Parallel Nonnegative Matrix Factorization for Data Analysis with CUDA

Organisatsiooni nimi
Algorithms & Theory
Kokkuvõte
Nonnegative Matrix Factorization (NMF) is a way to automatically decompose frequencies (number of times a word occurs on a web page) into a small number of basic frequency patterns (corresponding to topics). Computing an NMF with smallest error is a difficult non-convex optimization problems.
There are several algorithms which perform NMF. The goal of this thesis is to develop one which can be effectively and efficiently parallelized for execution on GPUs.
The resulting code will use CUDA.
Lõputöö kaitsmise aasta
2016-2017
Juhendaja
Dirk Oliver Theis
Suhtlemiskeel(ed)
inglise keel
Nõuded kandideerijale
C or C++. Interest in parallel programming, GPU, CUDA.
Tase
Bakalaureus, Magister
Märksõnad
#tcs

Kandideerimise kontakt

 
Nimi
Dirk Oliver Theis
Tel
E-mail
dotheis@ut.ee