Confidentiality and Authenticity for Distributed Version Control Systems - A Mercurial Extension. - Michael Lass; Dominik Leibenger; Christoph Sorge (LCN, 2016)

Using Approximate Computing for the Calculation of Inverse Matrix p-th Roots. - Michael Lass; Thomas D. Kühne; Christian Plessl (CoRR, 2017)

A Massively Parallel Algorithm for the Approximate Calculation of Inverse p-th Roots of Large Sparse Matrices. - Michael Lass; Stephan Mohr; Thomas D. Kühne; Christian Plessl (CoRR, 2017)

Efficient Branch and Bound on FPGAs Using Work Stealing and Instance-Specific Designs. - Heinrich Riebler; Michael Lass; Robert Mittendorf; Thomas Löcke; Christian Plessl (ACM Trans. Reconfigurable Technol. Syst., 2017)

A Massively Parallel Algorithm for the Approximate Calculation of Inverse p-th Roots of Large Sparse Matrices. - Michael Lass; Stephan Mohr; Hendrik Wiebeler; Thomas D. Kühne; Christian Plessl (PASC, 2018)

Using Approximate Computing for the Calculation of Inverse Matrix pth Roots. - Michael Lass; Thomas D. Kühne; Christian Plessl (IEEE Embed. Syst. Lett., 2018)

A submatrix-based method for approximate matrix function evaluation in the quantum chemistry code CP2K. - Michael Lass; Robert Schade; Thomas D. Kühne; Christian Plessl (SC, 2020)

Accurate Sampling with Noisy Forces from Approximate Computing. - Varadarajan Rengaraj; Michael Lass; Christian Plessl; Thomas D. Kühne (Comput., 2020)

Enabling Electronic Structure-Based Ab-Initio Molecular Dynamics Simulations with Hundreds of Millions of Atoms. - Robert Schade; Tobias Kenter; Hossam Elgabarty; Michael Lass; Ole Schütt; Alfio Lazzaro; Hans Pabst; Stephan Mohr; Jürg Hutter; Thomas D. Kühne; Christian Plessl (CoRR, 2021)

Towards electronic structure-based ab-initio molecular dynamics simulations with hundreds of millions of atoms. - Robert Schade; Tobias Kenter; Hossam Elgabarty; Michael Lass; Ole Schütt; Alfio Lazzaro; Hans Pabst; Stephan Mohr; Jürg Hutter; Thomas D. Kühne; Christian Plessl (Parallel Comput., 2022)

Bringing Massive Parallelism and Hardware Acceleration to Linear Scaling Density Functional Theory Through Targeted Approximations. - Michael Lass (2022)

A computation of D(9) using FPGA Supercomputing. - Lennart Van Hirtum; Patrick De Causmaecker; Jens Goemaere; Tobias Kenter; Heinrich Riebler; Michael Lass; Christian Plessl (CoRR, 2023)

Breaking the exascale barrier for the electronic structure problem in ab-initio molecular dynamics. - Robert Schade; Tobias Kenter; Hossam Elgabarty; Michael Lass; Thomas D. Kühne; Christian Plessl (Int. J. High Perform. Comput. Appl., 2023)

Scalable quantum detector tomography by high-performance computing. - Timon Schapeler; Robert Schade; Michael Lass; Christian Plessl; Tim J. Bartley (CoRR, 2024)

Characterizing Microheterogeneity in Liquid Mixtures via Local Density Fluctuations. - Michael Lass; Tobias Kenter; Christian Plessl; Martin Brehm (Entropy, 2024)

A Computation of the Ninth Dedekind Number Using FPGA Supercomputing. - Lennart Van Hirtum; Patrick De Causmaecker; Jens Goemaere; Tobias Kenter; Heinrich Riebler; Michael Lass; Christian Plessl (ACM Trans. Reconfigurable Technol. Syst., 2024)

AuroraFlow, an Easy-to-Use, Low-Latency FPGA Communication Solution Demonstrated on Multi-FPGA Neural Network Inference. - Gerrit Pape; Bjarne Wintermann; Linus Jungemann; Michael Lass; Marius Meyer; Heinrich Riebler; Christian Plessl (HEART, 2025)