M.Sc. Amir Raoofy

Researcher & Ph.D. candidate
Technische Universität München, Informatik 10
Lehrstuhl für Rechnerarchitektur & Parallele Systeme (Prof. Schulz)

Room 01.04.039
Boltzmannstr. 3
85748 Garching b. München

Email: amir.raoofy@tum.de
Tel.: +49 (89) 289 - 17683

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


  • 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


  • SS 20: Seminar: Cloud Computing: Internet of Things Technologies
  • SS 19: Seminar: Geschichte der Rechnerarchitektur
  • WS 18/19: Praktikum Advanced Topics in Computer Architecture and Parallel Systems
  • SS 18: Lecture Parallel Programming (Central Tutorial)


  • Amir Raoofy, Roman Karlstetter, Dai Yang, Carsten Trinitis, Martin Schulz: Time Series Mining at Petascale Performance: ISC High Performance 2020 (Winner of the Hans Meuer Best Paper Award).

  • Roman Karlstetter, Robert Widhopf-Fenk, Jakob Hermann, Driek Rouwenhorst, Amir Raoofy, Carsten Trinitis, Martin Schulz: Turning dynamic sensor measurements from gas turbines into insights: a big data approach. ASME Turbo-Expo conference 2019.
  • 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)