SCHEDULE: NOV 15-20, 2015

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Efficient GPU Techniques for Processing Temporally Correlated Satellite Image Data

SESSION: Regular & ACM Student Research Competition Poster Reception

EVENT TYPE: Posters, Receptions, ACM Student Research Competition

EVENT TAG(S): HPC Beginner Friendly, Regular Poster

TIME: 5:15PM - 7:00PM

SESSION CHAIR(S): Michela Becchi, Manish Parashar, Dorian C. Arnold

AUTHOR(S):Tahsin A. Reza, Dipayan Mukherjee, Tanuj Kr Aasawat, Matei Ripeanu

ROOM:Level 4 - Lobby

ABSTRACT:

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 cache-efficient 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.

Chair/Author Details:

Michela Becchi, Manish Parashar, Dorian C. Arnold (Chair) - University of Missouri|Rutgers University|University of New Mexico|

Tahsin A. Reza - University of British Columbia

Dipayan Mukherjee - Indian Institute of Technology Kharagpur

Tanuj Kr Aasawat - University of British Columbia

Matei Ripeanu - University of British Columbia

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