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.
LibreCat: /record/53474
DOI: 10.3390/e26040322

Schapeler, Timon, et al. “Scalable Quantum Detector Tomography by High-Performance Computing.” ArXiv:2404.02844, 2024.
LibreCat: /record/53202
arXiv: 2404.02844

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 .
LibreCat: /record/53663
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.
LibreCat: /record/45361
DOI: 10.1177/10943420231177631

Van Hirtum, Lennart, et al. “A Computation of D(9) Using FPGA Supercomputing.” ArXiv:2304.03039, 2023.
LibreCat: /record/43439
arXiv: 2304.03039

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.
LibreCat: /record/32414
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.
LibreCat: /record/33684
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.
LibreCat: /record/16277
DOI: 10.1063/5.0007045
arXiv: 2003.03868

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.
LibreCat: /record/16898
DOI: 10.1109/SC41405.2020.00084
arXiv: 2004.10811

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.
LibreCat: /record/12878
DOI: 10.3390/computation8020039
arXiv: 1907.08497

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.
LibreCat: /record/21
DOI: 10.4208/cicp.OA-2018-0053
arXiv: 1703.02456

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.
LibreCat: /record/20
DOI: 10.1109/LES.2017.2760923
arXiv: 1703.02283

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.
LibreCat: /record/1590
DOI: 10.1145/3218176.3218231
arXiv: 1710.10899

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.
LibreCat: /record/18
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.
LibreCat: /record/19
DOI: 10.1109/lcn.2016.11

Lass, Michael, et al. “Using Approximate Computing in Scientific Codes.” Workshop on Approximate Computing (AC), 2016.
LibreCat: /record/25

Lass, Michael. Localization and Analysis of Code Paths Suitable for Acceleration Using Approximate Computing. Paderborn University, 2015.
LibreCat: /record/1794

Lass, Michael. Sichere Speicherung Vertraulicher Daten in Verteilten Versionskontrollsystemen. Paderborn University, 2013.
LibreCat: /record/1795