Hopscotch hashing

Maurice Herlihy*, Nir Shavit, Moran Tzafrir

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


We present a new class of resizable sequential and concurrent hash map algorithms directed at both uni-processor and multicore machines. The new hopscotch algorithms are based on a novel hopscotch multi-phased probing and displacement technique that has the flavors of chaining, cuckoo hashing, and linear probing, all put together, yet avoids the limitations and overheads of these former approaches. The resulting algorithms provide tables with very low synchronization overheads and high cache hit ratios. In a series of benchmarks on a state-of-the-art 64-way Niagara II multicore machine, a concurrent version of hopscotch proves to be highly scalable, delivering in some cases 2 or even 3 times the throughput of today's most efficient concurrent hash algorithm, Lea's ConcurrentHashMap from java.concurr.util. Moreover, in tests on both Intel and Sun uni-processor machines, a sequential version of hopscotch consistently outperforms the most effective sequential hash table algorithms including cuckoo hashing and bounded linear probing. The most interesting feature of the new class of hopscotch algorithms is that they continue to deliver good performance when the hash table is more than 90% full, increasing their advantage over other algorithms as the table density grows.

Original languageEnglish
Title of host publicationDistributed Computing - 22nd International Symposium, DISC 2008, Proceedings
Number of pages15
StatePublished - 2008
Event22nd International Symposium on Distributed Computing, DISC 2008 - Arcachon, France
Duration: 22 Sep 200824 Sep 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5218 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference22nd International Symposium on Distributed Computing, DISC 2008


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