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Journal articles on the topic 'Cuckcoo hashing'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Pagh, Rasmus, and Flemming Friche Rodler. "Cuckoo hashing." Journal of Algorithms 51, no. 2 (May 2004): 122–44. http://dx.doi.org/10.1016/j.jalgor.2003.12.002.

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2

Jang, Rhong-ho, Chang-hun Jung, Keun-young Kim, Dae-hun Nyang, and Kyung-Hee Lee. "Enhancing RCC(Recyclable Counter With Confinement) with Cuckoo Hashing." Journal of Korean Institute of Communications and Information Sciences 41, no. 6 (June 30, 2016): 663–71. http://dx.doi.org/10.7840/kics.2016.41.6.663.

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3

Min, Dae-hong, Rhong-ho Jang, Dae-hun Nyang, and Kyung-hee Lee. "Sorting Cuckoo: Enhancing Lookup Performance of Cuckoo Hashing Using Insertion Sort." Journal of Korean Institute of Communications and Information Sciences 42, no. 3 (March 31, 2017): 566–76. http://dx.doi.org/10.7840/kics.2017.42.3.566.

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4

Tran, Thinh Ngoc. "CPM: CUCKOO-BASED PATTERN MATCHING APPLIED FOR NIDS." Science and Technology Development Journal 14, no. 2 (June 30, 2011): 53–61. http://dx.doi.org/10.32508/stdj.v14i2.1909.

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This paper describes the Cuckoo-based Pattern Matching (CPM) engine which based on a recently developed hashing algorithm called Cuckoo Hashing. We implemented the improved parallel Cuckoo Hashing suitable for hardware-based multi-pattern matching with arbitrary length. CPM is scalable with multi-character per clock cycle to sustain higher throughput rates with lower hardware resources. With the power of massively parallel processing, the speedup of CPM is up to 128X as compared with serial Cuckoo implementation. Compared to other hardware systems, CPM is far better in performance and save 30% of the area compared with the best system.

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5

Devroye, Luc, and Pat Morin. "Cuckoo hashing: Further analysis." Information Processing Letters 86, no. 4 (May 2003): 215–19. http://dx.doi.org/10.1016/s0020-0190(02)00500-8.

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6

Kirsch, Adam, Michael Mitzenmacher, and Udi Wieder. "More Robust Hashing: Cuckoo Hashing with a Stash." SIAM Journal on Computing 39, no. 4 (January 2010): 1543–61. http://dx.doi.org/10.1137/080728743.

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7

Jang, Saeyoung, Hayoung Byun, and Hyesook Lim. "Cuckoo Hashing with Three Hash Tables." Journal of the Institute of Electronics and Information Engineers 57, no. 8 (August 31, 2020): 91–99. http://dx.doi.org/10.5573/ieie.2020.57.8.91.

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8

Fountoulakis, Nikolaos, and Konstantinos Panagiotou. "Sharp load thresholds for cuckoo hashing." Random Structures & Algorithms 41, no. 3 (May 12, 2012): 306–33. http://dx.doi.org/10.1002/rsa.20426.

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9

Drmota, Michael, and Reinhard Kutzelnigg. "A precise analysis of Cuckoo hashing." ACM Transactions on Algorithms 8, no. 2 (April 2012): 1–36. http://dx.doi.org/10.1145/2151171.2151174.

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10

Berman, Itay, Iftach Haitner, Ilan Komargodski, and Moni Naor. "Hardness-Preserving Reductions via Cuckoo Hashing." Journal of Cryptology 32, no. 2 (May 7, 2018): 361–92. http://dx.doi.org/10.1007/s00145-018-9293-0.

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11

Frieze, Alan, Páll Melsted, and Michael Mitzenmacher. "An Analysis of Random-Walk Cuckoo Hashing." SIAM Journal on Computing 40, no. 2 (January 2011): 291–308. http://dx.doi.org/10.1137/090770928.

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12

Fountoulakis, Nikolaos, Konstantinos Panagiotou, and Angelika Steger. "On the Insertion Time of Cuckoo Hashing." SIAM Journal on Computing 42, no. 6 (January 2013): 2156–81. http://dx.doi.org/10.1137/100797503.

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13

Pontarelli, S., P. Reviriego, and J. A. Maestro. "Efficient Flow Sampling With Back-Annotated Cuckoo Hashing." IEEE Communications Letters 18, no. 10 (October 2014): 1695–98. http://dx.doi.org/10.1109/lcomm.2014.2347959.

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14

Frieze, Alan, and Tony Johansson. "On the insertion time of random walk cuckoo hashing." Random Structures & Algorithms 54, no. 4 (September 21, 2018): 721–29. http://dx.doi.org/10.1002/rsa.20808.

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15

Seethalakshmi, D., and Dr G. M. Nasira. "Intrusion Detection and Defense Implementation (IDDI) in Cuckoo Hashing." IOSR Journal of Computer Engineering 19, no. 03 (June 2017): 49–52. http://dx.doi.org/10.9790/0661-1903064952.

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16

Pontarelli, Salvatore, Pedro Reviriego, and Juan Antonio Maestro. "Parallel d-Pipeline: A Cuckoo Hashing Implementation for Increased Throughput." IEEE Transactions on Computers 65, no. 1 (January 1, 2016): 326–31. http://dx.doi.org/10.1109/tc.2015.2417524.

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17

Kim, Euihyeok, and Min-Soo Kim. "Enhanced chained and Cuckoo hashing methods for multi-core CPUs." Cluster Computing 17, no. 3 (January 28, 2014): 665–80. http://dx.doi.org/10.1007/s10586-013-0343-y.

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18

Sun, Yuanyuan, Yu Hua, Dan Feng, Ling Yang, Pengfei Zuo, Shunde Cao, and Yuncheng Guo. "A Collision-Mitigation Cuckoo Hashing Scheme for Large-Scale Storage Systems." IEEE Transactions on Parallel and Distributed Systems 28, no. 3 (March 1, 2017): 619–32. http://dx.doi.org/10.1109/tpds.2016.2594763.

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19

Reviriego, P., S. Pontarelli, and J. A. Maestro. "Energy Efficient Exact Matching for Flow Identification with Cuckoo Affinity Hashing." IEEE Communications Letters 18, no. 5 (May 2014): 885–88. http://dx.doi.org/10.1109/lcomm.2014.040214.140506.

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20

Li, Hengjian, Jian Qiu, and Andrew Beng Jin Teoh. "Palmprint template protection scheme based on randomized cuckoo hashing and MinHash." Multimedia Tools and Applications 79, no. 17-18 (January 10, 2020): 11947–71. http://dx.doi.org/10.1007/s11042-019-08446-8.

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21

Aumüller, Martin, Martin Dietzfelbinger, and Philipp Woelfel. "Explicit and Efficient Hash Families Suffice for Cuckoo Hashing with a Stash." Algorithmica 70, no. 3 (October 8, 2013): 428–56. http://dx.doi.org/10.1007/s00453-013-9840-x.

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22

FOUNTOULAKIS, NIKOLAOS, MEGHA KHOSLA, and KONSTANTINOS PANAGIOTOU. "The Multiple-Orientability Thresholds for Random Hypergraphs." Combinatorics, Probability and Computing 25, no. 6 (December 28, 2015): 870–908. http://dx.doi.org/10.1017/s0963548315000334.

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Ak-uniform hypergraphH= (V, E) is called ℓ-orientable if there is an assignment of each edgee∈Eto one of its verticesv∈esuch that no vertex is assigned more than ℓ edges. LetHn,m,kbe a hypergraph, drawn uniformly at random from the set of allk-uniform hypergraphs withnvertices andmedges. In this paper we establish the threshold for the ℓ-orientability ofHn,m,kfor allk⩾ 3 and ℓ ⩾ 2, that is, we determine a critical quantityc*k,ℓsuch that with probability 1 −o(1) the graphHn,cn,khas an ℓ-orientation ifc<c*k,ℓ, but fails to do so ifc>c*k,ℓ.Our result has various applications, including sharp load thresholds for cuckoo hashing, load balancing with guaranteed maximum load, and massive parallel access to hard disk arrays.

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23

Deeds, Kyle, Brian Hentschel, and Stratos Idreos. "Stacked filters." Proceedings of the VLDB Endowment 14, no. 4 (December 2020): 600–612. http://dx.doi.org/10.14778/3436905.3436919.

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We present Stacked Filters, a new probabilistic filter which is fast and robust similar to query-agnostic filters (such as Bloom and Cuckoo filters), and at the same time brings low false positive rates and sizes similar to classifier-based filters (such as Learned Filters). The core idea is that Stacked Filters incorporate workload knowledge about frequently queried non-existing values. Instead of learning, they structurally incorporate that knowledge using hashing and several sequenced filter layers, indexing both data and frequent negatives. Stacked Filters can also gather workload knowledge on-the-fly and adaptively build the filter. We show experimentally that for a given memory budget, Stacked Filters achieve end-to-end query throughput up to 130x better than the best alternative for a workload, either query-agnostic or classifier-based filters, and depending on where data is (SSD or HDD).

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24

Kutzelnigg, Reinhard. "An Improved Version of Cuckoo Hashing: Average Case Analysis of Construction Cost and Search Operations." Mathematics in Computer Science 3, no. 1 (November 27, 2009): 47–60. http://dx.doi.org/10.1007/s11786-009-0005-x.

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25

Byun, Hayoung, and Hyesook Lim. "Comparison on Search Failure between Hash Tables and a Functional Bloom Filter." Applied Sciences 10, no. 15 (July 29, 2020): 5218. http://dx.doi.org/10.3390/app10155218.

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Hash-based data structures have been widely used in many applications. An intrinsic problem of hashing is collision, in which two or more elements are hashed to the same value. If a hash table is heavily loaded, more collisions would occur. Elements that could not be stored in a hash table because of the collision cause search failures. Many variant structures have been studied to reduce the number of collisions, but none of the structures completely solves the collision problem. In this paper, we claim that a functional Bloom filter (FBF) provides a lower search failure rate than hash tables, when a hash table is heavily loaded. In other words, a hash table can be replaced with an FBF because the FBF is more effective than hash tables in the search failure rate in storing a large amount of data to a limited size of memory. While hash tables require to store each input key in addition to its return value, a functional Bloom filter stores return values without input keys, because different index combinations according to each input key can be used to identify the input key. In search failure rates, we theoretically compare the FBF with hash-based data structures, such as multi-hash table, cuckoo hash table, and d-left hash table. We also provide simulation results to prove the validity of our theoretical results. The simulation results show that the search failure rates of hash tables are larger than that of the functional Bloom filter when the load factor is larger than 0.6.

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26

Pagh, Rasmus, and Flemming Friche Rodler. "Cuckoo Hashing." BRICS Report Series 8, no. 32 (August 4, 2001). http://dx.doi.org/10.7146/brics.v8i32.21692.

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We present a simple and efficient dictionary with worst case constant lookup time, equaling the theoretical performance of the classic dynamic perfect hashing scheme of Dietzfelbinger et al. (<em>Dynamic perfect hashing: Upper and lower bounds. SIAM J. Comput., 23(4):738-761, 1994</em>). The space usage is similar to that of binary search trees, i.e., three words per key on average. The practicality of the scheme is backed by extensive experiments and comparisons with known methods, showing it to be quite competitive also in the average case.

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27

Kutzelnigg, Reinhard. "Bipartite Random Graphs and Cuckoo Hashing." Discrete Mathematics & Theoretical Computer Science DMTCS Proceedings vol. AG,..., Proceedings (January 1, 2006). http://dx.doi.org/10.46298/dmtcs.3486.

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International audience The aim of this paper is to extend the analysis of Cuckoo Hashing of Devroye and Morin in 2003. In particular we make several asymptotic results much more precise. We show, that the probability that the construction of a hash table succeeds, is asymptotically $1-c(\varepsilon)/m+O(1/m^2)$ for some explicit $c(\varepsilon)$, where $m$ denotes the size of each of the two tables, $n=m(1- \varepsilon)$ is the number of keys and $\varepsilon \in (0,1)$. The analysis rests on a generating function approach to the so called Cuckoo Graph, a random bipartite graph. We apply a double saddle point method to obtain asymptotic results covering tree sizes, the number of cycles and the probability that no complex component occurs.

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28

"High-speed data deduplication using parallelized cuckoo hashing." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 26, no. 3 (May 30, 2018). http://dx.doi.org/10.3906/elk-1708-336.

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29

Kutzelnigg, Reinhard. "A further analysis of Cuckoo Hashing with a Stash and Random Graphs of Excess r." Discrete Mathematics & Theoretical Computer Science Vol. 12 no. 3, Analysis of Algorithms (January 1, 2010). http://dx.doi.org/10.46298/dmtcs.495.

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Analysis of Algorithms International audience Cuckoo hashing is a hash table data structure offering constant access time, even in the worst case. As a drawback, the construction fails with small, but practically significant probability. However, Kirsch et al. (2008) showed that a constant-sized additional memory, the so called stash, is sufficient to reduce the failure rate drastically. But so far, using a modified insertion procedure that demands additional running time to look for an admissible key is required. As a major contribution of this paper, we show that the same bounds on the failure probability hold even without this search process and thus, the performance increases. Second, we extend the analysis to simplified cuckoo hashing, a variant of the original algorithm offering increased performance. Further, we derive some explicit asymptotic approximations concerning the number of usual resp. bipartite graphs related to the data structures. Using these results, we obtain much more precise asymptotic expansions of the success rate. These calculations are based on a generating function approach and applying the saddle point method. Finally, we provide numerical results to support the theoretical analysis.

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30

Zentgraf, Jens, and Sven Rahmann. "Fast lightweight accurate xenograft sorting." Algorithms for Molecular Biology 16, no. 1 (April 2, 2021). http://dx.doi.org/10.1186/s13015-021-00181-w.

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Abstract Motivation With an increasing number of patient-derived xenograft (PDX) models being created and subsequently sequenced to study tumor heterogeneity and to guide therapy decisions, there is a similarly increasing need for methods to separate reads originating from the graft (human) tumor and reads originating from the host species’ (mouse) surrounding tissue. Two kinds of methods are in use: On the one hand, alignment-based tools require that reads are mapped and aligned (by an external mapper/aligner) to the host and graft genomes separately first; the tool itself then processes the resulting alignments and quality metrics (typically BAM files) to assign each read or read pair. On the other hand, alignment-free tools work directly on the raw read data (typically FASTQ files). Recent studies compare different approaches and tools, with varying results. Results We show that alignment-free methods for xenograft sorting are superior concerning CPU time usage and equivalent in accuracy. We improve upon the state of the art sorting by presenting a fast lightweight approach based on three-way bucketed quotiented Cuckoo hashing. Our hash table requires memory comparable to an FM index typically used for read alignment and less than other alignment-free approaches. It allows extremely fast lookups and uses less CPU time than other alignment-free methods and alignment-based methods at similar accuracy. Several engineering steps (e.g., shortcuts for unsuccessful lookups, software prefetching) improve the performance even further. Availability Our software xengsort is available under the MIT license at http://gitlab.com/genomeinformatics/xengsort. It is written in numba-compiled Python and comes with sample Snakemake workflows for hash table construction and dataset processing.

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