Interacting with Accumulo and Graphulo using Julia/Python D4M

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Graph APIs/Frameworks


Julia and Python have become dominant languages in scientific computing, prompting the implementation of D4M in those languages. Graphulo provides a convenient mechanism for analyzing data within Accumulo using in-database graph analytics. The MATLAB®/Octave interface for Graphulo, accessible through the D4M library, provides both Accumulo and Graphulo capabilities to analysts comfortable with the matrix-based syntax of MATLAB and D4M. By introducing the Julia and Python implementations of D4M, including the Graphulo connectors, we have made D4M, Graphulo, and Accumulo more accessible to the Julia and Python community – already being explored is using Julia D4M to use Accumulo to store human brain-scale sparse deep neural networks.

In this presentation, we will introduce to the Accumulo community the Julia and Python implementations of D4M and demonstrate how users can leverage those to interface with Graphulo and Accumulo.


Lauren Milechin
Sponsored Research Technical Staff, MIT EAPS

Lauren Milechin is Technical Staff at MIT Earth, Atmospheric, and Planetary Sciences. Her interests and projects involve big data, database technology, machine learning, applications of these topics to other fields, and producing educational materials in these areas. Lauren also facilitates researchers on the MIT SuperCloud supercomputing system. Previously, Lauren worked as Associate Technical Staff at the Lincoln Laboratory Supercomputing Center.

Ms. Milechin received an MS degree in industrial mathematics from the University of Massachusetts, Lowell, focusing in computer science applications, such as machine learning and algorithms. She holds a BS degree in mathematical sciences from Worcester Polytechnic Institute, where she explored mathematical modeling of disease and of population dynamics.

Hayden Jananthan
PhD Student, Vanderbilt University

Hayden Jananthan is a mathematics educator. He is a certified high school mathematics teacher and has taught mathematics in Boston area public schools. He has also taught pure mathematics in a variety of programs for gifted high school students at MIT and at other institutions of higher learning. Hayden has been a researcher at MIT Lincoln Laboratory, supervising undergraduate researchers from MIT and CalTech, and authored a number of peer-reviewed papers on the application of mathematics to big data problems. His work has been instrumental in defining the mathematical foundations of associative array algebra and its relationship to other branches of pure mathematics. Hayden holds a BS degree in mathematics from MIT and is pursuing a PhD in pure mathematics at Vanderbilt University, working with Steve Simpson in Mathematical Logic.

Vijay Gadepally
Senior Technical Staff, MIT Lincoln Laboratory

Dr. Vijay Gadepally is a senior member of the technical staff at MIT Lincoln Laboratory. Vijay's research is in the area of high performance computing, big data/IoT systems, security, analytics, and advanced database technologies. He holds a Ph.D. in Electrical and Computer Engineering from The Ohio State University and a B.Tech degree in Electrical Engineering from the Indian Institute of Technology (IIT), Kanpur. In 2017, Vijay received the Early Career Technical Achievement Award at MIT Lincoln Laboratory and was named to AFCEA's inaugural 40 under 40 list. In 2011, Vijay received an Outstanding Graduate Student Award at The Ohio State University. Vijay has also worked at Raytheon Company and Rensselaer Polytechnic Institute. Vijay has over 50 peer reviewed publications and talks.

Jeremy Kepner
Fellow, MIT

Dr. Jeremy Kepner is a MIT Lincoln Laboratory Fellow. He founded the Lincoln Laboratory Supercomputing Center and pioneered the establishment of the Massachusetts Green High Performance Computing Center. He has developed novel big data and parallel computing software used by thousands of scientists and engineers worldwide. He has led several embedded computing efforts, which earned him a 2011 R&D 100 Award. Dr. Kepner has chaired SIAM Data Mining, the IEEE Big Data conference, and the IEEE High Performance Extreme Computing conference.

Dr. Kepner is the author of two bestselling books, Parallel MATLAB and Graph Algorithms in the Language of Linear Algebra. His peer-reviewed publications include works on abstract algebra, astronomy, cloud computing, cybersecurity, data mining, databases, graph algorithms, health sciences, signal processing, and visualization. Dr. Kepner holds a BA degree in astrophysics from Pomona College and a PhD degree in astrophysics from Princeton University.