ACI Theses and Dissertations


Distributed Parallel Extreme Event Analysis in Next Generation Simulation Architectures

Date of Award

Fall 8-25-2017

Document Type

Doctoral Dissertation

USMA Research Unit Affiliation

Army Cyber Institute, Electrical Engineering and Computer Science

Degree Type

Doctor of Philosophy (PhD)

First Advisor

Randal Burns

Second Advisor

Alex Szalay

Third Advisor

Charles Meneveau


Numerical simulations present challenges as they reach exascale because they generate petabyte-scale data that cannot be saved without interrupting the simulation due to I/O constraints. Data scientists must be able to reduce, extract, and visualize the data while the simulation is running, which is essential for in transit and post analysis. Next generation architectures in supercomputing include a burst buffer technology composed of SSDs primarily for the use of checkpointing the simulation in case a restart is required. In the case of turbulence simulations, this checkpoint provides an opportunity to perform analysis on the data without interrupting the simulation. First, we present a method of extracting velocity data in high vorticity regions. This method requires calculating the vorticity of the entire dataset and identifying regions where the threshold is above a specified value. Next we create a 3D stencil from values above the threshold and dilate the stencil. Finally we use the stencil to extract velocity data from the original dataset. The result is a dataset that is over an order of magnitude smaller and contains all the data required to study extreme events and visualization of vorticity. The next extraction utilizes the zfp lossy compressor to compress the entire velocity dataset. The compressed representation results in a dataset an order of magnitude smaller than the raw simulation data. This provides the researcher approximate data not captured by the velocity extraction. The error introduced is bounded, and results in a dataset that is visually indistinguishable from the original dataset. Finally we present a modular distributed parallel extraction system. This system allows a data scientist to run the previously mentioned extraction algorithms in a distributed parallel cluster of burst buffer nodes. The extraction algorithms are built as modules for the system and run in parallel on burst buffer nodes. A feature extraction coordinator synchronizes the simulation with the extraction process. A data scientist only needs to write one module that performs the extraction or visualization on a single subset of data and the system will execute that module at scale on burst buffers, managing all the communication, synchronization, and parallelism required to perform the analysis.

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