Nvidia nvGraph and Tesla P100 GPU

“The latest version of NVIDIA’s parallel computing platform gives developers direct access to powerful new Pascal features, including unified memory and NVLink. Also included in this release is a new graph analytics library — nvGRAPH — which can be used for robotic path planning, cyber security and logistics analysis, expanding the application of GPU acceleration into the realm of big data analytics.”

nvGRAPH supports three widely-used algorithms:

Page Rank is most famously used in search engines, and also used in social network analysis, recommendation systems, and for novel uses in natural science when studying the relationship between proteins and in ecological networks.

Single Source Shortest Path is used to identify the fastest path from A to B through a road network, and can also be used for a optimizing a wide range of other logistics problems.

Single Source Widest Path is used in domains like IP traffic routing and traffic-sensitive path planning.

In addition, the nvGRAPH semiring Sparse Matrix Vector Multiplication (SPMV) operations can be used to build a wide range of innovative graph traversal algorithms.

A paper on how to represent cyber attacks as graphs – http://csis.gmu.edu/noel/pubs/2015_IEEE_HST.pdf references the CAPEC, which is a collection of vulnerabilities for such a graph study.

A graphdb represents data using nodes, edges and properties.

graphdatabase_propertygraph

It allows querying with a graph traversal language such as Gremlin. Blazegraph offers a GPU accelerated graphdb. Here’s a graph of machine learning papers from Research Front Maps.

rfmap1

MapD, a GPU accelerated db recently got funding from Nvidia and others.

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