Georg Hager's Blog

Random thoughts on High Performance Computing

Content

Contact info

Dr. rer. nat. habil. Georg Hager
Erlangen National High Performance Computing Center (NHR@FAU)
Friedrich-Alexander Universität Erlangen-Nürnberg
Martensstr. 1
91058 Erlangen
Germany
Phone: +49 9131 85 28973
georg.hager@fau.de

You can download my GPG public key from here: Georg Hager georg.hager@fau.de-(0xCFE866752A812572)-public

(fingerprint 6BE4 61E5 AB6B 509A F0C9 C759 CFE8 6675 2A81 2572, Georg Hager (RRZE) <georg.hager@fau.de>)

Short bio:

Georg Hager holds a PhD and a Habilitation degree in Computational Physics from the University of Greifswald. He heads the Training and Support Division at Erlangen National High Performance Computing Center (NHR@FAU) and is an associate lecturer at the Institute of Physics of the University of Greifswald. Recent research includes architecture-specific optimization strategies for current microprocessors, performance engineering of scientific codes on chip and system levels, and structure formation in large-scale parallel codes. He served as a PI in the ESSEX (Equipping Sparse Solver for Exascale) project within the SPPEXA DFG priority programme. Georg Hager has authored and co-authored over 100 peer-reviewed publications and was instrumental in developing and refining the Execution-Cache-Memory (ECM) performance model and energy consumption models for multicore processors. In 2018, he won the “ISC Gauss Award” (together with Johannes Hofmann and Dietmar Fey) for a paper on accurate performance and power modeling. He received the “2011 Informatics Europe Curriculum Best Practices Award” (together with Jan Treibig and Gerhard Wellein) for outstanding contributions to teaching in computer science. His textbook “Introduction to High Performance Computing for Scientists and Engineers” is recommended or required reading in many HPC-related lectures and courses worldwide. Together with colleagues from FAU, HLRS Stuttgart, and TU Wien he develops and conducts successful international tutorials on node-level performance engineering and hybrid programming.