M.Sc. Amir Raoofy

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

Room I.1.030
Boltzmannstr. 1
85748 Garching b. München

Email: amir.raoofy@tum.de
Tel.: +49 (89) 35831 - 8871

Research interests

  • Programming and Usage Models
  • System Software and Programming
  • 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 accelerators and multi-core HPC systems
  • Porting applications to HPC systems


  • Dynamical Exascale Entry Platform - Software for Exascale Architectures (DEEP-SEA) ( www.deep-projects.eu ): Research project sponsored by European Commission and EuroHPC Programmes.
  • Commercial off-the-shelf Inference Processor ML Benchmark (MLAB): Research project in collaboration with Airbus Defence and Space GmbH, TUM chair of Big Geospatial Data Management, and Orora Technologies GmbH. 
  • From the Edge to the Cloud and Back: Scalable and Adaptive Sensor Data Processing (SensE): Research project funded by Bayerische Forschungsstiftung in cooperation with IfTA Ingenieurbüro für Thermoakustik GmbH
  • 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



  • Amir Raoofy, Roman Karlstetter, Martin Schreiber, Carsten Trinitis, Martin Schulz: Overcoming Weak Scaling Challenges in Tree-based Nearest Neighbor Time Series Mining, ISC 2023.

  • Yi Ju, Amir Raoofy, Dai Yang, Erwin Laure, Martin Schulz: Exploiting Reduced Precision for GPU-based Time Series Mining, IPDPS 2022.

  • Max Ghiglione, Amir Raoofy ,Gabriel Dax, Gianluca Furano, Richard Wiest, Carsten Trinitis, Martin Werner, Martin Schulz, Martin Langer: Machine Learning Application Benchmark for In-Orbit On-Board Data Processing, OBDP 2021.

  • Amir Raoofy, Gabriel Dax, Max Ghiglione, Martin Langer, Carsten Trinitis, Martin Werner, and Martin Schulz: Benchmarking Machine Learning Inference in FPGA-based Accelerated Space Applications, MLBench 2021.

  • Roman Karlstetter, Amir Raoofy, Martin Radev, Carsten Trinitis, Jakob Hermann, and Martin Schulz: Living on the Edge: Efficient Handling of Large Scale Sensor Data, CCGrid 2021.

  • 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.