Tobias Grosser
Reader (Associate Professor) in
Compilers and Runtime Systems
My interests are compilation, programming language design, and effective performance programming. However, I define these areas broadly. As a result, I ask questions such as:
- How can compilers contribute to understanding climate change?
- Can machine learning help us to automate compiler design and performance programming?
- Can compilers revolutionize how hardware (ASICs, CPUs, ...) is designed?
- Can formal verification increase confidence in self-driving cars?
- How can performance programming become a smooth and interactive experience for developers?
- address problems important to society,
- advocate for diversity,
- work with the open-source community,
- show that well-being increases scientific performance.
- ETH Zurich (CH), Ambizione Fellow with Torsten Hoefler
- École Normale Supérieure Paris (FR), Google Ph.D. Fellowship with Albert Cohen
- University of Passau (DE), Diploma with Christian Lengauer
Are you considering a PhD? I really enjoy working with students! There is nothing more rewarding for me as a scientist than seeing a student develop and execute their very personal research vision. I am excited to provide you guidance along this path.
Explore my research vision, papers, and projects. If a topic catches your attention or you want to discuss your personal research idea, email me! I often have generic funding available that I can use on your very personal project and am always happy to guide you towards getting a PhD position.
I also regularly host postdoctoral researchers and interns. Just email me.Project-specific openings available!
- 1x PostDoc, ?x PhD: Efficient Design of Multi-Domain Languages using Python and MLIR
One PostDoc positon and a number of EPCC PhD Scholarships. We build a new ecosystem for designing DSLs where we mix Python and MLIR. This enables production-quality DSLs for climate modeling, medical imaging, and more: xDSL.dev. - 1x PhD: Formal Verification at the Boundary of Soft and Hardware
PhD Scholarship, in collaboration with industry partners and the open-source community. - 1x PostDoc, 1x PhD: Rethinking Compiler Design
I have funding for projects to rethink the interaction of humans and compilers.- Can we interactively design compilers?
- Can we automate compiler design using program synthesis?
- Can we make formal formal verification part of the daily LLVM/MLIR development?
- 1x PostDoc, 1x PhD: Developer-guided compilation for Ultra-Low-Power and Security
These positions are funded via the Horizion Europe CONVOLVE project. Can we make domain-specific security and performance constraints a first-class concept in a compiler, such that developers can state these constraints and the compiler uses them when targetting domain-specific hardware?
Arjun Pitchanathan
PhD
Bea Healy
PhD
Mathieu Fehr
PhD
Siddharth Bhat
PhD
You
PhD
Kunwar Shaanjeet Singh Grover
Intern
Christian Ulmann
Collaborator
Michel Weber
Collaborator
Previous Members
- Yifan Xu, Intern
2021
Moved toUniversity of Edinburgh
BSc Student (continuing) - David Bourke, MSc
2021
Moved toVyatta NOS group
Performance Engineer - Anurudh Peduri, Intern
2020 - 2022
Moved toRuhr-Universität Bochum
PhD with Michael Walter - Christoph Mueller, Researcher
2020
Moved toMeteoSwiss
Scientific Software Developer - Jakub Lichman, M.Sc. Thesis
2020
Moved toGoogle
Internship with Albert Cohen and Oleksandr Zinenko - Arjun Pitchanathan, Intern (M.Sc.)
2019 - 2020
Moved toUniversity of Edinburgh
Ph.D. with me - Theodoros Theodoridis, Researcher
2019 - 2020
Moved toETH Zurich
Ph.D. student with Zhendong Su - Jean-Michel Gorius, Intern (M.Sc.)
2019
Moved toUniversity of Murcia
Internship with Alexandra Jimborean - Mathieu Fehr, Intern (M.Sc.)
2019
Moved toMIT CSAIL
Internship with Adam Chlipala - Lorenzo Chelini, PhD
2018 - 2020
Moved toIntel Zurich
Postdoctoral Researcher - Anastiasiia Ruzhanskaia, Intern (M.Sc.)
2018
Moved toETH Zurich
Ph.D. student with Timothy Roscoe - Tobias Gysi, PhD Student & Postdoctoral Researcher
2014 - 2020
Moved toGoogle Zurich
Research Engineer
Effective Performance Programming by Re-Connecting Developers and Compilers
My research uses strong theoretical reasoning to bring innovations to real-world compilation and programming language problems. My current objective is to rethink performance programming by re-connecting developers and compilers. Today, performance programming is no longer limited to the optimization of low-level code but often includes the use of domain-specific compilers, constraint programming libraries, complex performance models, and automatic (potentially learned) strategies to search for optimal code transformations.
My primary objectives are:- making compilation more modular, predictable, automatic, and trustworthy
- bringing open-source compiler innovation to an increasingly broad set of targets from GPUs over FPGAs to custom hardware, and
- breaking down the barriers between compilers and programmers by enabling their interaction via the programming language environment.
I dream of a future where performance programming is an intuitive play between the programmer and the compiler. A game that smoothly moves between manual as well as automatic techniques and works across software and hardware.
I am particularly interested in the following areas:- Compilers
- Static & dynamic analysis
- Abstract interpretation based program analysis
- Performance and cache models
- Test case generation
- Human-compiler interface
- Domain-specific compilation
- Deep learning
- Climate science
- High-performance computing
- Loop optimization & polyhedral compilation
- Vectorization
- Compilation for accelerators: GPU, FPGA
- Open-source hardware
- Compiler support for RISC-V
- Software/hardware co-design
- SMT and constraint solving
- Mixed integer linear programming
- SAT solving
- Parametric counting using Barvinok’s algorithm
- Automatic theorem proving for compiler verification and constraint solving
- Effective compilation from functional languages to imperative code
- Artificial intelligence for compilers and constraint solvers
2022
- ASPLOSUnderstanding and Exploiting Optimal Function Inlining ↞ New!
Theodoros Theodoridis, Tobias Grosser, Zhendong Su
- PLDIIRDL: An IR Definition Language for SSA Compilers ↞ New!
Mathieu Fehr, Jeff Niu, River Riddle, Mehdi Amini, Zhendong Su, Tobias Grosser
- ICSTMetamorphic Fuzzing of C++ Libraries ↞ New!
Andrei Lascu, Alastair Donaldson, Tobias Grosser, Torsten Hoefler
- CGOLambda the Ultimate SSA: Optimizing Functional Programs in SSA ↞ New!
Siddharth Bhat, Tobias Grosser
- CCQSSA: An SSA-Based IR for Quantum Computing ↞ New!
Anurudh Peduri, Siddharth Bhat, Tobias Grosser
- CCQRANE: Lifting QASM Programs to an Affine IR ↞ New!
Blake Gerard, Tobias Grosser, Martin Kong
2021
- OOPSLAFPL: Fast Presburger Arithmetic through Transprecision
Arjun Pitchanathan, Christian Ulmann, Michel Weber, Torsten Hoefler, Tobias Grosser
- TCADOCC: An Automated End-to-End Machine Learning Optimizing Compiler for Computing-In-Memory
Adam Siemieniuk, Lorenzo Chelini, Asif Ali Khan, Jeronimo Castrillon, Andi Drebes, Henk Corporaal, Tobias Grosser, Martin Kong
- CGOProgressive Raising in Multi-level IR
Lorenzo Chelini, Andi Drebes, Oleksandr Zinenko, Albert Cohen, Nicolas Vasilache, Tobias Grosser, Henk Corporaal
- SCOPESLoopOpt: Declarative Transformations Made Easy
Lorenzo Chelini, Martin Kong, Tobias Grosser, Henk Corporaal
- TACODomain-Specific Multi-Level IR Rewriting for GPU: The Open Earth Compiler for GPU-Accelerated Climate Simulation
Tobias Gysi, Christoph Müller, Oleksandr Zinenko, Stephan Herhut, Eddie Davis, Tobias Wicky, Oliver Fuhrer, Torsten Hoefler, Tobias Grosser
- TPDSWork-stealing prefix scan: Addressing load imbalance in large-scale image registration
Marcin Copik, Tobias Grosser, Torsten Hoefler, Paolo Bientinesi, Benjamin Berkels
- PPoPPExtracting Clean Performance Models from Tainted Programs
Marcin Copik, Alexandru Calotoiu, Tobias Grosser, Nicolas Wicki, Felix Wolf, Torsten Hoefler
2020
- OOPSLAFast Linear Programming through Transprecision Computing on Small and Sparse Data
Tobias Grosser, Theodoros Theodoridis, Maximilian Falkenstein, Arjun Pitchanathan, Michael Kruse, Manuel Rigger, Zhendong Su, Torsten Hoefler
- PACTAutomatic Generation of Multi-Objective Polyhedral Compiler Transformations
Lorenzo Chelini, Tobias Gysi, Tobias Grosser, Martin Kong, Henk Corporaal
- TCADPolyhedral Compilation for Racetrack Memories
Asif Ali Khan, Hauke Mewes, Tobias Grosser, Torsten Hoefler, Jeronimo Castrillon
- PLDILLHD: A Multi-Level Intermediate Representation for Hardware Description Languages
Fabian Schueki, Andreas Kurth, Tobias Grosser, Luca Benini
- EuroLLVMMultiLevel Tactics: Lifting loops in MLIR
Lorenzo Chelini, Andi Drebes, Oleksandr Zinenko, Albert Cohen, Henk Corporaal, Tobias Grosser, Nicolas Vasilache
- CCMixed-data-model heterogeneous compilation and OpenMP offloading
Andreas Kurth, Koen Wolters, Björn Forsberg, Alessandro Capotondi, Andrea Marongiu, Tobias Grosser, Luca Benini
- SPMACompiling Neural Networks for a Computational Memory Accelerator
Kornilios Kourtis, Martino Dazzi, Nikolas Ioannou, Tobias Grosser, Abu Sebastian, Evangelos Eleftheriou
- IMPACTTC-CIM: Empowering Tensor Comprehensions for Computing-In-Memory
Andi Drebes, Lorenzo Chelini, Oleksandr Zinenko, Albert Cohen, Henk Corporaal, Tobias Grosser, Kanishkan Vadivel, Nicolas Vasilache
Google Scholar
School of Informatics
10 Crichton Street
Room: 1.04
Edinburgh EH8 9AB, United Kingdom
first.last at ed.ac.uk