Contributor Index
Torsten Hoefler

Biography
Torsten Hoefler directs the Scalable Parallel Computing Laboratory (SPCL) at D-INFK ETH Zurich. He received his PhD degree in 2007 at Indiana University and started his first professor appointment in 2011 at UIUC.
Torsten has served as the lead for performance modeling and analysis in the US NSF Blue Waters project at NCSA/UIUC. Since 2013, he is professor of computer science at ETH Zurich and has held visiting positions at Argonne National Laboratories, Sandia National Laboratories, and Microsoft Research Redmond (Station Q).
Dr. Hoefler's research aims at understanding the performance of parallel computing systems ranging from parallel computer architecture through parallel programming to parallel algorithms. He is also active in the application areas of Weather and Climate as well as Machine Learning focusing on Distributed Deep Learning. In those areas, he has coordinated tens of funded projects and an ERC Starting Grant on Data-Centric Parallel Programming.
Torsten has served as the lead for performance modeling and analysis in the US NSF Blue Waters project at NCSA/UIUC. Since 2013, he is professor of computer science at ETH Zurich and has held visiting positions at Argonne National Laboratories, Sandia National Laboratories, and Microsoft Research Redmond (Station Q).
Dr. Hoefler's research aims at understanding the performance of parallel computing systems ranging from parallel computer architecture through parallel programming to parallel algorithms. He is also active in the application areas of Weather and Climate as well as Machine Learning focusing on Distributed Deep Learning. In those areas, he has coordinated tens of funded projects and an ERC Starting Grant on Data-Centric Parallel Programming.
Presentations
Focus Session

Communication Optimization
Extreme-Scale Computing
Heterogeneous Systems
Performance Analysis and Optimization
Programming Models & Languages
Machine Learning Day

AI/Machine Learning/Deep Learning
MPI
Tutorial

Extreme-Scale Computing
MPI
Networks
Programming Models & Languages
System Software & Runtime Systems
Birds of a Feather


AI/Machine Learning/Deep Learning
Communication Optimization
Parallel Applications
Performance Analysis and Optimization
Performance Tools
Birds of a Feather


AI/Machine Learning/Deep Learning
Big Data Analytics
Computer Architecture
Exascale Systems
Graph Algorithms