Markus Höhnerbach

According to our database, Markus Höhnerbach authored at least 14 papers between 2014 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

Generalized Neighborhood Attention: Multi-dimensional Sparse Attention at the Speed of Light.

Ali Hassani, Fengzhe Zhou, Aditya Kane, Jiannan Huang, Chieh-Yun Chen, Min Shi, Steven Walton, Markus Hoehnerbach, Vijay Thakkar, Michael Isaev, Qinsheng Zhang, Bing Xu, Haicheng Wu, Wen-Mei Hwu, Ming-Yu Liu, Humphrey Shi

CoRR, April, 2025

Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE.

Jesun Sahariar Firoz, Franco Pellegrini, Mario Geiger, Darren Hsu, Jenna A. Bilbrey, Han-Yi Chou, Maximilian Stadler, Markus Höhnerbach, Tingyu Wang, Dejun Lin, Emine Küçükbenli, Henry W. Sprueill, Ilyes Batatia, Sotiris S. Xantheas, MalSoon Lee, Christopher J. Mundy, Gábor Csányi, Justin S. Smith, Ponnuswamy Sadayappan, Sutanay Choudhury

Proceedings of the 34th International Symposium on High-Performance Parallel and Distributed Computing, 2025

2023

cuQuantum SDK: A High-Performance Library for Accelerating Quantum Science.

Harun Bayraktar, Ali Charara, David Clark, Saul Cohen, Timothy B. Costa, Yao-Lung L. Fang, Yang Gao, Jack Guan, John A. Gunnels, Azzam Haidar, Andreas Hehn, Markus Höhnerbach, Matthew Jones, Tom Lubowe, Dmitry I. Lyakh, Shinya Morino, Paul Springer, Sam Stanwyck, Igor Terentyev, Satya Varadhan, Jonathan Wong, Takuma Yamaguchi

Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2023

2020

A framework for the vectorization of molecular dynamics kernels.

Markus Höhnerbach

PhD thesis, 2020

2019

Accelerating AIREBO: Navigating the Journey from Legacy to High-Performance Code.

Markus Höhnerbach, Paolo Bientinesi

J. Comput. Chem., 2019

2018

Optimizing AIREBO: Navigating the Journey from Complex Legacy Code to High Performance.

Markus Höhnerbach, Paolo Bientinesi

CoRR, 2018

Accelerating the computation of FLAPW methods on heterogeneous architectures.

Davor Davidovic, Diego Fabregat-Traver, Markus Höhnerbach, Edoardo Di Napoli

Concurr. Comput. Pract. Exp., 2018

2017

The Tersoff many-body potential: Sustainable performance through vectorization.

Markus Höhnerbach, Ahmed E. Ismail, Paolo Bientinesi

CoRR, 2017

LAMMPS' PPPM Long-Range Solver for the Second Generation Xeon Phi.

William McDoniel, Markus Höhnerbach, Rodrigo Canales, Ahmed E. Ismail, Paolo Bientinesi

Proceedings of the High Performance Computing - 32nd International Conference, 2017

2016

Dynamic SIMD Vector Lane Scheduling.

Olaf Krzikalla, Florian Wende, Markus Höhnerbach

Proceedings of the High Performance Computing, 2016

The vectorization of the tersoff multi-body potential: an exercise in performance portability.

Markus Höhnerbach, Ahmed E. Ismail, Paolo Bientinesi

Proceedings of the International Conference for High Performance Computing, 2016

Hybrid CPU-GPU Generation of the Hamiltonian and Overlap Matrices in FLAPW Methods.

Diego Fabregat-Traver, Davor Davidovic, Markus Höhnerbach, Edoardo Di Napoli

Proceedings of the High-Performance Scientific Computing, 2016

2014

Implementation of a Transformation System for Relational Probabilistic Knowledge Bases Simplifying the Maximum Entropy Model Computation.

Christoph Beierle, Markus Höhnerbach, Marcus Marto

Proceedings of the Twenty-Seventh International Florida Artificial Intelligence Research Society Conference, 2014

Generation of Parametrically Uniform Knowledge Bases in a Relational Probabilistic Logic with Maximum Entropy Semantics.

Christoph Beierle, Markus Höhnerbach, Marcus Marto

Proceedings of the First Workshop on Logics for Reasoning about Preferences, 2014