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

TL;DR

This paper presents a GPU-accelerated system for spatial cross-comparison in pathology imaging, significantly boosting performance over traditional spatial database methods by over 18 times.

Contribution

It introduces a customized GPU algorithm and a pipelined framework with task migration for efficient spatial data cross-comparison.

Findings

Performance improved by over 18 times

Effective utilization of GPU and CPU resources

Cost-effective acceleration of spatial operations

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.