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 ...

NVIDIA introduces GPU acceleration for NetworkX using cuGraph, offering significant speed improvements in graph analytics without code changes, ideal for large-scale data processing. NVIDIA has ...

We believe these errors are due to your build environment. Our build documentation is out-of-date. All of RAPIDS now requires gcc 11.4 or greater, and nvcc 11.8 or greater. We are using compiler ...

Hey everyone! I recently passed the NVIDIA Data Science Professional Certification, and I'm thrilled to share some insights to help you on your journey. This is part of a series where I'll break down ...

RAPIDS cugraph_dgl enables the ability to use cugraph Property Graphs with DGL. This cugraph backend allows DGL users access to a collection of GPU-accelerated algorithms for graph analytics, such as ...

Abstract: Software development of high-performance graph algorithms is difficult on modern parallel computers. To simplify this task, we have designed and implemented a collection of C++ graph ...