Abstract
Spatial cross-matching operation over geospatial polygonal datasets is a highly compute-intensive yet an essential task to a wide array of real-world applications. At the same time, modern computing systems are typically equipped with multiple processing units capable of task parallelization and optimization at various levels. This mandates for the exploration of novel strategies in the geospatial domain focusing on efficient utilization of computing resources, such as CPUs and GPUs. In this paper, we present a CPU-GPU hybrid platform to accelerate the cross-matching operation of geospatial datasets. We propose a pipeline of geospatial subtasks that are dynamically scheduled to be executed on either CPU or GPU. To accommodate geospatial datasets processing on GPU using pixelization approach, we convert the floating point-valued vertices into integer-valued vertices with an adaptive scaling factor as a function of the area of minimum bounding box. We present a comparative analysis of GPU enabled cross-matching algorithm implementation in CUDA and OpenACC accelerated C++. We test our implementations over Natural Earth Data and our results indicate that although CUDA based implementations provide better performance, OpenACC accelerated implementations are more portable and extendable while still providing considerable performance gain as compared to CPU. We also investigate the effects of input data size on the IO / computation ratio and note that a larger dataset compensates for IO overheads associated with GPU computations. Finally, we demonstrate that an efficient cross-matching comparison can be achieved with a cost-effective GPU.
Highlights
Spatial data generation and availability have exploded over recent years due to the proliferation of GPS devices, location-based services, high-resolution imaging technologies and volunteered geographic information (Simion et al, 2012) systems, etc
We studied the effect of two different GPUs on the speedup ratio to process geospatial datasets and demonstrate the feasibility of computation with a cost-effective GPU
Hybrid approach (Gao et al, 2018) to perform spatial crossmatching on geospatial datasets focusing on potability, high resource utilization, and performance
Summary
Spatial data generation and availability have exploded over recent years due to the proliferation of GPS devices, location-based services, high-resolution imaging technologies and volunteered geographic information (Simion et al, 2012) systems, etc. In terms of spatial processing, different stages in the query pipeline have independent characteristics and dependencies making some of them suitable for CPU while others more fitting to be executed on GPU Inspired by these observations, our work intends to explore a hybrid approach by embracing heterogeneous computing strategy including both CPU and GPU. We extend our previous work (Wang et al, 2012; Aji et al, 2014; Gao et al, 2018) from the user case of pathological imaging analysis to enable spatial join query processing of geospatial datasets. Experimental evaluation with analysis of these implementations on real world dataset is presented in section 6 which is followed by conclusion
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