Corey J. Nolet
Corey is a principal engineer on the RAPIDS ML team at NVIDIA, where he builds machine learning algorithms that support extreme data loads at light speed. Prior to joining NVIDIA 5 years ago, Corey spent over a decade building massive-scale exploratory data science & real-time analytics platforms for big-data and HPC environments in the defense industry. Corey holds Bs. & Ms. degrees in Computer Science. He is also finishing up his Ph.D. in the same discipline, focused on the acceleration of algorithms at the intersection of graph and machine learning. Corey has a passion for using data to make better sense of the world.
Nvidia, Inc.
Session
This talk focuses on how we leveraged Nvidia’s open source cuVS library to accelerate Apache Lucene’s vector search capabilities on the GPU. cuVS contains several algorithms for approximate nearest neighbors and clustering on the GPU and we show how it pairs well with Lucene, which has long been the