GPU Computing in R by Jared Lander Visit https://rstats.ai/nyr/ to learn more. Abstract: Parallel computing has become easier and easier in R over the years thanks to packages like parallel and future. But the CPU measures cores in the single or double digits. With GPUs we can access thousands of cores, significantly speeding up our work. Taking advantage of the GPU for machine learning has never been easier thanks to torch, xgboost, catboost and Stan. We look at how to fit those models on the GPU and how to use some lower level code to perform custom operations with the GPU. Bio: Jared P. Lander is Chief Data Scientist of Lander Analytics, the Organizer of the New York Open Statistical Programming Meetup and the New York R Conference and an Adjunct Professor of Statistics at Columbia University. With a masters from Columbia University in statistics and a bachelors from Muhlenberg College in mathematics, he has experience in both academic research and industry. Jared oversees the long-term direction of the company and acts as Lead Data Scientist, researching the best strategy, models and algorithms for modern data needs. This is in addition to his client-facing consulting and training. He specializes in data management, multilevel models, machine learning, generalized linear models, data management, visualization and statistical computing. He is the author of R for Everyone, a book about R Programming geared toward Data Scientists and Non-Statisticians alike. The book is available from Amazon, Barnes & Noble and InformIT. The material is drawn from the classes he teaches at Columbia and is incorporated into his corporate training. Very active in the data community, Jared is a frequent speaker at conferences, universities and meetups around the world. He is a member of the Strata New York selection committee. His writings on statistics can be found at jaredlander.com. Twitter: https://twitter.com/jaredlander Presented at the 2021 New York R Conference (September 9, 2021)