Foto von Amir Raoofy

M.Sc. Amir Raoofy

Research interests

  • Analysis of large datasets on HPC systems
  • Machine Learning and Data Mining on HPC systems
  • Time-series analysis on industrial datasets using HPC systems
  • Parallelization and code optimization on multi-core and HPC systems
  • Porting applications to HPC systems

Projects

  • Gas Turbine Optimization using Big Data and Machine Learning (TurbO): Research project funded by Bayerische Forschungsstiftung in cooperation with IfTA Ingenieurbüro für Thermoakustik GmbH
  • Porting Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics (LULESH) to LAIK (A Library for Automatic Data Migration in Parallel Applications) Code

Teaching

  • WS 18/19: Praktikum Advanced Topics in Computer Architecture and Parallel Systems
  • SS 18: Lecture Parallel Programming (Central Tutorial)

Publications

  • Amir Raoofy, Dai Yang, Josef Weidendorfer, Carsten Trinitis and Martin Schulz: Enabling Malleability for Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics using LAIK. PARS Workshop 2019
  • Dai Yang, Moritz Dötterl, Sebastian Rückerl and Amir Raoofy: Hardening the Linux Kernel agains Soft Errors. Poster for The 13th International School on the Effects of Radiation on Embedded Systems for Space
  • Arash Bakhtiari, Dhairya Malhotra, Amir Raoofy, Miriam Mehl, Hans-Joachim Bungartz, George Biros. A parallel arbitrary-order accurate AMR algorithm for the scalar advection-diffusion equation. SC '16. (URL)