Prof. Dr. - Ing. Morris Riedel

2019-06-17 Parallel and Scalable Machine Learning Co Design of a Modular Supercomputing Architecture Morris Riedel 0

Talks

Parallel and Scalable Machine Learning Co-Design of a Modular Supercomputing Architecture

Parallel and Scalable Machine Learning Co-Design of a Modular Supercomputing Architecture Invited Talk School for Simulation and Data Sciences (SSD), Aachen Institute for Advanced Study in Computational Engineering Science (AICES), RWTH Aachen University Aachen, Germany 2019-06-17 [ EVENT ] [ Slides ~17.0 MB (pdf) ] Invited talk @RWTH about parallel & scalable #MachineLearning #DeepLearning in #HPC & #ON4OFF / #SMITH projects using @helmholtz_ai & modular supercomputing architecture by @fz_juelich @fzj_jsc @Haskoli_Islands @uisens @uni_iceland @DEEPprojects – slides: https://t.co/xCDitaxS71 pic.twitter.com/47fENOXCDY — Morris Riedel (@MorrisRiedel) June 20, 2019

HPDBSCAN Highly Parallel DBSCAN 0

Publications

HPDBSCAN – Highly Parallel DBSCAN

HPDBSCAN – Highly Parallel DBSCAN Goetz, M., Bodenstein, C., Riedel, M.: HPDBSCAN – Highly Parallel DBSCAN, in conference proceedings of ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2015), Machine Learning in HPC Environments (MLHPC 2015) Workshop, November 15-20, 2015, Austin, Texas, USA [ EVENT ] [ DOI ] [ JUSER ] [ RESEARCHGATE ] Abstract: Clustering algorithms in the field of data-mining are used to aggregate similar objects into common groups. One of the best-known of these algorithms is called DBSCAN. Its distinct design enables the search for an apriori unknown number of arbitrarily shaped...