Task: Learned index structures

Abstract

Tradicionalne strukture podataka za indeksiranje mogu se poboljšati metodama strojnog učenja. Neuronske mreže za tu primjenu uče distribuciju podataka spremljenih u indeksne strukture. Razvoj grafičkih kartica može povećati efikasnost i smanjiti vremena izvođenja takvih modela. U ovom radu pokazano je da naučeni model može bolje raspoređivati ključeve po tablici raspršenog adresiranja smanjujuću broj kolizija i memorijsko zauzeće. U radu je predstavljena teorijska podloga tablica raspršenog adresiranja i struktura modela strojnog učenja te implementacijski detalji razvijenog programskog koda.Traditional index data structures can be improved using machine learning. Neural networks used for this learn the underlying data distributions of the data which is stored in these data structures. The development of graphics processing units could improve the efficiency of such models and reduce their execution time. It is shown in this thesis that a model can be learned which distributes the keys over the hash table more efficiently, thereby reducing the number of collisions as well as reducing the memory footprint. This thesis presents the theoretical background of hash tables and the machine learning structure as well as the implementation details of the developed source code