In geographic information systems, Digital Elevation Models (DEMs) are commonly processed using radial scanning based algorithms. These algorithms are particularly popular when calculating parameters whose magnitudes decrease with the distance squared such as those related to radio signals, sound waves , and human eyesight. However, radial scanning algorithms imply a large number of accesses to 2D arrays , which despite being regular, results in poor data locality . This paper proposes a new methodology , termed sDEM , which substantially improves the locality of memory accesses and largely increases the inherent parallelism involved in the computation of radial scanning algorithms. In particular, sDEM applies a data restructuring technique prior to accessing the memory and performing the computation. In order to demonstrate the high efficiency of sDEM, we use the problem of total viewshed computation as a case study . Sequential, parallel , single-GPU and multi-GPU implementations are analyzed and compared with the state-of-the-art total viewshed computation algorithm. Experiments show that sDEM achieves an acceleration rate of up to 827.3 times for the best multi-GPU execution approach with respect to the best multi-core implementation .
 Digital Elevation Models (DEMs) are important datasets for modelling the line of sight, such as radio signals, sound waves and human vision. These are commonly analyzed using rotational sweep algorithms. However, such algorithms require large numbers of memory accesses to 2D arrays which, despite being regular, result in poor data locality in memory. Here, we propose a new methodology called skewed Digital Elevation Model (sDEM) , which substantially improves the locality of memory accesses and increases the inherent parallelism involved in the computation of rotational sweep-based algorithms. In particular, sDEM applies a data restructuring technique before accessing the memory and performing the computation. To demonstrate the high efficiency of sDEM, we use the problem of total viewshed computation as a case study considering different implementations for single-core, multi-core , single-GPU and multi-GPU platforms. We conducted two experiments to compare sDEM with (i) the most commonly used geographic information systems (GIS) software and (ii) the state-of-the-art algorithm. In the first experiment, sDEM is on average 8.8x faster than current GIS software despite being able to consider only few points because of their limitations. In the second experiment, sDEM is 827.3 x faster than the state-of-the-art algorithm in the best case .
D
In geographic information systems,
R
commonly processed using radial scanning based algorithms. These algorithms are particularly popular when calculating parameters whose magnitudes decrease with the distance squared such as those related to
important datasets for modelling the line of sight, such as
R
, and human eyesight. However, radial scanning algorithms imply a large number of
and human vision. These are commonly analyzed using rotational sweep algorithms. However, such algorithms require large numbers of memory
R
, which
which,
R
results
result
R
. This paper proposes
in memory. Here, we propose
R
, termed sDEM
called skewed Digital Elevation Model (sDEM)
D
largely
R
radial scanning
rotational sweep-based
R
prior to
before
R
In order to
To
R
. Sequential, parallel
considering different implementations for single-core, multi-core
R
implementations are analyzed and compared with the
platforms. We conducted two experiments to compare sDEM with (i) the most commonly used geographic information systems (GIS) software and (ii) the
R
total viewshed computation algorithm. Experiments show that sDEM achieves an acceleration rate of up to
algorithm. In the first experiment, sDEM is on average 8.8x faster than current GIS software despite being able to consider only few points because of their limitations. In the second experiment, sDEM is
R
times for the best multi-GPU execution approach with respect to the best multi-core implementation
x faster than the state-of-the-art algorithm in the best case