RT info:eu-repo/semantics/article
T1 Distributed programming of a hyperspectral image registration algorithm for heterogeneous GPU clusters
A1 Fernández-Fabeiro, Jorge
A1 Gonzalez-Escribano, Arturo
A1 Llanos, Diego R.
K1 Informática
K1 Hyperspectral imaging
K1 Image registration
K1 Heterogeneous computing
K1 Distributed arrays
K1 Load balancing
K1 1203 Ciencia de Los Ordenadores
K1 3304 Tecnología de Los Ordenadores
AB Hyperspectral image registration is a relevant task for real-time applications such as environmental disaster management or search and rescue scenarios. The HYFMGPU algorithm was proposed as a single-GPU high-performance solution, but the need for a distributed version has arisen due to the continuous evolution of sensors that generate images with finer spatial and spectral resolutions. In a previous work, we simplified the programming of the multi-device parts of an initial MPI+CUDA multi-GPU implementation of HYFMGPU by means of Hitmap, a library to ease the programming of parallel applications based on distributed arrays. The performance of that Hitmap version was assessed in a homogeneous GPU cluster. In this paper, we extend this implementation by means of new functionalities added to the latest version of Hitmap in order to support arbitrary load distributions for multi-node heterogeneous GPU clusters. Three different load balancing layouts are tested, which prove that selecting a proper layout affects the performance of the code and how this performance is correlated with the use of the GPUs available in the cluster.
PB Elsevier
SN 0743-7315
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/70201
UL https://uvadoc.uva.es/handle/10324/70201
LA eng
NO Journal of Parallel and Distributed Computing, vol. 151, pages 86-93, May 2021, ISSN 0743-7315
NO Producción Científica
DS UVaDOC
RD 28-nov-2024