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Parallel Multiway Methods for Compression of Massive Data and Other Applications

Tamara Kolda from Sandia gave this Invited Talk at SC16. “Scientists are drowning in data. The scientific data produced by high-fidelity simulations and high-precision experiments are far too massive to store. For instance, a modest simulation on a 3D grid with 500 grid points per dimension, tracking 100 variables for 100 time steps yields 5TB of data. Working with this massive data is unwieldy and it may not be retained for future analysis or comparison. Data compression is a necessity, but there are surprisingly few options available for scientific data.”