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Space Efficient Hash Tables With Worst Case Constant Access Time
 In STACS
, 2003
"... We generalize Cuckoo Hashing [23] to dary Cuckoo Hashing and show how this yields a simple hash table data structure that stores n elements in (1 + ffl) n memory cells, for any constant ffl ? 0. Assuming uniform hashing, accessing or deleting table entries takes at most d = O(ln ffl ) probes ..."
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Cited by 60 (4 self)
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We generalize Cuckoo Hashing [23] to dary Cuckoo Hashing and show how this yields a simple hash table data structure that stores n elements in (1 + ffl) n memory cells, for any constant ffl ? 0. Assuming uniform hashing, accessing or deleting table entries takes at most d = O(ln ffl ) probes and the expected amortized insertion time is constant. This is the first dictionary that has worst case constant access time and expected constant update time, works with (1 + ffl) n space, and supports satellite information. Experiments indicate that d = 4 choices suffice for ffl 0:03. We also describe variants of the data structure that allow the use of hash functions that can be evaluted in constant time.
Why simple hash functions work: Exploiting the entropy in a data stream
 In Proceedings of the 19th Annual ACMSIAM Symposium on Discrete Algorithms
, 2008
"... Hashing is fundamental to many algorithms and data structures widely used in practice. For theoretical analysis of hashing, there have been two main approaches. First, one can assume that the hash function is truly random, mapping each data item independently and uniformly to the range. This idealiz ..."
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Cited by 50 (9 self)
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Hashing is fundamental to many algorithms and data structures widely used in practice. For theoretical analysis of hashing, there have been two main approaches. First, one can assume that the hash function is truly random, mapping each data item independently and uniformly to the range. This idealized model is unrealistic because a truly random hash function requires an exponential number of bits to describe. Alternatively, one can provide rigorous bounds on performance when explicit families of hash functions are used, such as 2universal or O(1)wise independent families. For such families, performance guarantees are often noticeably weaker than for ideal hashing. In practice, however, it is commonly observed that weak hash functions, including 2universal hash functions, perform as predicted by the idealized analysis for truly random hash functions. In this paper, we try to explain this phenomenon. We demonstrate that the strong performance of universal hash functions in practice can arise naturally from a combination of the randomness of the hash function and the data. Specifically, following the large body of literature on random sources and randomness extraction, we model the data as coming from a “block source, ” whereby
More Robust Hashing: Cuckoo Hashing with a Stash
 IN PROCEEDINGS OF THE 16TH ANNUAL EUROPEAN SYMPOSIUM ON ALGORITHMS (ESA
, 2008
"... Cuckoo hashing holds great potential as a highperformance hashing scheme for real applications. Up to this point, the greatest drawback of cuckoo hashing appears to be that there is a polynomially small but practically significant probability that a failure occurs during the insertion of an item, r ..."
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Cited by 39 (14 self)
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Cuckoo hashing holds great potential as a highperformance hashing scheme for real applications. Up to this point, the greatest drawback of cuckoo hashing appears to be that there is a polynomially small but practically significant probability that a failure occurs during the insertion of an item, requiring an expensive rehashing of all items in the table. In this paper, we show that this failure probability can be dramatically reduced by the addition of a very small constantsized stash. We demonstrate both analytically and through simulations that stashes of size equivalent to only three or four items yield tremendous improvements, enhancing cuckoo hashing’s practical viability in both hardware and software. Our analysis naturally extends previous analyses of multiple cuckoo hashing variants, and the approach may prove useful in further related schemes.
Efficient hashing with lookups in two memory accesses, in: 16th
 SODA, ACMSIAM
"... The study of hashing is closely related to the analysis of balls and bins. Azar et. al. [1] showed that instead of using a single hash function if we randomly hash a ball into two bins and place it in the smaller of the two, then this dramatically lowers the maximum load on bins. This leads to the c ..."
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Cited by 19 (3 self)
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The study of hashing is closely related to the analysis of balls and bins. Azar et. al. [1] showed that instead of using a single hash function if we randomly hash a ball into two bins and place it in the smaller of the two, then this dramatically lowers the maximum load on bins. This leads to the concept of twoway hashing where the largest bucket contains O(log log n) balls with high probability. The hash look up will now search in both the buckets an item hashes to. Since an item may be placed in one of two buckets, we could potentially move an item after it has been initially placed to reduce maximum load. Using this fact, we present a simple, practical hashing scheme that maintains a maximum load of 2, with high probability, while achieving high memory utilization. In fact, with n buckets, even if the space for two items are preallocated per bucket, as may be desirable in hardware implementations, more than n items can be stored giving a high memory utilization. Assuming truly random hash functions, we prove the following properties for our hashing scheme. • Each lookup takes two random memory accesses, and reads at most two items per access. • Each insert takes O(log n) time and up to log log n+ O(1) moves, with high probability, and constant time in expectation. • Maintains 83.75 % memory utilization, without requiring dynamic allocation during inserts. We also analyze the tradeoff between the number of moves performed during inserts and the maximum load on a bucket. By performing at most h moves, we can maintain a maximum load of O(hlogl((~og~og:n/h)). So, even by performing one move, we achieve a better bound than by performing no moves at all. 1
Balanced Allocation on Graphs
 In Proc. 7th Symposium on Discrete Algorithms (SODA
, 2006
"... It is well known that if n balls are inserted into n bins, with high probability, the bin with maximum load contains (1 + o(1))log n / loglog n balls. Azar, Broder, Karlin, and Upfal [1] showed that instead of choosing one bin, if d ≥ 2 bins are chosen at random and the ball inserted into the least ..."
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Cited by 17 (2 self)
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It is well known that if n balls are inserted into n bins, with high probability, the bin with maximum load contains (1 + o(1))log n / loglog n balls. Azar, Broder, Karlin, and Upfal [1] showed that instead of choosing one bin, if d ≥ 2 bins are chosen at random and the ball inserted into the least loaded of the d bins, the maximum load reduces drastically to log log n / log d+O(1). In this paper, we study the two choice balls and bins process when balls are not allowed to choose any two random bins, but only bins that are connected by an edge in an underlying graph. We show that for n balls and n bins, if the graph is almost regular with degree n ǫ, where ǫ is not too small, the previous bounds on the maximum load continue to hold. Precisely, the maximum load is
HistoryIndependent Cuckoo Hashing
"... Cuckoo hashing is an efficient and practical dynamic dictionary. It provides expected amortized constant update time, worst case constant lookup time, and good memory utilization. Various experiments demonstrated that cuckoo hashing is highly suitable for modern computer architectures and distribute ..."
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Cited by 16 (4 self)
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Cuckoo hashing is an efficient and practical dynamic dictionary. It provides expected amortized constant update time, worst case constant lookup time, and good memory utilization. Various experiments demonstrated that cuckoo hashing is highly suitable for modern computer architectures and distributed settings, and offers significant improvements compared to other schemes. In this work we construct a practical historyindependent dynamic dictionary based on cuckoo hashing. In a historyindependent data structure, the memory representation at any point in time yields no information on the specific sequence of insertions and deletions that led to its current content, other than the content itself. Such a property is significant when preventing unintended leakage of information, and was also found useful in several algorithmic settings. Our construction enjoys most of the attractive properties of cuckoo hashing. In particular, no dynamic memory allocation is required, updates are performed in expected amortized constant time, and membership queries are performed in worst case constant time. Moreover, with high probability, the lookup procedure queries only two memory entries which are independent and can be queried in parallel. The approach underlying our construction is to enforce a canonical memory representation on cuckoo hashing. That is, up to the initial randomness, each set of elements has a unique memory representation.
Using the Power of Two Choices to Improve Bloom Filters
, 2006
"... We consider the combination of two ideas from the hashing literature, the power of twochoices and Bloom filters. Specifically, we show via simulations that in comparison with a standard Bloom filter, using the power of two choices can yield modest reductions in the falsepositive probability using ..."
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Cited by 15 (1 self)
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We consider the combination of two ideas from the hashing literature, the power of twochoices and Bloom filters. Specifically, we show via simulations that in comparison with a standard Bloom filter, using the power of two choices can yield modest reductions in the falsepositive probability using the same amount of space and more hashing.
Backyard Cuckoo Hashing: Constant WorstCase Operations with a Succinct Representation
, 2010
"... The performance of a dynamic dictionary is measured mainly by its update time, lookup time, and space consumption. In terms of update time and lookup time there are known constructions that guarantee constanttime operations in the worst case with high probability, and in terms of space consumption ..."
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Cited by 13 (5 self)
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The performance of a dynamic dictionary is measured mainly by its update time, lookup time, and space consumption. In terms of update time and lookup time there are known constructions that guarantee constanttime operations in the worst case with high probability, and in terms of space consumption there are known constructions that use essentially optimal space. In this paper we settle two fundamental open problems: • We construct the first dynamic dictionary that enjoys the best of both worlds: we present a twolevel variant of cuckoo hashing that stores n elements using (1+ϵ)n memory words, and guarantees constanttime operations in the worst case with high probability. Specifically, for any ϵ = Ω((log log n / log n) 1/2) and for any sequence of polynomially many operations, with high probability over the randomness of the initialization phase, all operations are performed in constant time which is independent of ϵ. The construction is based on augmenting cuckoo hashing with a “backyard ” that handles a large fraction of the elements, together with a deamortized perfect hashing scheme for eliminating the dependency on ϵ.
Tabulation Based 5Universal Hashing and Linear Probing
"... Previously [SODA’04] we devised the fastest known algorithm for 4universal hashing. The hashing was based on small precomputed4universal tables. This led to a fivefold improvement in speed over direct methods based on degree 3 polynomials. In this paper, we show that if the precomputed tables a ..."
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Cited by 7 (4 self)
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Previously [SODA’04] we devised the fastest known algorithm for 4universal hashing. The hashing was based on small precomputed4universal tables. This led to a fivefold improvement in speed over direct methods based on degree 3 polynomials. In this paper, we show that if the precomputed tables are made 5universal, then the hash value becomes 5universal without any other change to the computation. Relatively this leads to even bigger gains since the direct methods for 5universal hashing use degree 4 polynomials. Experimentally, we find that our method can gain up to an order of magnitude in speed over direct 5universal hashing. Some of the most popular randomized algorithms have been proved to have the desired expected running time using