SIAM Parallel Processing 2022 Minisymposium on “Advances in Performance Modeling of Parallel Code”
We are happy to announce that our proposal for a SIAM PP22 Minisymposium was accepted. The SIAM Parallel Processing conference series is unique in its emphasis on the intersection between high performance scientific computing and scalable algorithms, architectures, and software. The conference provides a forum for communication among the applied mathematics, computer science, and computational science and engineering communities. SIAM PP22 takes place in a hybrid format from February 23-26, 2022, in Seattle, WA. The full program can be viewed at: https://meetings.siam.org/program.cfm?CONFCODE=PP22.
Our minisymposium, to take place on February 25, 2022, is titled “Advances in Performance Modeling of Parallel Code.” It is organized by Georg Hager (NHR@FAU) and Alexandru Calotoiu (ETH Zurich). Here’s the abstract:
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.
An impressive lineup of international speakers have been brought together for this two-part MS:
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.
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.