Disaster Monitoring using Grid Based Data Fusion Algorithms
Journal Title: INCAS BULLETIN - Year 2010, Vol 2, Issue 4
Abstract
This is a study of the application of Grid technology and high performance parallel computing to a candidate algorithm for jointly accomplishing data fusion from different sensors. This includes applications for both image analysis and/or data processing for simultaneously tracking multiple targets in real-time. The emphasis is on comparing the architectures of the serial and parallel algorithms, and characterizing the performance benefits achieved by the parallel algorithm with both on-ground and in-space hardware implementations. The improved performance levels achieved by the use of Grid technology (middleware) for Parallel Data Fusion are presented for the main metrics of interest in near real-time applications, namely latency, total computation load, and total sustainable throughput. The objective of this analysis is, therefore, to demonstrate an implementation of multi-sensor data fusion and/or multi-target tracking functions within an integrated multi-node portable HPC architecture based on emerging Grid technology. The key metrics to be determined in support of ongoing system analyses includes: required computational throughput in MFLOPS; latency between receipt of input data and resulting outputs; and scalability, processor utilization and memory requirements. Furthermore, the standard MPI functions are considered to be used for inter-node communications in order to promote code portability across multiple HPC computer platforms, both in space and on-ground.
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- EP ID EP124291
- DOI 10.13111/2066-8201.2010.2.4.19
- Views 113
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