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Accelerating Pathology Image Data Cross-Comparison on CPU-GPU Hybrid Systems

Kaibo Wang, Yin Huai, Rubao Lee, Fusheng Wang, Xiaodong Zhang, Joel H. Saltz
Department of Computer Science and Engineering, The Ohio State University
arXiv:1208.0277v1 [cs.DB], 1 Aug 2012

@article{wang2012accelerating,

   title={Accelerating Pathology Image Data Cross-Comparison on CPU-GPU Hybrid Systems},

   author={Wang, Kaibo and Huai, Yin and Lee, Rubao and Wang, Fusheng and Zhang, Xiaodong and Saltz, Joel H.},

   year={2012},

   archivePrefix={"arXiv"},

   eprint={"1208.0277"},

   primaryClass={"cs.db"}

}

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As an important application of spatial databases in pathology imaging analysis, cross-comparing the spatial boundaries of a huge amount of segmented micro-anatomic objects demands extremely data- and compute-intensive operations, requiring high throughput at an affordable cost. However, the performance of spatial database systems has not been satisfactory since their implementations of spatial operations cannot fully utilize the power of modern parallel hardware. In this paper, we provide a customized software solution that exploits GPUs and multi-core CPUs to accelerate spatial cross-comparison in a cost-effective way. Our solution consists of an efficient GPU algorithm and a pipelined system framework with task migration support. Extensive experiments with real-world data sets demonstrate the effectiveness of our solution, which improves the performance of spatial cross-comparison by over 18 times compared with a parallelized spatial database approach.
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