Efficient GPU Techniques for Processing Temporally Correlated Satellite Image Data

Spatio-temporal processing has many usages in different scientific domains, e.g., geostatistical processing, video processing and signal processing. Spatio-temporal processing typically operates on massive volume multi-dimensional data that make cacheefficient processing challenging. In this paper, we present highlights of our ongoing work on efficient parallel processing of spatio-temporal data on massively parallel many-core platforms, GPUs. Our example application solves a unique problem within Interferometric Synthetic Aperture Radar (InSAR) processing pipeline. The goal is selecting objects that appear stable across a set of satellite images taken over time. We present three GPU approaches that differ in terms of thread mapping, parallel efficiency and memory access patterns. We conduct roofline analysis [4] to understand how the most time consuming GPU kernel can be improved. Through detailed benchmarking using hardware counters, we gain insights into runtime performance of the GPU techniques and discuss their tradeoffs.