Accelerating spatial clustering detection of epidemic disease with graphics processing unit

• Published in: 2010 18th International Conference on Geoinformatics pp. 1 - 6
• Main Authors: Sisi Zhao, Chenghu Zhou
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2010
• Subjects: Central Processing Unit; Clustering detection; Diseases; Epidemic disease; GPGPU; Graphics processing unit; Instruction sets; Kernel; Monte Carlo methods; Monte Carlo simulation; Runtime; Spatial analysis
• ISBN: 1424473012; 9781424473014
• ISSN: 2161-024X
• DOI: 10.1109/GEOINFORMATICS.2010.5567882

The statistics of disease clustering is of interest to epidemiologists. In order to detect spatial clustering of disease in all the regions of China, we adopted a likelihood ratio based method which utilizes Monte Carlo simulation and spatial exploring to analyze the real time updating data stored in database. However, large number of random tests for Monte Carlo simulation and large scale of the data set had made the speed of analysis too slow to detect and monitor potential public health hazards. Therefore, we explored to adopt graphics processing unit (GPU) and compute unified device architecture (CUDA) to accelerate the spatial exploring and analyzing process. The algorithm has been implemented efficiently on GPU and the access pattern to memory has been optimized to exploit the computing power of GPU. As a result, the GPU based spatial exploring and likelihood ratio test program performed more than forty times faster then the CPU implementation. The Monte Carlo simulation on GPU performed around thirty times faster than the counter part on CPU. By using GPU and CUDA, the usage of our application is changed from verification after the event to early warning.