The hardware and software that led to the revolution of deep learning was built during the era of computer vision.  Differences in architecture and data between that domain and recommenders made the HW/SW stack a poor fit for deep learning based recommender systems, and the experience of many who explored recommendation on the GPU early on, myself included, was bad.  In this talk we’ll explore changes in GPU hardware within the last generation that make it much better suited to the recommendation problem, along with improvements on the software side that take advantage of optimizations only possible in the recommendation domain.  A new era of faster ETL, Training and Inference is coming to the RecSys space and this talk will walk through some of the patterns of optimization that guide the tools we’re building to make recommenders faster and easier to use on the GPU.

Speaker Bio

Even Oldridge is a Sr. Manager at NVIDIA leading the effort to develop the open source libraries of Merlin which provide fast, easy to use and deploy, scalable recommender systems on the GPU.  He has a PhD in Computer Vision and a Masters in Programmable Hardware from the University of British Columbia.  He’s worked in the RecSys space for the past decade and has developed systems for recommending dates and houses, among other things.  He’s an industry co-chair for ACM RecSys Conference 2021, and he’ll talk your ear off about embeddings and deep learning based recommenders if you let him.