World's Best Scientists 2026 revealed!
Torsten Hoefler

Torsten Hoefler

Award Badge
Computer Science
Switzerland
2025

D-Index & Metrics

Computer Science

D-Index
67
Citations
15170
World Ranking
2222
National Ranking
50

Research.com Recognitions

  • 2025 - Research.com Computer Science in Switzerland Leader Award
  • 2022 - Research.com Computer Science in Switzerland Leader Award
  • 2020 - ACM Senior Member
  • 2019 - ACM Gordon Bell Prize For A Data-Centric Approach to Extreme-Scale Ab initio Dissipative Quantum Transport Simulations

Overview

Torsten Hoefler is affiliated with ETH Zurich in Switzerland and specializes in the field of Computer Science. Their research encompasses a range of subfields, including Computer Networks and Communications, Artificial Intelligence, Hardware and Architecture, Information Systems, and Computer Vision and Pattern Recognition.

The scientist's main topics of work involve Parallel Computing and Optimization Techniques, Cloud Computing and Resource Management, Interconnection Networks and Systems, Advanced Data Storage Technologies, Distributed and Parallel Computing Systems, Graph Theory and Algorithms, and Software-Defined Networks and 5G.

Hoefler has contributed extensively to several publication venues, with the most frequent being arXiv (Cornell University), Zenodo (CERN European Organization for Nuclear Research), Repository for Publications and Research Data (ETH Zurich), IEEE Transactions on Parallel and Distributed Systems, and IEEE Transactions on Computers.

Recent significant papers include:

  • Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks, 2021, arXiv (Cornell University)
  • Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks, 2021, Repository for Publications and Research Data (ETH Zurich)
  • Graph of Thoughts: Solving Elaborate Problems with Large Language Models, 2024, Proceedings of the AAAI Conference on Artificial Intelligence
  • The digital revolution of Earth-system science, 2021, Nature Computational Science
  • GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers, 2022, arXiv (Cornell University)

Frequent co-authors collaborating with Hoefler include Maciej Besta, Tal Ben-Nun, Salvatore Di Girolamo, Marcin Copik, and Daniele De Sensi.

Torsten Hoefler's honors include the ACM Senior Member designation received in 2020 and the ACM Gordon Bell Prize awarded in 2019 for work titled "A Data-Centric Approach to Extreme-Scale Ab initio Dissipative Quantum Transport Simulations."

Best Publications

  • Demystifying Parallel and Distributed Deep Learning: An In-depth Concurrency Analysis

    Tal Ben-Nun;Torsten Hoefler

  • The Convergence of Sparsified Gradient Methods

    Dan Alistarh;Torsten Hoefler;Mikael Johansson;Nikola Konstantinov

  • Slim fly: a cost effective low-diameter network topology

    Maciej Besta;Torsten Hoefler

  • Characterizing the Influence of System Noise on Large-Scale Applications by Simulation

    Torsten Hoefler;Timo Schneider;Andrew Lumsdaine

  • Generic topology mapping strategies for large-scale parallel architectures

    Torsten Hoefler;Marc Snir

  • The PERCS High-Performance Interconnect

    Baba Arimilli;Ravi Arimilli;Vicente Chung;Scott Clark

  • Scientific benchmarking of parallel computing systems: twelve ways to tell the masses when reporting performance results

    Torsten Hoefler;Roberto Belli

  • GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

    Unknown

  • Implementation and performance analysis of non-blocking collective operations for MPI

    Torsten Hoefler;Andrew Lumsdaine;Wolfgang Rehm

  • LogGOPSim: simulating large-scale applications in the LogGOPS model

    Torsten Hoefler;Timo Schneider;Andrew Lumsdaine

  • The digital revolution of Earth-system science

    Peter Bauer;Peter D. Dueben;Torsten Hoefler;Tiago Quintino

  • Enabling highly scalable remote memory access programming with MPI-3 one sided

    Robert Gerstenberger;Maciej Besta;Torsten Hoefler

  • Using automated performance modeling to find scalability bugs in complex codes

    Alexandru Calotoiu;Torsten Hoefler;Marius Poke;Felix Wolf

  • Deep learning for post-processing ensemble weather forecasts

    Peter Grönquist;Chengyuan Yao;Tal Ben-Nun;Nikoli Dryden

  • Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0

    Oliver Fuhrer;Tarun Chadha;Torsten Hoefler;Grzegorz Kwasniewski

  • DARE: High-Performance State Machine Replication on RDMA Networks

    Marius Poke;Torsten Hoefler

  • To Push or To Pull: On Reducing Communication and Synchronization in Graph Computations

    Maciej Besta;Michał Podstawski;Linus Groner;Edgar Solomonik

  • Using Advanced MPI: Modern Features of the Message-Passing Interface

    William Gropp;Torsten Hoefler;Rajeev Thakur;Ewing Lusk

  • Augment Your Batch: Improving Generalization Through Instance Repetition

    Elad Hoffer;Tal Ben-Nun;Itay Hubara;Niv Giladi

  • Multistage switches are not crossbars: Effects of static routing in high-performance networks

    T. Hoefler;T. Schneider;A. Lumsdaine

  • Message progression in parallel computing - to thread or not to thread?

    T. Hoefler;A. Lumsdaine

  • SeBS: a serverless benchmark suite for function-as-a-service computing

    Marcin Copik;Grzegorz Kwasniewski;Maciej Besta;Michal Podstawski

Frequent Co-Authors

Andrew Lumsdaine
Andrew Lumsdaine Pacific Northwest National Laboratory
William Gropp
William Gropp University of Illinois at Urbana-Champaign
Rajeev Thakur
Rajeev Thakur Argonne National Laboratory
Felix Wolf
Felix Wolf Technical University of Darmstadt
Ewing Lusk
Ewing Lusk Argonne National Laboratory
Gustavo Alonso
Gustavo Alonso ETH Zurich
Luca Benini
Luca Benini ETH Zurich
Marc Snir
Marc Snir University of Illinois at Urbana-Champaign
Adrian Perrig
Adrian Perrig ETH Zurich

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