Lass, Michael, et al. “Characterizing Microheterogeneity in Liquid Mixtures via Local Density Fluctuations.”10.3390/e26040322__Entropy, vol. 26, no. 4, 322, MDPI AG, 2024, doi:10.3390/e26040322.

Schapeler, Timon, et al. “Scalable Quantum Detector Tomography by High-Performance Computing.” ArXiv:2404.02844, 2024.

Bauer, Carsten, et al. “Noctua 2 Supercomputer.”10.17815/jlsrf-8-187 __Journal of Large-Scale Research Facilities, vol. 9, 2024, doi:10.17815/jlsrf-8-187 .

Schade, Robert, et al. “Breaking the Exascale Barrier for the Electronic Structure Problem in Ab-Initio Molecular Dynamics.”10.1177/10943420231177631__The International Journal of High Performance Computing Applications, 109434202311776, SAGE Publications, 2023, doi:10.1177/10943420231177631.

Van Hirtum, Lennart, et al. “A Computation of D(9) Using FPGA Supercomputing.” ArXiv:2304.03039, 2023.

Lass, Michael. Bringing Massive Parallelism and Hardware Acceleration to Linear Scaling Density Functional Theory Through Targeted Approximations. Universität Paderborn, 2022, doi:10.17619/UNIPB/1-1281.

Schade, Robert, et al. “Towards Electronic Structure-Based Ab-Initio Molecular Dynamics Simulations with Hundreds of Millions of Atoms.”10.1016/j.parco.2022.102920__Parallel Computing, vol. 111, 102920, Elsevier BV, 2022, doi:10.1016/j.parco.2022.102920.

Kühne, Thomas, et al. “CK: An Electronic Structure and Molecular Dynamics Software Package - Quickstep: Efficient and Accurate Electronic Structure Calculations.”10.1063/5.0007045__The Journal of Chemical Physics, vol. 152, no. 19, 194103, 2020, doi:10.1063/5.0007045.

Lass, Michael, et al. “A Submatrix-Based Method for Approximate Matrix Function Evaluation in the Quantum Chemistry Code CK.”10.1109/SC41405.2020.00084__Proc. International Conference for High Performance Computing, Networking, Storage and Analysis (SC), IEEE Computer Society, 2020, pp. 1127–40, doi:10.1109/SC41405.2020.00084.

Rengaraj, Varadarajan, et al. “Accurate Sampling with Noisy Forces from Approximate Computing.”10.3390/computation8020039__Computation, vol. 8, no. 2, 39, MDPI, 2020, doi:10.3390/computation8020039.

Richters, Dorothee, et al. “A General Algorithm to Calculate the Inverse Principal P-Th Root of Symmetric Positive Definite Matrices.”10.4208/cicp.OA-2018-0053__Communications in Computational Physics, vol. 25, no. 2, Global Science Press, 2019, pp. 564–85, doi:10.4208/cicp.OA-2018-0053.

Lass, Michael, et al. “Using Approximate Computing for the Calculation of Inverse Matrix P-Th Roots.”10.1109/LES.2017.2760923__Embedded Systems Letters, vol. 10, no. 2, IEEE, 2018, pp. 33–36, doi:10.1109/LES.2017.2760923.

Lass, Michael, et al. “A Massively Parallel Algorithm for the Approximate Calculation of Inverse P-Th Roots of Large Sparse Matrices.”10.1145/3218176.3218231__Proc. Platform for Advanced Scientific Computing (PASC) Conference, ACM, 2018, doi:10.1145/3218176.3218231.

Riebler, Heinrich, et al. “Efficient Branch and Bound on FPGAs Using Work Stealing and Instance-Specific Designs.”10.1145/3053687__ACM Transactions on Reconfigurable Technology and Systems (TRETS), vol. 10, no. 3, Association for Computing Machinery (ACM), 2017, p. 24:1-24:23, doi:10.1145/3053687.

Lass, Michael, et al. “Confidentiality and Authenticity for Distributed Version Control Systems - A Mercurial Extension.”10.1109/lcn.2016.11__Proc. 41st Conference on Local Computer Networks (LCN), IEEE, 2016, doi:10.1109/lcn.2016.11.

Lass, Michael, et al. “Using Approximate Computing in Scientific Codes.” Workshop on Approximate Computing (AC), 2016.

Lass, Michael. Localization and Analysis of Code Paths Suitable for Acceleration Using Approximate Computing. Paderborn University, 2015.

Lass, Michael. Sichere Speicherung Vertraulicher Daten in Verteilten Versionskontrollsystemen. Paderborn University, 2013.