  Usage of GPUs as co-processors is a well-established approach to accelerate
costly algorithms operating on matrices and vectors.
  We aim to further improve the performance of the Global Neutrino Analysis
framework (GNA) by adding GPU support in a way that is transparent to the end
user. To achieve our goal we use CUDA, a state of the art technology providing
GPGPU programming methods.
  In this paper we describe new features of GNA related to CUDA support. Some
specific framework features that influence GPGPU integration are also
explained. The paper investigates the feasibility of GPU technology application
and shows an example of the achieved acceleration of an algorithm implemented
within framework. Benchmarks show a significant performance increase when using
GPU transformations.
  The project is currently in the developmental phase. Our plans include
implementation of the set of transformations necessary for the data analysis in
the GNA framework and tests of the GPU expediency in the complete analysis
chain.
