Performance Evaluation of GPU-Accelerated Spatial Interpolation Using Radial Basis Functions for Building Explicit Surfaces

This paper focuses on evaluating the computational performance of parallel spatial interpolation with Radial Basis Functions (RBFs) that is developed by utilizing modern GPUs. The RBFs can be used in spatial interpolation to build explicit surfaces such as Discrete Elevation Models. When interpolati...

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Veröffentlicht in:International journal of parallel programming Jg. 46; H. 5; S. 963 - 991
Hauptverfasser: Ding, Zengyu, Mei, Gang, Cuomo, Salvatore, Xu, Nengxiong, Tian, Hong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.10.2018
Springer Nature B.V
Schlagworte:
Algorithms
Basis functions
Computational efficiency
Computer Science
Computing time
Data points
Efficiency
Interpolation
Linear equations
Performance evaluation
Processor Architectures
Radial basis function
Software Engineering/Programming and Operating Systems
Special Issue on Parallel Approaches for Data Mining in the Internet of Things Realm
Theory of Computation
Spatial interpolation
Radial basis function (RBF)
Parallel programming
Graphics processing unit (GPU)
Explicit surfaces
ISSN:0885-7458, 1573-7640
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Zusammenfassung:This paper focuses on evaluating the computational performance of parallel spatial interpolation with Radial Basis Functions (RBFs) that is developed by utilizing modern GPUs. The RBFs can be used in spatial interpolation to build explicit surfaces such as Discrete Elevation Models. When interpolating with large-size of data points and interpolated points for building explicit surfaces, the computational cost would be quite expensive. To improve the computational efficiency, we specifically develop a parallel RBF spatial interpolation algorithm on many-core GPUs, and compare it with the parallel version implemented on multi-core CPUs. Five groups of experimental tests are conducted on two machines to evaluate the computational efficiency of the presented GPU-accelerated RBF spatial interpolation algorithm. Experimental results indicate that: in most cases, the parallel RBF interpolation algorithm on many-core GPUs does not have any significant advantages over the parallel version on multi-core CPUs in terms of computational efficiency. This unsatisfied performance of the GPU-accelerated RBF interpolation algorithm is due to: (1) the limited size of global memory residing on the GPU, and (2) the need to solve a system of linear equations in each GPU thread to calculate the weights and prediction value of each interpolated point.
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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-017-0538-6