4A_ 3_Parallel k-means clustering using gp_us for the geocomputation of real-time geodemographics

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GISRUK conference

Parallel k-means clustering using GPUs for the Geocomputation of Real-time Geodemographics Muhammad Adnan, Alex Singleton, Paul Longley

Presentation Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Geodemographic Classifications ,[object Object],[object Object],[object Object],[object Object],[object Object]

Does one size fit all ? OAC (Output Area Classification): London OAC (Output Area Classification): Birmingham

Does one size fit all ? ,[object Object],[object Object],Employment classification of Yorkshire and Humber OAC (Output Area Classification)

Does one size fit all ? ,[object Object],[object Object],Employment classification of Yorkshire and Humber OAC (Output Area Classification) ,[object Object],[object Object],[object Object]

Live data is available on the web ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Major challenge for ‘on the fly’ classifications ,[object Object],[object Object],[object Object],[object Object],[object Object]

Enhancements for K-means ,[object Object],[object Object],[object Object]

Nvidia Graphics Cards ,[object Object],[object Object],[object Object],[object Object],[object Object],Has 1000 GPUs

Parallel k-means ,[object Object],[object Object],[object Object],Step-1 CPU ,[object Object],[object Object],[object Object]

Parallel k-means ,[object Object],[object Object],[object Object],Step-1 CPU ,[object Object],[object Object],[object Object],Step-2 Graphics Card GPU-1 GPU-2 GPU-3 GPU-N ,[object Object],[object Object]

Comparing k-means and Parallel k-means ,[object Object],OA (Output Area) Level results

Comparing k-means and Parallel k-means ,[object Object],LSOA (Lower Super Output Area) Level results

Efficiency achieved by using Parallel K-means ,[object Object],[object Object],No. of clusters K-means Parallel K-means Throughput 7 9 sec. 0.54 sec. 94% 12 25 sec. 1.5 sec. 93% 52 38 sec. 2.16 sec. 89%

Within sum of squares of K-means ,[object Object]

2 nd performance enhancement for k-means Establishing a threshold value ,[object Object]

Testing the approach ,[object Object],K-means for K=7 ,[object Object],Run Number Convergence achieved 1 1016 runs 2 928 runs 3 1800 runs 4 826 runs

Conclusion & Future Work ,[object Object],[object Object],[object Object],[object Object]

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