The NVIDIA Graph Analytics library (nvGRAPH) comprises of parallel algorithms for high performance analytics on graphs with up to 2 billion edges. nvGRAPH makes it possible to build interactive and high throughput graph analytics applications.

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 semi-ring SPMV operations can be used to build a wide range of innovative graph traversal algorithms.

nvGRAPH accelerates analysis of large graphs by making efficient use of the massive parallelism available in NVIDIA Tesla GPUs. The size of a graph in memory is dominated by the number of edges. An M40 with 24 GB can support a graph of up to 2 billion edges.

### Availability

The nvGRAPH library is freely available as part of the CUDA Toolkit

**Links of Interest**