Digital soil mapping (DSM) at high spatial resolutions over large areas often demands considerable computing power. This study aims to harness the heterogeneous computing resources on multi-core central processing units (CPUs) and graphics processing units (GPUs) to accelerate DSM by implementing PyCLiPSM, a parallel version of the iPSM (individual predictive soil mapping) algorithm which represents the type of geospatial algorithms that is data- and compute-intensive and highly parallelizable. PyCLiPSM was implemented in Python based on the PyOpenCL parallel programming library, which runs on any operating system and exploits the computing power of both CPUs and GPUs. Experiments show that PyCLiPSM can effectively leverage multi-core CPUs and GPUs to speed up DSM tasks. PyCLiPSM is open-source and freely available. Using PyCLiPSM as an example, we advocate implementing parallel geospatial algorithms using the PyOpenCL framework to harness the heterogeneous computing resources available to researchers and practitioners for accelerated geospatial analysis.

中文翻译：

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PyCLiPSM：利用 CPU 和 GPU 上的异构计算资源来加速数字土壤测绘

大面积高空间分辨率的数字土壤制图 (DSM) 通常需要相当大的计算能力。本研究旨在利用多核中央处理器 (CPU) 和图形处理单元 (GPU) 上的异构计算资源，通过实施 PyCLiPSM 来加速 DSM，PyCLiPSM 是 iPSM（个人预测土壤映射）算法的并行版本，代表了类型数据和计算密集型且高度可并行化的地理空间算法。PyCLiPSM 是在基于 PyOpenCL 并行编程库的 Python 中实现的，该库可在任何操作系统上运行并利用 CPU 和 GPU 的计算能力。实验表明，PyCLiPSM 可以有效地利用多核 CPU 和 GPU 来加速 DSM 任务。PyCLiPSM 是开源的，可免费使用。以 PyCLiPSM 为例，