Evaluating the Power of GPU Acceleration for IDW Interpolation Algorithm
We first present two GPU implementations of the standard Inverse Distance Weighting (IDW) interpolation algorithm, the tiled version that takes advantage of shared memory and the CDP version that is implemented using CUDA Dynamic Parallelism (CDP). Then we evaluate the power of GPU acceleration for...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/171574 |
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| author | Gang Mei |
| author_facet | Gang Mei |
| author_sort | Gang Mei |
| collection | DOAJ |
| description | We first present two GPU implementations of the standard Inverse Distance Weighting (IDW) interpolation algorithm, the tiled version that takes advantage of shared memory and the CDP version that is implemented using CUDA Dynamic Parallelism (CDP). Then we evaluate the power of GPU acceleration for IDW interpolation algorithm by comparing the performance of CPU implementation with three GPU implementations, that is, the naive version, the tiled version, and the CDP version. Experimental results show that the tilted version has the speedups of 120x and 670x over the CPU version when the power parameter p is set to 2 and 3.0, respectively. In addition, compared to the naive GPU implementation, the tiled version is about two times faster. However, the CDP version is 4.8x∼6.0x slower than the naive GPU version, and therefore does not have any potential advantages in practical applications. |
| format | Article |
| id | doaj-art-fc6cf333c1e24cd88620d154fd56132e |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-fc6cf333c1e24cd88620d154fd56132e2025-02-03T01:10:38ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/171574171574Evaluating the Power of GPU Acceleration for IDW Interpolation AlgorithmGang Mei0Institute of Earth and Environmental Science, University of Freiburg, Albertstraße 23B, 79104 Freiburg im Breisgau, GermanyWe first present two GPU implementations of the standard Inverse Distance Weighting (IDW) interpolation algorithm, the tiled version that takes advantage of shared memory and the CDP version that is implemented using CUDA Dynamic Parallelism (CDP). Then we evaluate the power of GPU acceleration for IDW interpolation algorithm by comparing the performance of CPU implementation with three GPU implementations, that is, the naive version, the tiled version, and the CDP version. Experimental results show that the tilted version has the speedups of 120x and 670x over the CPU version when the power parameter p is set to 2 and 3.0, respectively. In addition, compared to the naive GPU implementation, the tiled version is about two times faster. However, the CDP version is 4.8x∼6.0x slower than the naive GPU version, and therefore does not have any potential advantages in practical applications.http://dx.doi.org/10.1155/2014/171574 |
| spellingShingle | Gang Mei Evaluating the Power of GPU Acceleration for IDW Interpolation Algorithm The Scientific World Journal |
| title | Evaluating the Power of GPU Acceleration for IDW Interpolation Algorithm |
| title_full | Evaluating the Power of GPU Acceleration for IDW Interpolation Algorithm |
| title_fullStr | Evaluating the Power of GPU Acceleration for IDW Interpolation Algorithm |
| title_full_unstemmed | Evaluating the Power of GPU Acceleration for IDW Interpolation Algorithm |
| title_short | Evaluating the Power of GPU Acceleration for IDW Interpolation Algorithm |
| title_sort | evaluating the power of gpu acceleration for idw interpolation algorithm |
| url | http://dx.doi.org/10.1155/2014/171574 |
| work_keys_str_mv | AT gangmei evaluatingthepowerofgpuaccelerationforidwinterpolationalgorithm |