
DHash: Enabling Dynamic and Efficient Hash Tables
Given a specified average load factor, hash tables offer the appeal of c...
read it

Dash: Scalable Hashing on Persistent Memory
Byteaddressable persistent memory (PM) brings hash tables the potential...
read it

AllPurpose Hashing
Despite being one of the oldest data structures in computer science, has...
read it

WarpCore: A Library for fast Hash Tables on GPUs
Hash tables are ubiquitous. Properties such as an amortized constant tim...
read it

DataParallel Hashing Techniques for GPU Architectures
Hash tables are one of the most fundamental data structures for effectiv...
read it

A Genetic Algorithm for Obtaining Memory Constrained NearPerfect Hashing
The problem of fast items retrieval from a fixed collection is often enc...
read it

Fast hashing with Strong Concentration Bounds
Previous work on tabulation hashing of Pǎtraşcu and Thorup from STOC'11 ...
read it
Linear Probing Revisited: Tombstones Mark the Death of Primary Clustering
First introduced in 1954, linear probing is one of the oldest data structures in computer science, and due to its unrivaled data locality, it continues to be one of the fastest hash tables in practice. It is widely believed and taught, however, that linear probing should never be used at high load factors; this is because primaryclustering effects cause insertions at load factor 1  1 /x to take expected time Θ(x^2) (rather than the ideal Θ(x)). The dangers of primary clustering, first discovered by Knuth in 1963, have been taught to generations of computer scientists, and have influenced the design of some of many widely used hash tables. We show that primary clustering is not a foregone conclusion. We demonstrate that small design decisions in how deletions are implemented have dramatic effects on the asymptotic performance of insertions, so that, even if a hash table operates continuously at a load factor 1  Θ(1/x), the expected amortized cost per operation is Õ(x). This is because tombstones created by deletions actually cause an anticlustering effect that combats primary clustering. We also present a new variant of linear probing (which we call graveyard hashing) that completely eliminates primary clustering on any sequence of operations: if, when an operation is performed, the current load factor is 1  1/x for some x, then the expected cost of the operation is O(x). One corollary is that, in the externalmemory model with a data blocks of size B, graveyard hashing offers the following remarkable guarantee: at any load factor 1  1/x satisfying x = o(B), graveyard hashing achieves 1 + o(1) expected block transfers per operation. Past externalmemory hash tables have only been able to offer a 1 + o(1) guarantee when the block size B is at least Ω(x^2).
READ FULL TEXT
Comments
There are no comments yet.