lo2s - Multi-core System and Application Performance Analysis for Linux. - Thomas Ilsche; Robert Schöne; Mario Bielert; Andreas Gocht; Daniel Hackenberg (CLUSTER, 2017)

READEX: Linking two ends of the computing continuum to improve energy-efficiency in dynamic applications. - Per Gunnar Kjeldsberg; Andreas Gocht; Michael Gerndt; Lubomir Riha; Joseph Schuchart; Umbreen Sabir Mian (DATE, 2017)

The READEX formalism for automatic tuning for energy efficiency. - Joseph Schuchart; Michael Gerndt; Per Gunnar Kjeldsberg; Michael Lysaght; David Horák; Lubomír Ríha; Andreas Gocht; Mohammed Sourouri; Madhura Kumaraswamy; Anamika Chowdhury; Magnus Jahre; Kai Diethelm; et al. (Computing, 2017)

System Monitoring with lo2s: Power and Runtime Impact of C-State Transitions. - Thomas Ilsche; Robert Schöne; Philipp Joram; Mario Bielert; Andreas Gocht (IPDPS Workshops, 2018)

A New Approach for Automated Feature Selection. - Andreas Gocht; Christoph Lehmann; Robert Schöne (GI-Jahrestagung, 2019)

Q-Learning Inspired Self-Tuning for Energy Efficiency in HPC. - Andreas Gocht; Robert Schöne; Mario Bielert (HPCS, 2019)

Energy Efficiency Features of the Intel Skylake-SP Processor and Their Impact on Performance. - Robert Schöne; Thomas Ilsche; Mario Bielert; Andreas Gocht; Daniel Hackenberg (HPCS, 2019)

Using performance analysis tools for parallel-in-time integrators - Does my time-parallel code do what I think it does? - Robert Speck; Michael Knobloch; Andreas Gocht; Sebastian Lührs (CoRR, 2019)

Advanced Python Performance Monitoring with Score-P. - Andreas Gocht; Robert Schöne; Jan Frenzel (CoRR, 2020)

Controlling the Runtime Overhead of Python Monitoring with Selective Instrumentation. - Andreas Gocht; Alexander Grund; Robert Schöne (ProTools@SC, 2021)

Ein Framework zur Optimierung der Energieeffizienz von HPC-Anwendungen auf der Basis von Machine-Learning-Methoden. - Andreas Gocht-Zech (2022)

JUmPER: Performance Data Monitoring, Instrumentation and Visualization for Jupyter Notebooks. - Elias Werner; Anton Rygin; Andreas Gocht-Zech; Sebastian Döbel; Matthias Lieber (SC Workshops, 2024)

Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer. - Gesa Mittmann; Sara Laiouar-Pedari; Hendrik Alexander Mehrtens; Sarah Haggenmüller; Tabea-Clara Bucher; Tirtha Chanda; Nadine T. Gaisa; Mathias Wagner; Gilbert Georg Klamminger; Tilman T. Rau; Christina Neppl; Eva Maria Compérat; et al. (CoRR, 2024)