Intel Graph Analytics & AI: An Efficient Way to Analyze Massive Datasets

Graphs are playing a key role in big data analytics, providing insights in many domains through traditional graph algorithms and graph neural networks. As the size of these data sets increase, the computing power needed also increases and the software techniques to manage it becomes ever more critical. For example, there are Intel-based single node systems which can have up to 16TB of main memory with Optane DC PMM, large clusters of machines are available with thousands of cores, and specialized hardware for processing large scale graphs are being developed. Intel and Katana Graph are collaborating to produce an efficient and scalable graph analytics library that works across this wide variety of platforms.

Presented by Arijit Bandyopadhyay, CTO – Enterprise Analytics & AI and Head of Strategy – Cloud and Enterprise, Data Platforms Group, Intel Corporation, Ramesh Peri, Senior Principal Engineer at Intel Corporation, Intel’s Architecture, Graphics and Software Group, Intel Corporation, and Roshan Dathathri, Software Engineer, Katana Graph.