TR-12-4.pdf

"Accelerating pathology image data cross-comprison on CPU-GPU hybrid systems", 

Kaibo Wang, Yin Huai, Rubao Lee, Fusheng Wang, Xiaodong Zhang, and Joel H. Saltz

Proceedings of 38th International Conference on Very Large Databases 
(VLDB 2012), Istanbul, Turkey, August 27-31, 2012.  


Abstract
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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