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引用次数: 1
摘要
地理空间数据可能非常庞大，在标准计算工作站上进行有效处理非常繁琐。通过高度并行化的处理来分配处理任务可以减少主处理器的负担，从而大大缩短处理时间。ERSI的ArcGIS虽然广泛应用于军事领域，但它本身并不支持多核处理或图形处理器单元(gpu)的利用。然而，ArcGIS 10中包含的ArcPy Python库为地理空间开发人员提供了在灵活的环境中处理地理空间数据的方法，该环境可以与GPU应用程序编程接口(api)相关联。本研究扩展了用于远程土壤分类的自定义桌面地理空间模型，该模型利用了标准ArcPy/ArcGIS地理处理功能和自定义GPU内核，在配备1792个内核的NVIDIA Tesla S2050上运行。作者将介绍他们的结果，其中描述了硬件和软件配置，处理效率的提高，和经验教训。
Accelerating Geospatial Modeling in ArcGIS with Graphical Processor Units
Geospatial data can be enormous in size and tedious to process efficiently on standard computational workstations. Distributing the processing tasks through highly parallelized processing reduces the burden on the primary processor and processing times can drastically shorten as a result. ERSI's ArcGIS, while widely used in the military, does not natively support multi-core processing or utilization of graphic processor units (GPUs). However, the ArcPy Python library included in ArcGIS 10 provides geospatial developers with the means to process geospatial data in a flexible environment that can be linked with GPU application programming interfaces (APIs). This research extends a custom desktop geospatial model of spatial similarity for remote soil classification which takes advantage of both standard ArcPy/ArcGIS geoprocessing functions and custom GPU kernels, operating on an NVIDIA Tesla S2050 equipped with potential access to 1792 cores. The author will present their results which describe hardware and software configurations, processing efficiency gains, and lessons learned.
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