Corey Nolet
Principal Engineer, Nvidia, Inc.
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Corey is a data scientist and principal engineer on the RAPIDS ML team at NVIDIA, where he focuses on building and scaling machine learning algorithms to support extreme data loads at light speed. Prior to working at NVIDIA, 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 working towards 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.
GPU accelerated vector search using Apache Solr and Nvidia’s cuVS library
This talk focuses on enabling GPU accelerated vector search in Apache Solr using Lucene-cuVS connector and Nvidia’s cuVS library - A library that provides implementations of several algorithms for approximate nearest neighbor and clustering on GPU.
This talk will have three main sections:
[1] Apache Solr, its introduction, current vector search implementation, and enabling accelerated vector search using Lucene-cuVS connector and its comparison.
[2] Lucene-cuVS and its architecture, challenges, and benchmarking. How Lucene-cuVS enables accelerated vector search in large workloads.
[3] Introduction to Nvidia’s cuVS library, its history, ANN search algorithm types, their implementations and comparison, the novel graph-based CAGRA algorithm, and the cuVS future roadmap.
Speakers:
[1] Corey J. Nolet - Senior Data Scientist & Software Architect, Nvidia
[2] Ishan Chattopadhyaya - Apache Lucene & Apache Solr Committer, SearchScale
[3] Vivek Narang - Senior Software Engineer, SearchScale
Corey Nolet
Principal Engineer, Nvidia, Inc.
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