[1907.04217] Streaming 1.9 Billion Hypersparse Network Updates per Second with D4M

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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:1907.04217 (cs)

[Submitted on 6 Jul 2019]

Title:Streaming 1.9 Billion Hypersparse Network Updates per Second with D4M

Authors:Jeremy Kepner, Vijay Gadepally, Lauren Milechin, Siddharth Samsi, William Arcand, David Bestor, William Bergeron, Chansup Byun, Matthew Hubbell, Michael Houle, Michael Jones, Anne Klein, Peter Michaleas, Julie Mullen, Andrew Prout, Antonio Rosa, Charles Yee, Albert Reuther<br>View a PDF of the paper titled Streaming 1.9 Billion Hypersparse Network Updates per Second with D4M, by Jeremy Kepner and 17 other authors

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Abstract: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. Streaming updates of D4M associative arrays put enormous pressure on the memory hierarchy. This work describes the design and performance optimization of an implementation of hierarchical associative arrays that reduces memory pressure and dramatically increases the update rate into an associative array. The parameters of hierarchical associative arrays rely on controlling the number of entries in each level in the hierarchy before an update is cascaded. The parameters are easily tunable to achieve optimal performance for a variety of applications. Hierarchical arrays achieve over 40,000 updates per second in a single instance. Scaling to 34,000 instances of hierarchical D4M associative arrays on 1,100 server nodes on the MIT SuperCloud achieved a sustained update rate of 1,900,000,000 updates per second. This capability allows the MIT SuperCloud to analyze extremely large streaming network data sets.

Comments:<br>6 pages; 6 figures; accepted to IEEE High Performance Extreme Computing (HPEC) Conference 2019. arXiv admin note: text overlap with arXiv:1807.05308, arXiv:1902.00846

Subjects:

Distributed, Parallel, and Cluster Computing (cs.DC); Databases (cs.DB); Data Structures and Algorithms (cs.DS); Information Retrieval (cs.IR); Performance (cs.PF)

Cite as:<br>arXiv:1907.04217 [cs.DC]

(or<br>arXiv:1907.04217v1 [cs.DC] for this version)

https://doi.org/10.48550/arXiv.1907.04217

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arXiv-issued DOI via DataCite

Related DOI:

https://doi.org/10.1109/HPEC.2019.8916508

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Submission history<br>From: Jeremy Kepner [view email]<br>[v1]<br>Sat, 6 Jul 2019 20:55:04 UTC (452 KB)

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