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.
|Published (Last):||5 August 2012|
|PDF File Size:||20.4 Mb|
|ePub File Size:||3.80 Mb|
|Price:||Free* [*Free Regsitration Required]|
As for the linked-list neighborhood, I was referring to cache prefetching more specifically. 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. On the other hand, a successful search will hopscocth initiate another search.
At this point, the state of the hash table is as shown prior to Step 1 in Figure 3. How many buckets to inspect prior to termination is an open question.
Hopscotch Hashing — Multicore Algorithmics – Multicore TAU group
If neighborhoods are stored using linked lists, each bucket has a pointer to the next bucket in the same neighborhood. The size of the bitmaps could be increased, but that would mean that the size of each bucket would be increased, which at some point would make the whole bucket array explode in memory. Say we want to check if b is in the map: As I am planning to implement other reordering schemes, I am keeping this analysis for a future article, when I will be done implementing all the other methods that I want to test.
Insertion is really fast and much more efficient, query time is also a bit faster than std:: The size of the neighborhood must be sufficient to accommodate a logarithmic number of items in bashing worst case i.
Another idea that I wanted to try was to have variable neighborhood sizes. Regarding storing the hashed keys in the bucket array, the main advantage is for when the data is stored in secondary storage HDD, SSD, etc.
In hopscotch hashing, as in cuckoo hashingand unlike in linear probinga given item will always be inserted-into and found-in the neighborhood of its hashed bucket.
That means at index 6 is an element that actually belongs there: Wikipedia has a nice representation: This page was last edited on 6 Octoberat uashing I found a typing error in the last hashjng of the second line in section 2. This clustering behavior occurs around load factors of 0. When querying for an element, we just need to sequentially check the offsets.
Because of that, none of hasjing buckets can be swapped with the empty bucket.
Hopscotch hashing is a reordering scheme that can be used with the open addressing method for collision resolution in hash tables. If no empty bucket is found, the insertion algorithm is terminated automatically hopscocth it has inspected a predetermined number of buckets.
Increasing the size of the neighborhood beyond that will give smaller and smaller improvements, and therefore is not worth it.
This 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. From the hashed key only, it is possible to find for any entry the position of its initial bucket using the modulo operator.
Hopscotch hashing is interesting because it guarantees a small number of look-ups to find entries. With haehing reordering scheme, entries already in the table can be moved as new entries are inserted.
One advantage of hopscotch hashing is that it provides good performance at very high table load factors, even ones exceeding 0. The desired property of the neighborhood is that the cost of finding an item in the buckets of the neighborhood is close to the cost of finding it hopscottch the bucket itself for example, by having buckets in the neighborhood fall within the same cache line.
Conclusion This was just a short presentation of hopscotch hashing. The first search is confined to the neighborhood of bucket 3 and hence will terminate at or before bucket 6, given that the neighborhood size H equals 4.
In my next post, part 2I will explain these ideas and hopefully have a fantastically fast and memory efficient hash table in my repository. An entry will respect the hopscotch guarantee if it can be found in the neighborhood of its initial bucket, i. I forgot to add the condition for that in the if statement, so that was definitely a bug. 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.
Hopscotch hashing August For each bucket, its neighborhood is a small hopcotch of nearby consecutive buckets i. As a hash function, I am using std:: Russell A Brown permalink.
Very Fast HashMap in C++: Hopscotch & Robin Hood Hashing (Part 1)
It is important to understand that hopscorch relationship between an entry and its neighborhood is reversed in the shadow representation compared to the bitmap and linked-list representations. So now we can ditch the hop size, and just keep swapping elements exactly like robin hood hashing does. Neighborhoods can be stored using bitmaps bit arrays.