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Rutgers University, Computer Science Department Colloquium
3/7/2017 02:00 pm
CoRE Building- Room 701

Matrix Factorization on GPU: A Tale of Two Algorithms

Wei Tan, IBM T. J. Watson Research Center

Faculty Host: Manish Parashar

Abstract

Attendees will be introduced to cuMF, a CUDA-based matrix factorization library that accelerates both ALS and SGD to solve very large-scale MF. Please join us to hear about lessons learned in accelerating compute- and memory-intensive kernels on GPUs.

Bio

Wei Tan is a Research Staff Member at IBM T. J. Watson Research Center. His research interest includes big data, distributed systems, NoSQL and services computing. Currently he works on accelerating machine learning algorithms using scale-up (e.g., GPU) and scale-out (e.g., Spark) approaches. His work has been incorporated into IBM patent portfolio and software products such as Spark, BigInsights and Cognos.