High-Performance Machine Learning, Deep Learning and Data Science
Event Type
Tutorial
In-Person Only
Machine Learning and Artificial Intelligence
TUT
TimeMonday, 15 November 20211pm - 5pm CST
Location264
DescriptionRecent advances in machine and deep learning (ML/DL) have led to many exciting challenges and opportunities. This tutorial provides an overview of recent trends in ML/DL and the role of cutting-edge hardware architectures and interconnects in moving the field forward. We will also present an overview of different DNN architectures and ML/DL frameworks with a special focus on parallelization strategies for model training. We highlight new challenges and opportunities for communication runtimes to exploit high-performance CPU/GPU architectures to efficiently support large-scale distributed training. The tutorial covers training traditional ML models including K-Means, linear regression, nearest neighbors, using the cuML framework accelerated using MVAPICH2-GDR. Also, the tutorial presents accelerating GPU-based data science applications using MPI4Dask, which is an MPI-based backend for Dask. Throughout the tutorial, we include hands-on exercises to enable attendees to gain first-hand experience running distributed ML/DL training and Dask on a modern GPU cluster.
Promotional Video
