04-18, 11:30–12:05 (US/Eastern), Auditorium 4
Graphs are a fundamental form of storing data. This is because everything is connected! Hence, Graphs are very useful for modeling and solving a wide variety of real-world problems.
While NetworkX is amazing for getting started with Graphs, the library encounters bottlenecks in performance at scale.
Is there a solution out there for users who want more performance from NX and also Open-Source developers who want to implement fast algorithms? Yes! Thanks to the magic of dispatching.
NetworkX now supports dispatching to various backends, including the GPU accelerated cuGraph library by Nvidia RAPIDS.
Attend this talk to learn about how you can use nx-cugraph – the cuGraph-powered backend for NetworkX – and how it unlocks exciting new possibilities for you to solve real-world graph analytics problems.
This talk will showcase a GPU accelerated graph backend presented by NVIDIA in partnership with the NetworkX Community. It aims to showcase how GPUs are well-suited to solving graph problems at large scales.
The talk is intended for Python developers who are interested in using GPUs in their workflows and data scientists interested in Graph analytics.
During the talk, we intend to go over the following.
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Brief introduction to Graphs and why Graph Analytics is so powerful.
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Introducing NetworkX – Why is it so popular? What are its limitations?
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Example showcasing the magic of Dispatching. The design philosophy and how it benefits both users and OS developers.
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Real-world example on the Pokec (Social Network) dataset. How to do Community Detection on a large Graph using Louvain (with Zero Code Change)!
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Finally, how we aim to work with the community to add new algorithm implementations and contribute to upstream NetworkX.
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Q&A!
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Learn more:
I'd love to connect with you and discuss ideas of applying Graph analytics to your work.
Reach out via LinkedIn
No previous knowledge expected
Ralph is currently a software engineer at NVIDIA, working on GPU-accelerated graph libraries (cuGraph, nx-cugraph) as a part of RAPIDS.