SIAM PP22 Minisymposium: Advances in Performance Modeling of Parallel Code
Two-part minisymposium at SIAM Parallel Processing 2022 (full schedule available here) on February 25, 2022.
Performance modeling is an indispensable tool for the assessment, analysis, prediction, and optimization of parallel code in scientific computing and computational science. Modeling approaches can take a variety of forms, from purely analytic, first-principle models to curve fitting, machine learning, and AI-based solutions. The goals of modeling are just as diverse: Identification of bottlenecks or scaling problems, extrapolation, architectural exploration, and even the prediction of power dissipation and energy consumption can all be supported be modeling procedures. This minisymposium tries to provide an overview of the current state of the art in performance, or more generally, resource modeling of parallel code. The hardware focus will be very broad, from the node to the massively parallel level, including standard multicore systems, GPUs, and reconfigurable hardware. Contributions will cover fundamental research as well as tools development and case studies. After the minisymposium, the organizers plan to issue an open call for a journal special issue.
Agenda (speakers in italic; slides linked under the title, if available):
Part 1 (February 25, 11:10 am – 12:50 pm PST)
11:10-11:30 Computational Waves in Parallel Programs and Their Impact on Performance Modeling
Ayesha Afzal, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany; Georg Hager, Erlangen National High Performance Computing Center, Germany
11:35-11:55 The Price Performance of Performance Models
Alexandru Calotoiu, ETH Zurich, Switzerland; Alexander Geiss, Benedikt Naumann, Marcus Ritter, and Felix Wolf, Technische Universität Darmstadt, Germany
12:00-12:20 perf-taint: Extracting Clean Performance Models from Tainted Programs
Marcin Copik, ETH Zurich, Switzerland
12:25-12:45 Extra-P Meets Hatchet: Towards Modeling in Performance Analytics
Sergei Shudler, Lawrence Livermore National Laboratory, U.S.
Part 2 (February 25, 3:35 pm – 5:15 pm PST)
3:35-3:55 Performance Modeling of Graph Processing Workloads
Ana Lucia Varbanescu and Merijn Verstraaten, University of Amsterdam, Netherlands
4:00-4:20 Machine Learning–enabled Scalable Performance Prediction of Scientific Codes
Stephan Eidenbenz and Nandakishore Santhi, Los Alamos National Laboratory, U.S.
4:25-4:45 Automatic Application Performance Data Collection with Caliper and Adiak
David Boehme, Lawrence Livermore National Laboratory, U.S.