Georgia Tech is now the recipient of a NVIDIA Artificial Intelligence (AI) Lab (NVAIL) grant as part of the NVAIL program focusing on graph analytics on graphics processing units (GPUs).
Additionally, Georgia Tech, along with with Texas A&M, and U.C. Davis, will all contribute to the progress of the NVIDIA data science curriculum partnership this year, expanding the open source RAPIDS graph analytics algorithms.
Through these partnerships, Georgia Tech will work closely with NVIDIA researchers on GPU technologies and their application to data analytics. The goal of this collaboration is to build the world’s most advanced accelerated end-to-end pipeline for data analytics, and to support groundbreaking work of the world’s leading AI researchers and labs.
School of Computational Science and Engineering (CSE) Chair David Bader led the NVAIL program proposal effort as part of continuing work at Georgia Tech on GPU technology and scalable graph algorithms.
“Data science is the fastest growing field of computer science today. With this growth in mind, we are excited to further the collaborative relationship of Georgia Tech and NVIDIA,” said Bader. “We will focus on the design and implementation of scalable graph algorithms and primitives for integrating into cuGRAPH, leveraging their Hornet framework that will foster new abilities in the broader community of data scientists.”
As part of the awards, NVIDIA is providing:
- $100,000 cash award for one year toward research into scalable graph algorithms
- (1) NVIDIA DGX Station
- Collaboration on Deep Learning Institute Teaching Kits and educator outreach
- Opportunities for faculty and students of the Georgia Tech NVIDIA AI Lab to work at NVIDIA as visiting professors and interns
A text image of a graph with Georgia tech in between cuGraph and Hornet and UC Davis between cuGraph and Gunrock. NVIDIA points to cuGraph and nvGraph. cuGraph points to Texas A&M which points to SuiteSparse/GraphBLAS.