Streaming 1.9 Billion Hypersparse Network Updates per Second with D4M

6 Jul 2019Jeremy KepnerVijay GadepallyLauren MilechinSiddharth SamsiWilliam ArcandDavid BestorWilliam BergeronChansup ByunMatthew HubbellMichael HouleMichael JonesAnne KleinPeter MichaleasJulie MullenAndrew ProutAntonio RosaCharles YeeAlbert Reuther

The Dynamic Distributed Dimensional Data Model (D4M) library implements associative arrays in a variety of languages (Python, Julia, and Matlab/Octave) and provides a lightweight in-memory database implementation of hypersparse arrays that are ideal for analyzing many types of network data. D4M relies on associative arrays which combine properties of spreadsheets, databases, matrices, graphs, and networks, while providing rigorous mathematical guarantees, such as linearity... (read more)

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