Implementing Random Indexing on GPU

Implementing Random Indexing on GPU

Paper in proceedings outside WoS and Scopus

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.

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.

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

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

POLOK, L.; SMRŽ, P.

2012

14.07.2011

SCS Publication House

Boston

978-1-61782-840-9

Proceedings of the 19th High Performance Computing Symposium

HPC '11

134

142

9

http://dl.acm.org/citation.cfm?id=2048577.2048595

Implementing Random Indexing on GPU