A new open-source library by Nvidia could be the secret ingredient to advancing analytics and making graph databases faster. The key: parallel processing on Nvidia GPUs.

A new open-source library by Nvidia could be the secret ingredient to advancing analytics and making graph databases faster. The key: parallel processing on Nvidia GPUs.

Nvidia has long ago stopped being “just” a hardware company. As its hardware is what much of the compute supporting the explosion in AI runs on, Nvidia has taken upon itself the task of paving the last mile to the software. Nvidia does this by developing and releasing libraries that software developers and data scientists can use to integrate GPU power in their work.

The premise is simple: Not everyone is a specialist in parallelism or wants to be one. Parallel programming is hard. Yet, this is what is required to take advantage of GPU capabilities and boost performance in software and analytics. So, Nvidia provides libraries people can use to build their software, without knowing all the implementation and hardware details.

Nvidia has been doing this with CUDA since 2007. Since then, Nvidia has released more than 40 Nvidia CUDA-X libraries, with the most recent being Rapids, an open-source data science platform that is the umbrella under which several initiatives such as Dask or XGBoost for data science have evolved.

Nvidia is now releasing Rapids cuGraph 0.9, a library whose goal is to make graph analysis ubiquitous. This could be the foundation for major developments in graph analytics and graph databases. Graph is a field we have been closely monitoring, but we’re no longer the only ones, and that’s not the only reason why we think this is big.

This brief excerpt is taken out of Gartner’s analysis on why graph will rule the world in the 2020s:

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