that come from using powers of two? ht.erase (prev); This is correct code with unordered_map, but causes corruption with flat_hash_map, because it doesn’t have the same iterator stability guarantees as std::unordered_map, and obviously itertaor invalidation rules are a very important part of the interface. SipHash is a fast but 'cryptographically strong' pseudo-random function byAumasson and Bernstein [https://www.131002.net/siphash/siphash.pdf]. This site uses Akismet to reduce spam. This package provides two 'strong' (well-distributed andunpredictable) hash functions: a faster version of SipHash, and an even fasteralgorithm we call HighwayHash. Creating a Fast Hash Function. I responded on to Matthew Kulukundis on YouTube where he did a presentation, not sure if it is a similar implementation, but the idea I have is generic enough for you to play with. The first graph is looking up an element that’s in the table: This is a pretty dense graph so let’s spend some time on this one. Searching is dominant operation on any data structure. The only real difference is in step 7. g++ -O3 -std=c++14 knucleotide.cpp -o knucleotide -lpthread This one is the GCC unordered_map. And I gave a bit more of a response to Matthew Kulukundis above. Division method. The next thing I tried to vary was the size of the value. Enjoy! Measuring the impact of that is a bit difficult, but I believe I have found a test that works for this purpose: I insert and erase elements over and over again: The way this test works is that I first generate a million random ints. Especially since linear probing actually makes it pretty likely that you will hit the worst case since linear probing tends to bunch elements together. https://1ykos.github.io/patchmap/. But all other hash operations will still pretend that I only have 1009 slots. Change ), You are commenting using your Facebook account. The cost of reallocations kills the flat containers. For example one problem that I have personally experienced in the past is that when you insert pointers into a hash table that uses powers of two, some slots will never be used. 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. Also at least in my table the lookups are just a linear search. = total 60 bytes Otherwise the main difference here is that erasing from flat_hash_map has gotten much more spiky than it was in the other erase picture above, and the line has moved up considerably, getting almost as expensive as in the node based containers. Couldn't make it work with google::dense_hash_map. That alone can account for all of the difference: In the last benchmark in there which has to hash an 18 character string, the C++ version will start from scratch and re-hash the 18 characters for every loop iteration. Check if its valid The advantage is that swap will be very fast again because only a pointer is exchanged, the disadvantage is that it requires lots of allocations. But if you’re not getting cache misses for every single lookup, chances are that the integer modulo will end up being your most expensive operation. I use google benchmark and that calls table.find() over and over again for half a second, and then counts how many times it was able to call that function. Other hash tables are O(1) in the average case, but O(n) in the worst case. This is actually pretty impressive: All of these hash tables maintain consistent performance across many different orders of magnitude. flat_hash_map is the new hash table I’m presenting in this blog post. https://github.com/1ykos/ordered_patch_map, New links: This only affects the worst case for inserts, which you can make really slow with malicious data. That being said this does not affect the worst case for lookups. Let’s also measure erasing elements. > I think libdivide doesn’t have a modulo operator. If I make the value size 1024 bytes, the graph looks very similar to the one above this one, so just look at that one again. This works great for small tables, but when I insert random values into a large table, I would get unlucky all the time and hit four probes and I would have to grow the table even though it was mostly empty. This is not how std::unordered_map works, which stores every element in a separate heap allocation. time(find_erase): 16 time(find): 812 It does only re-distribute the collisions in L1 which remain in-place until a L1 growth. My first idea was to set this to a very low number, say 4. Hi, Thanks for taking the time to write this implementation. I have an implementation that at compile time switches to a node-based implementation when sizeof(mapped_type) > 2*sizof(void*). Two probes also happen pretty often, but three probes are rare. The cache miss picture looks different: In this we can see that when the table is not already in the cache, dense_hash_map remains faster. So then how do we solve the problem of the slow integer modulo? So if your container is long lived this cost will be amortized. MurMurHash3, an ultra fast hash algorithm for C# / .NET. Oh, and for every small tables, L2 can be considered the L1, bypassing the L1 entirely. Most of the buckets in this case are still empty though (~636 thousand). Read the entry For the graphing I actually just manually copy+paste my results to a table in Libre Office and use their graphing functionality. time(insert): 375 On the other hand you also can’t look at buckets individually: If in a table with 500 thousand elements in it there will be 13-14 buckets with 6 elements in it and 1 bucket with 7 elements, what tends to happen is that they’re all bunched together and push each other over. Normally when we say hash tables are O(n) in the worst case, we mean n = number of elements present. The reason why all other lookup graphs looked the same (and why I don’t show them) is this: For the node-based containers you don’t care how big the value is. I want to talk about this graph a little. In this tutorial you will learn about Hashing in C and C++ with program example. 10.3.1.3. I haven’t tested it rigorously, though. 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); Here google::dense_hash_map beats my new hash table, but not by much. You can see that it’s much faster and I’ll explain why that is below. Could you run a comparison with that as well? time(erase): 31 There are three expensive steps in looking up an item in a hashtable: Step 1 can be really cheap if your key is an integer: You just cast the int to a size_t. The 32 byte value picture looks identical, so I’m not even going to show it. So in the first insert the table has to reallocate a bunch of times and that is very expensive in the flat containers, and it’s cheaper for the node based containers. } Needed both to insert data and lookup data to get it done. (Note also that in Rust pointers/references have almost always a `__restrict` annotation because they cannot alias mutably, so adding this annotation to the C++ code might resolve the issue). It’s easier to generate. Heh, yeah I hadn’t thought about that case. The idiom you are suggesting should work (and has been tested).
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