Streaming 1.9 billion hypersparse network updates per second with D4M

Jeremy Kepner, Michael Houle, Michael Jones, Anne Klein, Peter Michaleas, Julie Mullen, Andrew Prout, Antonio Rosa, Charles Yee, Albert Reuther, Vijay Gadepally, Lauren Milechin, Siddharth Samsi, William Arcand, David Bestor, William Bergeron, Chansup Byun, Matthew Hubbell

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

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.

Original languageEnglish
Title of host publication2019 IEEE High Performance Extreme Computing Conference, HPEC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728150208
DOIs
StatePublished - Sep 2019
Externally publishedYes
Event2019 IEEE High Performance Extreme Computing Conference, HPEC 2019 - Waltham, United States
Duration: Sep 24 2019Sep 26 2019

Publication series

Name2019 IEEE High Performance Extreme Computing Conference, HPEC 2019

Conference

Conference2019 IEEE High Performance Extreme Computing Conference, HPEC 2019
Country/TerritoryUnited States
CityWaltham
Period09/24/1909/26/19

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