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A comparison study of Kd-tree, Vp-tree and Octree for storing neuronal morphology data with respect to performance
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In this thesis we investigated performance of Kdtree, Vptree and Octree for storing neuronal morphology data. Two naive list structures were implemented to compare with the space partition data structures. The performance was measured with different sizes of neuronal networks and different types of test cases. A comparison with focus on cache misses, average search time and memory usage was made. Furthermore, measurements gathered quantitative data about each data structure. The results showed significant difference in performance of each data structure. It was concluded that Vptree is more suitable for searches in smaller populations of neurons and for specific nodes in larger populations, while Kdtree is better for volume searches in larger populations. Octree had highest average search time and memory requirement.

Abstract [sv]

I denna rapport har vi undersökt prestanda av tre datastrukturer, Vptree, Kdtree och Octree, för lagring av neurala morfologidata. Två naiva liststrukturer implementerades, för att kunna jämföras med tre datastrukturer. Prestanda mättes med olika storlekar av neurala nätverket och med olika typer av testfall. En jämförelse med fokus på cachemissar, genomsnittlig söktid och minnesanvändning utfördes. Dessutom, samlade mätningarna kvantitativ data om varje datastruktur. Resultatet visade signifikant skillnad i prestanda mellan de implementerade datastrukturerna. Det konstaterades att Vptree är bättre för sökning i mindre populationer av neuroner samt för sökning av specifika noder i större populationer, medan Kdtree är bättre för volymsökning i större populationer. Octree hade högst medelsöktid och minnesanvändning.

Place, publisher, year, edition, pages
2016.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-187026OAI: oai:DiVA.org:kth-187026DiVA: diva2:928589
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Examiners
Available from: 2016-05-18 Created: 2016-05-16 Last updated: 2016-06-15Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf