M.Sc. Alexis Engelke

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Technische Universität München

Informatik 10 - Lehrstuhl für Rechnerarchitektur & Parallele Systeme (Prof. Schulz)


Boltzmannstr. 3
85748 Garching b. München


Alexis Engelke is a PhD candidate at the Chair of Computer Architecture and Parallel Systems at Technical University of Munich (TUM), where he also earned his Master of Science in Computer Science in 2017. He conducts research in the area of runtime code generation, focusing on dynamic binary rewriting and optimization. His other research interests include parallel performance optimization, reverse engineering, software security, and low-level aspects of modern processor architectures in general.

Research Interests

  • Binary rewriting and instrumentation
  • Dynamic code generation and optimization
  • New/modern processor architectures
  • Programmability of non-linear architectures
  • Reverse engineering, software security, hacking
  • Low-level aspects of modern processor architectures
  • ... and more.

Ongoing Projects

  • Instrew: A high performance dynamic binary instrumentation framework based on LLVM
  • Rellume: Lifting x86-64 machine code to performant LLVM-IR
  • BinOpt: A library for self-guided binary specialization at runtime with support for multiple rewriters
    • DBLL: A binary optimizer and specializer using LLVM-IR for transformations (lives in BinOpt repository)
    • Drob: A binary optimizer using a low-level IR to support whole function optimizations
    • DBrew: A tracing binary rewriter and optimizer; DBrew-LLVM additionally uses LLVM as post-optimizer
  • Fadec: A very fast and small decoder for x86 and x86-64
  • HimMUC (Live Status): An ARM cluster for teaching and research
  • ... and more interesting things — get in contact to find it out.
Projects no longer under active development:
  • Mandel-QPU: Mandelbrot computation for the Raspberry Pi GPU



  • Alexis Engelke and Martin Schulz. Instrew: Leveraging LLVM for High Performance Dynamic Binary Instrumentation. VEE20, March 2020. Paper Slides
  • Poster: Alexis Engelke, David Hildenbrand, Martin Schulz. Optimizing Performance at Runtime Using Binary Rewriting. SC19, November 2019. Poster Abstract
  • Alexis Engelke. Reconstructing Program Semantics from Go Binaries. Master's Thesis. Department of Informatics, Technical University of Munich. September 2017. Thesis
  • Alexis Engelke and Josef Weidendorfer. Using LLVM for Optimized Lightweight Binary Re-Writing at Runtime. In Proceedings of the 22nd int. Workshop on High-Level Parallel Programming Models and Supportive Environments (HIPS 2017). Orlando, US, 2017. Paper