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- W4308264385 abstract "AbstractSpatial data processing had been a dominant contributor in a wide assortment of applications including health care, urban planning and infrastructure designing. As the complexity of data has increased due to higher dimensions, diverse approaches have been successfully tested for nearest neighbor queries, clustering algorithms, etc. These algorithms have been optimized and adapted for CPU, GPU and hybrid models as combination of CPU and GPU. Closest pair (CP) computation is a frequently used operation in range and distance-based queries. Although there are different algorithms and optimizations available for CP computations, suitability of GPU for this operation is yet to be explored. Hereby in this paper we propose CPU-GPU Hybrid Model, to optimize the closest pair (CP) of points problem concerning two-dimensional floating point values. As part of the combined model, the sorting phase is implemented on CPU and computation of closest pair is implemented on GPU. In this work, three CP algorithms are analyzed on CPU and CPU-GPU combinations. With the proposed algorithm on the CPU-GPU combined model, up to 12 times speedup is achieved in the closest pair computation time. Several parameters were tuned to work with the device-specific features and the overhead in terms of data transfer is also analyzed.KeywordsClosest pairCPU-GPU combined modelDivide and ConquerQuick CP" @default.
- W4308264385 created "2022-11-09" @default.
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- W4308264385 date "2022-11-06" @default.
- W4308264385 modified "2023-10-16" @default.
- W4308264385 title "Optimized Closest Pair Computation with CPU-GPU Combined Model" @default.
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- W4308264385 doi "https://doi.org/10.1007/978-981-19-5224-1_74" @default.
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