nvgraph.sh 1.2.44
Install from the command line:
Learn more about npm packages
$ npm install @nodef/nvgraph.sh@1.2.44
Install via package.json:
"@nodef/nvgraph.sh": "1.2.44"
About this version
CLI for nvGraph, which is a GPU-based graph analytics library written by NVIDIA, using CUDA.
π Shell,
π Files,
π Wiki.
This is for running nvGraph functions right from the CLI with graphs in
MatrixMarket format (.mtx
) directly. It just needs a x86_64 linux machine
with NVIDIA GPU drivers installed. Execution time, along with the results can be
saved in JSON/YAML file. The executable code is written in C++. You can
install this with npm install -g nvgraph.sh
.
nvGraph, as mentioned above, is a GPU-based graph analytics library written by NVIDIA. It provides four core graph algorithms: Single Source Shortest Path (SSSP), PageRank, Triangle count, and Breadth First Search (BFS) traversal. Data is loaded into the GPU in Compressed Sparse Row (CSR) format, upon which computation is performed. Here, we load the graph stored in MatrixMarket format (a text-based file format for sparse matrices) into a dynamic graph data structure in the host (CPU) memory, which is then converted to CSR format and then transferred to the device (GPU) memory. Computed results are then copied back to the host memory, and written to the output file in suitable format (JSON/YAML). Note that measured time only includes the time required for computation on the GPU.
The SSSP algorithm accepts the source vertex as an argument, and returns
the shortest distance to each vertex form the source vertex. The PageRank
algorithm accepts the damping factor, tolerance, and max. iterations as
arguments, and returns the rank of each vertex. In addition to ranks of
vertices in case of the PageRank algorithm, we like to also obtain additional
analytics of the rank values, i.e., Lorenz curve, and Gini coefficient. The
Triangle count algorithm, unsurprisingly, counts the number of triangles
in the graph. The BFS traversal algorithm accepts the source vertex as an
argument, traverses the graph in breadthwise manner, and returns the distance
and predecessor of each vertex from the source vertex. Note that
detailed results are written to the output file only when the full
(-f
) flag is provided.
Alas, nvGraph is now no more actively developed. NVIDIA started developing cuGraph a collection of graph analytics that process data found in GPU Dataframes as part of RAPIDS.
Stability: Experimental.
## Finds single source shortest path from source vertex
## β returns distances
$ nvgraph sssp -o=out.json -f web-Google.mtx -s=1
## Finds pagerank of all vertices
## β returns ranks
$ nvgraph pagerank -o=out.json -f web-Google.mtx -a=0.85 -t=1e-6
## Counts triangles in undirected, lower triangular graph
## β returns count
$ nvgraph triangle-count -o=out.json -f web-Google.mtx
## Traverses breadth-first from source vertex
## β returns distances, predecessors
$ nvgraph traversal-bfs -o=out.json -f web-Google.mtx -s=1
Command | Action |
---|---|
pagerank | Finds pagerank of all vertices. |
sssp | Finds single source shortest path from source vertex. |
traversal-bfs | Traverses breadth-first from source vertex. |
triangle-count | Counts triangles in undirected, lower triangular graph. |
- nvGraph pagerank example, EN605.617, JHU-EP-Intro2GPU
- nvGraph pagerank example, CUDA Toolkit Documentation
- nvGraph Library User's Guide
- RAPIDS nvGraph NVIDIA graph library
- Get path of current script when executed through a symlink