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Virginia Tech

Indexing Large Permutations in Hardware

Abstract

dc:description.abstract

Generating unbiased permutations at run time has traditionally been accomplished through application specific optimized combinational logic and has been limited to very small permutations. For generating unbiased permutations of any larger size, variations of the memory dependent Fisher-Yates algorithm are known to be an optimal solution in software and have been relied on as a hardware solution even to this day. However, in hardware, this thesis proves Fisher-Yates to be a suboptimal solution. This thesis will show variations of Fisher-Yates to be suboptimal by proposing an alternate method that does not rely on memory and outperforms Fisher-Yates based permutation generators, while still able to scale to very large sized permutations. This thesis also proves that this proposed method is unbiased and requires a minimal input. Lastly, this thesis demonstrates a means to scale the proposed method to any sized permutations and also to produce optimal partial permutations.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Computer Engineering
Department dc:contributor.department
Electrical and Computer Engineering
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2019

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Odom, Jacob Henry
Chair dc:contributor.committeechair
  • Athanas, Peter M.
Committee members dc:contributor.committeemember
  • Martin, Thomas L.
  • Tront, Joseph G.

Subjects

dc:subject × 5

Rights

dc:rights
Statement dc:rights
  • In Copyright

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:20931
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/89906

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Odom, Jacob Henry. Indexing Large Permutations in Hardware. masters thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/89906