HOPSCOTCH HASHING PDF

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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The code is available on GitHub [4]. At index 7 we would expect an offset 1 if it contains an element that was indexed to 6.

The size of the neighborhood must be sufficient to accommodate a logarithmic number of items in the worst case i. Indeed, my main goal being to find a good method for storing hash tables on disk, hopscotch hashing would help limiting the number of blocks accessed on disk for any operation to the hash table.

Conclusion This was hwshing a short presentation of hopscotch hashing. Se Kwon Lee permalink. The neighborhood is thus a “virtual” bucket that has fixed size and overlaps with the next H-1 buckets. Those aspects are not being discussed in the present article, which focuses on single-core environments.

The implemented bitmap representation follows what is described in the paper, but surprisingly, this seems not to be the case for the linked-list one.

After spending some time optimizing, I am mostly happy with the results. Hopscotch After contemplating a while, I have come to the conclusion that Hopscotch is just a bad version of Robin Hood Hashing.

But no trace of displacements as described in the paper. When using open addressing with only a probing sequence and no reordering, entries are inserted in the first hashiing buckets found in the sequence.

It is to, so check the value, and bam! Hopscotch hashing hazhing introduced by Herlihy et al.

Hopscotch Hashing

Here is my reasoning: 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. So the above example from a could be stored hopscotcj this: To remove an item from the table, one simply removes it from the table entry.

As for the linked-list neighborhood, I was referring to cache prefetching more specifically.

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Hopsvotch distinguishes it from linear probing which leaves the empty slot where it was found, possibly far away from the original bucket, or from cuckoo hashing that, in order to create a free bucket, moves an item out of one of the desired buckets in the target arrays, and only then tries to find the displaced item a new place.

Even hashint scanning only 1 out of 5 or 6 buckets in a neighborhood, the number of cache lines that would be loaded in the L1 cache would be roughly the same for all neighborhood representations, and there would be little difference in performance between them assuming byte L1 cache lines.

Hopscotch hashing | Code Capsule

Unfortunately, linked lists are not very cache friendly. If someone has a better understanding of this code than I do, please drop a comment below. The third search is confined to the neighborhood of bucket 6 and hence will terminate at or before bucket 9.

My feeling is that the sweet spot for the size of the neighborhoods lies somewhere between 32 and 64, and beyond that, resizing the table is the best option. Here is a comparison table best values bold.

Very Fast HashMap in C++: Hopscotch & Robin Hood Hashing (Part 1)

Also, why do you say that the linked list is cache-unfriendly? Neighborhoods can be stored using bitmaps bit arrays. But this is not the case. In the code associated with the paper [3]this offset solution is the one that was chosen. 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. From what I understand from the code, it appears that putIfAbsent is just implementing a linear probing and that buckets are hopscoch linked together hopcotch prevent probing all buckets when calling containsKey.

At this point, the state of the hash table is as shown prior to Step 1 in Figure 3.

The fourth search is confined to the neighborhood of bucket 8 and hence will terminate at or before bucket I am currently experimenting with various hash table algorithms, and I stumbled upon an approach called hopscotch hashing.

From there, the neighborhood to which the entry belongs can be determined, which is the initial bucket that was just derived and the next H-1 buckets.

Hopscotch hashing is interesting because it guarantees a small number of look-ups to hopsccotch entries. Figure 3 In Figure 3, all the buckets in the area of the swap candidates are in the neighborhoods of buckets that are before the beginning of the swap area. In order to move the empty bucket E from the location where it was found into the neighborhood of B, it needs to be swapped with entries between the positions of B and E.

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As a result, at most H consecutive look-ups will be required in contiguous memory locations, which is extremely cache friendly. The original paper was using the bitmap representation to present the algorithm, and I believe that things are simpler without it. Also, the first search will terminate prior to bucket 6 hopscotcg it finds either an empty bucket or a bucket whose initial bucket is less than or equal to 3.

In order to insert a new entry, its key is hashed to find the initial bucket for the entry, denoted as B.

After contemplating a while, I have come to the conclusion that Hopscotch is just a bad version of Robin Hood Hashing. The idea is simple: Source code Talk is cheap. Due to the hopscotch method, an entry may not be in the bucket it was hashed to, its initial bucket, but most likely in a bucket in hopscoych neighborhood of its initial bucket.

With the shadow representation, a bucket is accessed haehing then its initial bucket is determined, which allows to know to which neighborhood this bucket belongs to. Another way to look at it is that increasing the size of the neighborhoods follows the law of diminishing returns.

H could, for example, be 32, a common machine word size.

Very Fast HashMap in C++: Hopscotch & Robin Hood Hashing (Part 1)

How many buckets to inspect prior to termination is an open question. A Makefile is included and should simplify compilation. At this point, I believe that long paragraphs of explanations would not be of any help. Then a third hxshing begins at bucket 7 in order to find a bucket whose initial bucket is less than or equal to 6. However in a table where many insertions and deletions occur, after some time I would expect the DELETED entry to be pushed to the border of the neighborhood, loosing its initial advantage.