Accumulated cost surfaces (ACSs) are a tool for spatial modelling used in a number of fields. Some relevant applications, especially in the areas of multi-criteria evaluation and spatial optimization, require the availability of several ACSs on the same raster, which may result in a significant computational cost. In this paper, we discuss some techniques available in the literature for accelerating the ACS computation using graphics processing units (GPUs) and CUDA. Also, we illustrate in details a new CUDA algorithm suitable for the computation of multiple ACSs. Moreover, we present some preliminary results on a test case, including an experimental comparison against a fast sequential implementation running on a CPU.
Computing Multiple Accumulated Cost Surfaces with Graphics Processing Units / Trunfio, Giuseppe, Andrea; Sirakoulis, Georgios C. h.. - (2016), pp. 694-701. (Intervento presentato al convegno 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, PDP 2016 tenutosi a grc nel 2016) [10.1109/PDP.2016.76].
Computing Multiple Accumulated Cost Surfaces with Graphics Processing Units
TRUNFIO, Giuseppe, Andrea;
2016-01-01
Abstract
Accumulated cost surfaces (ACSs) are a tool for spatial modelling used in a number of fields. Some relevant applications, especially in the areas of multi-criteria evaluation and spatial optimization, require the availability of several ACSs on the same raster, which may result in a significant computational cost. In this paper, we discuss some techniques available in the literature for accelerating the ACS computation using graphics processing units (GPUs) and CUDA. Also, we illustrate in details a new CUDA algorithm suitable for the computation of multiple ACSs. Moreover, we present some preliminary results on a test case, including an experimental comparison against a fast sequential implementation running on a CPU.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
