利用CUDA的地图代数局部算子优化
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摘要
本文针对地图代数局部算子的传统实现方法应用于海量栅格数据计算时效率低下的问题,从串行算法的并行化映射、计算机图形处理器资源的自适应参数调整等多角度来研究地图代数空间并行算法的实现机制,总结出地图代数局部算子在GPU并行处理架构上的通用求解步骤。实验结果表明,该方法在大数据量处理时较CPU加速效果明显。
Aiming at the low efficiency when traditional realization methods of local arithmetic operators of map algebra apply to calculations for gigantic raster data,this paper discussed the realization mechanism from surrounding parallel mapping of serial algorithms and adaptive parameter adjustments on computer graphic processor resources.Then it summarized the common solving procedures of implementing those local operators on GPU parallel processing architecture.The experimental results demonstrated that the proposed method could achieve better accelerating effect than that of CPU.
引文
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