Accelerating Graph Analysis on GPUs
Graph analysis plays a critical role in many applications across various domains, ranging from social network analysis to bioinformatics, to fraud detection, to cybersecurity, to recommendation systems, etc. NetworkX is the go-to library for graph analysis in Python. However, when dataset and graph sizes grow, the performance of using NetworkX becomes a significant concern. This webinar introduces NVIDIA cuGraph for accelerating graph analysis on GPUs. Moreover, a recent integration of NetworkX with cuGraph, named nx-cugraph, allows accelerating workflows in NetworkX on GPUs with zero code changes. A live demo will be done on the clusters.
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This webinar was presented by Jinhui Qin (SHARCNET) on March 27th, 2024, as a part of a series of weekly Compute Ontario Colloquia. The webinar was hosted by SHARCNET. The colloquia cover different advanced research computing (ARC) and high performance computing (HPC) topics, are approximately 45 minutes in length, and are delivered by experts in the relevant fields. Further details can be found on this web page: https://www.computeontario.ca/trainin... . Recordings, slides, and other materials can be found here: https://helpwiki.sharcnet.ca/wiki/Onl...
SHARCNET is a consortium of 19 Canadian academic institutions who share a network of high performance computers (http://www.sharcnet.ca). SHARCNET is a part of Compute Ontario (http://computeontario.ca/) and Digital Research Alliance of Canada (https://alliancecan.ca).
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