Detail publikace

Implementing Random Indexing on GPU

POLOK, L. SMRŽ, P.

Originální název

Implementing Random Indexing on GPU

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

Vector space models (also word space models or term space models) are algebraic models, used for representing text documents as vectors of terms. They have received much attention recently as they have wide spectrum of applications, including information filtering, information retrieval, indexing and relevancy ranking. They can be advantageous over the other representations because vector spaces are mathematically well defined and there's large set of tools for manipulating them. Random Indexing is one of methods used for calculating vector space models from set of documents, based on distributional statistics of term cooccurrences. To  produce useful results it may therefore require large amounts of data and significant computational power. We present an efficient implementation of Random Indexing on GPU, allowing fast training even on large datasets. It is only limited by amount of memory available on GPU, some techniques to overcome this limitation are suggested. Speedups in magnitude of tens are achieved for training from random seed vectors, and even much higher for retraining. Implementation scales well with both term vector dimension and seed length.

Klíčová slova

random indexing, word space models, term co-occurence, GPGPU 

Autoři

POLOK, L.; SMRŽ, P.

Rok RIV

2011

Vydáno

14. 7. 2011

Nakladatel

SCS Publication House

Místo

Boston

ISBN

978-1-61782-840-9

Kniha

Proceedings of the 19th High Performance Computing Symposium

Edice

HPC '11

Strany od

134

Strany do

142

Strany počet

9

URL

BibTex

@inproceedings{BUT76420,
  author="Lukáš {Polok} and Pavel {Smrž}",
  title="Implementing Random Indexing on GPU",
  booktitle="Proceedings of the 19th High Performance Computing Symposium",
  year="2011",
  series="HPC '11",
  pages="134--142",
  publisher="SCS Publication House",
  address="Boston",
  isbn="978-1-61782-840-9",
  url="http://dl.acm.org/citation.cfm?id=2048577.2048595"
}