Many-Core Acceleration of Common Graph Programming Frameworks

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W911NF-15-P-0041
Agency Tracking Number: D152-004-0139
Amount: $149,964.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: SB152-004
Solicitation Number: 2015.2
Timeline
Solicitation Year: 2015
Award Year: 2015
Award Start Date (Proposal Award Date): 2015-09-24
Award End Date (Contract End Date): 2016-06-23
Small Business Information
51 East Main Street, Newark, DE, 19711
DUNS: 071744143
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Paul Fox
 Engineer
 (302) 456-9003
 fox@emphotonics.com
Business Contact
 Mr. Eric Kelmelis
Title: program manager
Phone: (302) 456-9003
Email: kelmelis@emphotonics.com
Research Institution
N/A
Abstract
Graph analytics is a key component in identifying emerging trends and threats in many real-world applications. Large-scale graph analytics frameworks provide a convenient and highly scalable platform for developing algorithms to analyze large datasets. Although conceptually scalable, these techniques exhibit poor performance on modern computational hardware. Another model of graph computation has emerged that promises improved performance and scalability by using abstract linear algebra operations as the basis for graph analysis as laid out by the GraphBLAS standard. By using sparse linear algebra as the basis, existing highly efficient algorithms can be adapted to perform computations on the graph. This approach, however, is often less intuitive to graph analytics experts, who are accustomed to vertex-centric APIs such as Giraph, GraphX, and Tinkerpop. EM Photonics therefore proposes an implementation of the high-level operations supported by these APIs in terms of linear algebra operations, which will be parallel on each pair of vertices connected by an edge. This implementation will be backed by many-core implementations of the fundamental GraphBLAS operations required. This approach offers the advantages of both the intuitive programming model of a vertex-centric API and the performance of a sparse linear algebra implementation.

* Information listed above is at the time of submission. *

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