A technical paper titled “ACTS: A Near-Memory FPGA Graph Processing Framework” was published by researchers at University of Virginia and Samsung. “Despite the high off-chip bandwidth and on-chip ...

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More What do you get when you combine two of the most up-and-coming paradigms ...

TigerGraph, provider of a leading graph analytics platform, is introducing the TigerGraph ML (Machine Learning) Workbench—a powerful toolkit that enables data scientists to significantly improve ML ...

In this video from the 2016 Blue Waters Symposium, George Slota from Pennsylvania State University presents: Extreme-scale Graph Analysis on Blue Waters. “In recent years, many graph processing ...

“Spark GraphX in Action” book from Manning Publications, authored by Michael Malak and Robin East, provides a tutorial based coverage of Spark GraphX, the graph data processing library from Apache ...

Graph processing at hyperscale has historically been a challenge because of the sheer complexity of algorithms and graph workflows. Alibaba has been tackling this issue via a project called GraphScope ...

Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with content, and download exclusive resources. Vivek Yadav, an engineering manager from ...

Although traditional relational databases will not be disappearing anytime soon, for a growing set of data-intensive problems, graph-based approaches are still finding a fit, even well after the boom ...

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 ...

A team of researchers led by professor Xindong Wu in Hefei, China has developed an unsupervised entity alignment framework to improve the process of searching for related information in multiple ...