DataSToRM: Data Science and Technology
Research Environment
Technology Office | Lincoln Laboratory
The above is an illustration of a graph that has been analyzed by a community detection algorithm in order to reveal groups of similar vertices—here, circles with the same color belong to the same community. As an example, in social network graphs, these communities may represent groups of people sharing similar tastes in music or a common set of friends.
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nformation such as proteininteractions or social networks can be represented with a data structure called a graph. By analyzing these graphs using specialized algorithms, complex relationships and deeper insight can be extracted from the raw information. Analyzing customer buying habits for targeted recommendations and finding new medical applications for existing drugs are two of many applications. In the last several years, the Laboratory has developed the Graph Processor, which has a unique hardware architecture that provides 100 to 1000 times better processing performance for analyzing large graph datasets. To put this performance improvement in perspective, even basic analysis, which can take several hours or days to compute in a super-computing center, can be performed in seconds or minutes.
The Data Science and
Technology Research Environment (DataSToRM) program is developing a software environment and
algorithms to take advantage of the Graph Processor’s capabilities. The environment will be closely integrated with the Lincoln
Laboratory Supercomputing Center infrastructure, which will enable
development of Department of Defense applications that can leverage both traditional computing resources, such as central processing units or graphics processing units, and the specialized graph hardware. The initial focus of the effort is to develop basic capabilities that will become the foundation for a full suite of graph analysis tools. Utilizing this
environment, the program is looking to enable research and development of new high-performance algorithms as well as continued hardware innovation for accelerating graph applications.
February 09, 2018
MIT LINCOLN LABORATORY
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U P E R C O M P U T I N GC
E N T E RDISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited. This material is based upon work supported by the United States Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force.
