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Parallel Nonnegative Matrix Factorization for Data Analysis with CUDA
Organisatsiooni nimiAlgorithms & Theory
KokkuvõteNonnegative 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 aasta2016-2017
JuhendajaDirk Oliver Theis
Suhtlemiskeel(ed)inglise keel
Nõuded kandideerijaleC or C++. Interest in parallel programming, GPU, CUDA.
Tase Bakalaureus, Magister
Märksõnad #tcs
Kandideerimise kontakt
Nimi Dirk Oliver Theis