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18 Publications
2022 | Dissertation | LibreCat-ID: 32414
Lass, Michael. Bringing Massive Parallelism and Hardware Acceleration to Linear Scaling Density Functional Theory Through Targeted Approximations. Paderborn: Universität Paderborn, 2022. https://doi.org/10.17619/UNIPB/1-1281.
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| DOI
2022 | Journal Article | LibreCat-ID: 33684 | 
Schade, Robert, Tobias Kenter, Hossam Elgabarty, Michael Lass, Ole Schütt, Alfio Lazzaro, Hans Pabst, et al. “Towards Electronic Structure-Based Ab-Initio Molecular Dynamics Simulations with Hundreds of Millions of Atoms.” Parallel Computing 111 (2022). https://doi.org/10.1016/j.parco.2022.102920.
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2020 | Journal Article | LibreCat-ID: 16277 |
Kühne, Thomas, Marcella Iannuzzi, Mauro Del Ben, Vladimir V. Rybkin, Patrick Seewald, Frederick Stein, Teodoro Laino, et al. “CP2K: An Electronic Structure and Molecular Dynamics Software Package - Quickstep: Efficient and Accurate Electronic Structure Calculations.” The Journal of Chemical Physics 152, no. 19 (2020). https://doi.org/10.1063/5.0007045.
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| arXiv
2020 | Conference Paper | LibreCat-ID: 16898
Lass, Michael, Robert Schade, Thomas Kühne, and Christian Plessl. “A Submatrix-Based Method for Approximate Matrix Function Evaluation in the Quantum Chemistry Code CP2K.” In Proc. International Conference for High Performance Computing, Networking, Storage and Analysis (SC), 1127–40. Los Alamitos, CA, USA: IEEE Computer Society, 2020. https://doi.org/10.1109/SC41405.2020.00084.
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| arXiv
2020 | Journal Article | LibreCat-ID: 12878 |
Rengaraj, Varadarajan, Michael Lass, Christian Plessl, and Thomas Kühne. “Accurate Sampling with Noisy Forces from Approximate Computing.” Computation 8, no. 2 (2020). https://doi.org/10.3390/computation8020039.
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| arXiv
2019 | Journal Article | LibreCat-ID: 21
Richters, Dorothee, Michael Lass, Andrea Walther, Christian Plessl, and Thomas Kühne. “A General Algorithm to Calculate the Inverse Principal P-Th Root of Symmetric Positive Definite Matrices.” Communications in Computational Physics 25, no. 2 (2019): 564–85. https://doi.org/10.4208/cicp.OA-2018-0053.
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| DOI
| arXiv
2018 | Journal Article | LibreCat-ID: 20
Lass, Michael, Thomas Kühne, and Christian Plessl. “Using Approximate Computing for the Calculation of Inverse Matrix P-Th Roots.” Embedded Systems Letters 10, no. 2 (2018): 33–36. https://doi.org/10.1109/LES.2017.2760923.
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| DOI
| arXiv
2018 | Conference Paper | LibreCat-ID: 1590
Lass, Michael, Stephan Mohr, Hendrik Wiebeler, Thomas Kühne, and Christian Plessl. “A Massively Parallel Algorithm for the Approximate Calculation of Inverse P-Th Roots of Large Sparse Matrices.” In Proc. Platform for Advanced Scientific Computing (PASC) Conference. New York, NY, USA: ACM, 2018. https://doi.org/10.1145/3218176.3218231.
LibreCat
| DOI
| arXiv
2017 | Journal Article | LibreCat-ID: 18
Riebler, Heinrich, Michael Lass, Robert Mittendorf, Thomas Löcke, and Christian Plessl. “Efficient Branch and Bound on FPGAs Using Work Stealing and Instance-Specific Designs.” ACM Transactions on Reconfigurable Technology and Systems (TRETS) 10, no. 3 (2017): 24:1-24:23. https://doi.org/10.1145/3053687.
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2016 | Conference Paper | LibreCat-ID: 19
Lass, Michael, Dominik Leibenger, and Christoph Sorge. “Confidentiality and Authenticity for Distributed Version Control Systems - A Mercurial Extension.” In Proc. 41st Conference on Local Computer Networks (LCN). IEEE, 2016. https://doi.org/10.1109/lcn.2016.11.
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2016 | Conference Paper | LibreCat-ID: 25
Lass, Michael, Thomas Kühne, and Christian Plessl. “Using Approximate Computing in Scientific Codes.” In Workshop on Approximate Computing (AC), 2016.
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2015 | Mastersthesis | LibreCat-ID: 1794
Lass, Michael. Localization and Analysis of Code Paths Suitable for Acceleration Using Approximate Computing. Paderborn: Paderborn University, 2015.
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2013 | Bachelorsthesis | LibreCat-ID: 1795
Lass, Michael. Sichere Speicherung Vertraulicher Daten in Verteilten Versionskontrollsystemen. Paderborn: Paderborn University, 2013.
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