SSD - Riedel Seminar
Prof. Dr. Morris Riedel - Parallel and Scalable Machine Learning Co-Design of a Modular Supercomputing Architecture
The fast training of machine learning models and innovative deep learning networks from increasingly growing large quantities of scientific and engineering datasets requires high-performance computing (HPC). Modern supercomputing technologies such as those developed within the European DEEP-EST project provide innovative approaches w.r.t. processing, memory, and modular supercomputing usage during training, testing, and validation processes. This talk illustrates why and how parallel processing is a key enabler for a wide variety of machine and deep learning algorithms today. Examples include commercial, scientific and engineering applications that leverage parallel and scalable feature engineering, density-based spatial clustering of applications with noise (DBSCAN), and convolutional neural networks (CNNs). The talk concludes with a short overview of the new Helmholtz Artificial Intelligence Cooperation Unit (HAICU) and its local setup at Forschungszentrum Juelich.