We present a new class of resizable sequential and concurrent hash map algorithms directed at both uni-processor and multicore machines. The new hopscotch. I am currently experimenting with various hash table algorithms, and I stumbled upon an approach called hopscotch hashing. Hopscotch. We present a new resizable sequential and concurrent hash map algorithm directed at both uniprocessor and multicore machines. The algorithm is based on a.
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Remember to take a look at the source code on GitHub . The search for an empty bucket E then starts, using linear probing in the neighborhood of B.
Instead, I am presenting the insertion process of hopscotch hashing with a diagram, in Figure 1 below. Assuming deletions have occurred in the table, if you have two buckets X and Y in the same linked list with a link from X to Y, there is no guarantee that Y will be stored at an address higher than X, and indeed, Y could be at an address lower than X. The third search is confined to the neighborhood of bucket 6 and hence will terminate at or before bucket 9.
With a reordering scheme, entries already in the table can be moved as new entries are inserted. Wikipedia has a nice representation: An advantage of using linked lists is that the size of the neighborhoods can be much larger without too much overhead on the total size of the bucket array. My intuition was that by starting with smaller neighborhood sizes, items would be more spatially localized, which would allow for higher load factors to be reached than with constant neighborhood sizes.
The bitmap for bucket 5 is thereforewith a bit set to 1 at index 1, because bucket 6 is at an offset of 1 from bucket 5.
I am using Visual Studio Update 3, 64 bit, Intel i 3. For a perfectly full hashmap, where each bucket contains a corresponding entry, of the 32 hop bits there will be just a single bit that is set to 1. Is there a better way to represent the hop bitmap? References  Submission draft for Hopscotch Hashing by Herlihy et at.
How many buckets to inspect prior to termination is an open question.
Hopscotch Hashing — Multicore Algorithmics – Multicore TAU group
With these insights, I believe I have a great idea to implement a highly efficient variant of the hashkng hood hash table, that takes some ideas from the hopscotch implementation. Another idea that I wanted to try was to have variable neighborhood sizes. Also, the first search will terminate prior to bucket 6 if it finds either an empty bucket or a bucket whose initial bucket is less than or equal to 3.
If the neighborhood buckets are cache aligned, then one could apply a reorganization operation in which items are moved into the now vacant location in order to improve alignment. The neighborhood is thus a “virtual” bucket that has fixed size and overlaps with the next H-1 buckets.
The code is available on GitHub . Then a fourth search begins at bucket 9 in order to find a bucket whose initial hopscptch is less than or equal to 8. The first step to retrieve an entry is to determine its initial bucket. We found the element.
I am storing the hash of the key in each bucket, and my intent was to compare that stored hash with the hash of the key being looked up to avoid unnecessary accesses to the entry. Overview Hopscotch hashing was introduced by Herlihy et al. The fourth search is confined to the neighborhood of bucket 8 and hence will terminate at or before bucket From there, simply calling the program with the –help parameter gives a full description of the options available: Implementation Variants Part 3: Proceedings of the 22nd international symposium on Distributed Computing.
Storing the hashed keys is frequently required. What it appears is that with neighborhoods of size 64, the load factor at which clustering prevents inserting is around 0.
Subscribe to this comment feed via RSS. As a hash function, I am using std:: Those aspects are not being discussed in the present article, which focuses on single-core environments. The deletion process is simple: This representation was not part of the original paper, this is just an idea that I wanted to experiment with. All this sounds increasingly similar to Robin Hood Hashing. If neighborhoods are stored using linked lists, each bucket has a pointer to the next bucket in the same neighborhood.
Anyway at such high loads, the best thing to do would be to resize the table. In that that case, entries would be removed by lazy deletionwhich require the introduction of special values to mark deleted buckets. Then a second search begins at bucket 6 in order to find a bucket whose initial bucket is less than or equal to 5.