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Video: Scientific Benchmarking of Parallel Computing Systems

In this video, Torsten Hoefler from ETH Zurich presents: Scientific Benchmarking of Parallel Computing Systems.

Measuring and reporting performance of parallel computers constitutes the basis for scientific advancement of high-performance computing. Most scientific reports show performance improvements of new techniques and are thus obliged to ensure reproducibility or at least interpretability. Our investigation of a stratified sample of 120 papers across three top conferences in the field shows that the state of the practice is not sufficient. For example, it is often unclear if reported improvements are in the noise or observed by chance. In addition to distilling best practices from existing work, we propose statistically sound analysis and reporting techniques and simple guidelines for experimental design in parallel computing. We aim to improve the standards of reporting research results and initiate a discussion in the HPC field. A wide adoption of this minimal set of rules will lead to better reproducibility and interpretability of performance results and improve the scientific culture around HPC.

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 the University of Illinois at Urbana-Champaign. 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 simulations as well as Machine Learning with a focus on Distributed Deep Learning. In those areas, he has coordinated tens of funded projects and an ERC Starting Grant on Data-Centric Parallel Programming. He has been chair of the Hot Interconnects conference and technical program chair of the Supercomputing and ACM PASC conferences. He is associate editor of the IEEE Transactions of Parallel and Distributed Computing (TPDS) and the Parallel Computing Journal (PARCO) and a key member of the Message Passing Interface (MPI) Forum.

He has published more than 200 papers in peer-reviewed international conferences and journals and co-authored the latest versions of the MPI specification. He has received best paper awards at the ACM/IEEE Supercomputing Conference in 2010, 2013, and 2014 (SC10, SC13, SC14), EuroMPI 2013, IPDPS’15, ACM HPDC’15 and HPDC’16, ACM OOPSLA’16, and other conferences. Torsten received ETH Zurich’s Latsis Prize in 2015, the SIAM SIAG/Supercomputing Junior Scientist Prize in 2012, the IEEE TCSC Young Achievers in Scalable Computing Award in 2013, the Young Alumni Award 2014 from Indiana University, and the best student award 2005 of the Chemnitz University of Technology. Torsten was elected into the first steering committee of ACM’s SIGHPC in 2013 and he was re-elected in 2016. His Erdős numberis two (via Amnon Barak) and he is an academic descendant of Hermann von Helmholtz.

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