Lecture (After Call)

FZJ-2018-04961

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png High Performance and Cloud Computing for Remote Sensing Data

Cavallaro, G. (Corresponding author)FZJ* ; Erlingsson, E. ; Memon, A.FZJ*

2018

Please use a persistent id in citations: http://hdl.handle.net/2128/19637

Abstract: The development of the latest-generation sensors mounted on board of Earth observation platforms has led to a necessary re-definition of the challenges within the entire lifecycle of remote sensing data. The acquisition, processing and application phases face problems, which are well described by the Vs big data definitions: (1) Volume - the increasing scale of archived data (i.e., hundreds of Terabytes per day) raises not only data storage but also massive analysis issues, (2) Variety - the data are delivered by sensors acting over different spatial, spectral, radiometric and temporal resolutions, (3) Velocity - the data processing and analysis must confront the rapidly growing rate of data generation, (4) Veracity - the massive amount of sensed data coming in at high speed is associated with uncertainty and accuracy measurement and (5) Value - the acquired data are not straightforward to interpret and they require a powerful yet highly accurate processing scheme in order to extract reliable and valuable information. Traditional serial methods (i.e., desktop approaches, such as MATLAB, R, SAS, etc.) present several limitations and they become ineffective when considering these challenges. Despite modern desktop computers and laptops becoming increasingly multi core and performing better, they preserve limitations in terms of memory and core availability. Trends in parallel architectures like many-core systems (e.g. GPUs) are in continuous expansion to satisfy the growing demands of domain-specific applications for handling computationally intensive problems. In the context of large scale remote sensing applications, High Performance Computing (HPC) and Cloud Computing have the chance of overcoming the limitations of serial algorithms. Parallel architectures such as clusters, grids, or clouds provide tremendous computation capacity and outstanding scalability underpinned by strong and stable standards used for decades, e.g. message passing interface (MPI). The tutorial aims at providing a complete overview for an audience that is not familiar with these topics. The tutorial will follow a twofold approach: selected background lectures (morning session) followed by practical hands-on exercises (afternoon session) in order to enable you to perform your own research. The tutorial will discuss the fundamentals of supercomputing as well as cloud computing, and how we can take advantage of such systems to solve remote sensing problems that require fast and highly scalable solutions.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 512 - Data-Intensive Science and Federated Computing (POF3-512) (POF3-512)
2. DEEP-EST - DEEP - Extreme Scale Technologies (754304) (754304)
3. PhD no Grant - Doktorand ohne besondere Förderung (PHD-NO-GRANT-20170405) (PHD-NO-GRANT-20170405)

Appears in the scientific report 2018

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Database coverage:

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