@article{16277,
abstract = {CP2K is an open source electronic structure and molecular dynamics software package to perform atomistic simulations of solid-state, liquid, molecular, and biological systems. It is especially aimed at massively parallel and linear-scaling electronic structure methods and state-of-theart ab initio molecular dynamics simulations. Excellent performance for electronic structure calculations is achieved using novel algorithms implemented for modern high-performance computing systems. This review revisits the main capabilities of CP2K to perform efficient and accurate electronic structure simulations. The emphasis is put on density functional theory and multiple post–Hartree–Fock methods using the Gaussian and plane wave approach and its augmented all-electron extension.},
author = {Kühne, Thomas and Iannuzzi, Marcella and Ben, Mauro Del and Rybkin, Vladimir V. and Seewald, Patrick and Stein, Frederick and Laino, Teodoro and Khaliullin, Rustam Z. and Schütt, Ole and Schiffmann, Florian and Golze, Dorothea and Wilhelm, Jan and Chulkov, Sergey and Mohammad Hossein Bani-Hashemian, Mohammad Hossein Bani-Hashemian and Weber, Valéry and Borstnik, Urban and Taillefumier, Mathieu and Jakobovits, Alice Shoshana and Lazzaro, Alfio and Pabst, Hans and Müller, Tiziano and Schade, Robert and Guidon, Manuel and Andermatt, Samuel and Holmberg, Nico and Schenter, Gregory K. and Hehn, Anna and Bussy, Augustin and Belleflamme, Fabian and Tabacchi, Gloria and Glöß, Andreas and Lass, Michael and Bethune, Iain and Mundy, Christopher J. and Plessl, Christian and Watkins, Matt and VandeVondele, Joost and Krack, Matthias and Hutter, Jürg},
journal = {The Journal of Chemical Physics},
number = {19},
title = {{CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations}},
doi = {10.1063/5.0007045},
volume = {152},
year = {2020},
}
@unpublished{16898,
abstract = {Electronic structure calculations based on density-functional theory (DFT)
represent a significant part of today's HPC workloads and pose high demands on
high-performance computing resources. To perform these quantum-mechanical DFT
calculations on complex large-scale systems, so-called linear scaling methods
instead of conventional cubic scaling methods are required. In this work, we
take up the idea of the submatrix method and apply it to the DFT computations
in the software package CP2K. For that purpose, we transform the underlying
numeric operations on distributed, large, sparse matrices into computations on
local, much smaller and nearly dense matrices. This allows us to exploit the
full floating-point performance of modern CPUs and to make use of dedicated
accelerator hardware, where performance has been limited by memory bandwidth
before. We demonstrate both functionality and performance of our implementation
and show how it can be accelerated with GPUs and FPGAs.},
author = {Lass, Michael and Schade, Robert and Kühne, Thomas and Plessl, Christian},
title = {{A Submatrix-Based Method for Approximate Matrix Function Evaluation in the Quantum Chemistry Code CP2K}},
year = {2020},
}
@article{12878,
abstract = {In scientific computing, the acceleration of atomistic computer simulations by means of custom hardware is finding ever-growing application. A major limitation, however, is that the high efficiency in terms of performance and low power consumption entails the massive usage of low precision computing units. Here, based on the approximate computing paradigm, we present an algorithmic method to compensate for numerical inaccuracies due to low accuracy arithmetic operations rigorously, yet still obtaining exact expectation values using a properly modified Langevin-type equation.},
author = {Rengaraj, Varadarajan and Lass, Michael and Plessl, Christian and Kühne, Thomas},
journal = {Computation},
number = {2},
publisher = {MDPI},
title = {{Accurate Sampling with Noisy Forces from Approximate Computing}},
doi = {10.3390/computation8020039},
volume = {8},
year = {2020},
}
@article{21,
abstract = {We address the general mathematical problem of computing the inverse p-th
root of a given matrix in an efficient way. A new method to construct iteration
functions that allow calculating arbitrary p-th roots and their inverses of
symmetric positive definite matrices is presented. We show that the order of
convergence is at least quadratic and that adaptively adjusting a parameter q
always leads to an even faster convergence. In this way, a better performance
than with previously known iteration schemes is achieved. The efficiency of the
iterative functions is demonstrated for various matrices with different
densities, condition numbers and spectral radii.},
author = {Richters, Dorothee and Lass, Michael and Walther, Andrea and Plessl, Christian and Kühne, Thomas},
journal = {Communications in Computational Physics},
number = {2},
pages = {564--585},
publisher = {Global Science Press},
title = {{A General Algorithm to Calculate the Inverse Principal p-th Root of Symmetric Positive Definite Matrices}},
doi = {10.4208/cicp.OA-2018-0053},
volume = {25},
year = {2019},
}
@article{20,
abstract = {Approximate computing has shown to provide new ways to improve performance
and power consumption of error-resilient applications. While many of these
applications can be found in image processing, data classification or machine
learning, we demonstrate its suitability to a problem from scientific
computing. Utilizing the self-correcting behavior of iterative algorithms, we
show that approximate computing can be applied to the calculation of inverse
matrix p-th roots which are required in many applications in scientific
computing. Results show great opportunities to reduce the computational effort
and bandwidth required for the execution of the discussed algorithm, especially
when targeting special accelerator hardware.},
author = {Lass, Michael and Kühne, Thomas and Plessl, Christian},
issn = {1943-0671},
journal = {Embedded Systems Letters},
number = {2},
pages = { 33--36},
publisher = {IEEE},
title = {{Using Approximate Computing for the Calculation of Inverse Matrix p-th Roots}},
doi = {10.1109/LES.2017.2760923},
volume = {10},
year = {2018},
}
@inproceedings{1590,
abstract = {We present the submatrix method, a highly parallelizable method for the approximate calculation of inverse p-th roots of large sparse symmetric matrices which are required in different scientific applications. Following the idea of Approximate Computing, we allow imprecision in the final result in order to utilize the sparsity of the input matrix and to allow massively parallel execution. For an n x n matrix, the proposed algorithm allows to distribute the calculations over n nodes with only little communication overhead. The result matrix exhibits the same sparsity pattern as the input matrix, allowing for efficient reuse of allocated data structures.
We evaluate the algorithm with respect to the error that it introduces into calculated results, as well as its performance and scalability. We demonstrate that the error is relatively limited for well-conditioned matrices and that results are still valuable for error-resilient applications like preconditioning even for ill-conditioned matrices. We discuss the execution time and scaling of the algorithm on a theoretical level and present a distributed implementation of the algorithm using MPI and OpenMP. We demonstrate the scalability of this implementation by running it on a high-performance compute cluster comprised of 1024 CPU cores, showing a speedup of 665x compared to single-threaded execution.},
author = {Lass, Michael and Mohr, Stephan and Wiebeler, Hendrik and Kühne, Thomas and Plessl, Christian},
booktitle = {Proc. Platform for Advanced Scientific Computing (PASC) Conference},
isbn = {978-1-4503-5891-0/18/07},
keyword = {approximate computing, linear algebra, matrix inversion, matrix p-th roots, numeric algorithm, parallel computing},
location = {Basel, Switzerland},
publisher = {ACM},
title = {{A Massively Parallel Algorithm for the Approximate Calculation of Inverse p-th Roots of Large Sparse Matrices}},
doi = {10.1145/3218176.3218231},
year = {2018},
}
@inproceedings{25,
author = {Lass, Michael and Kühne, Thomas and Plessl, Christian},
booktitle = {Workshop on Approximate Computing (AC)},
title = {{Using Approximate Computing in Scientific Codes}},
year = {2016},
}
@inproceedings{169,
abstract = {We apply methods of genetic programming to a general problem from software engineering, namely example-based generation of specifications. In particular, we focus on model transformation by example. The definition and implementation of model transformations is a task frequently carried out by domain experts, hence, a (semi-)automatic approach is desirable. This application is challenging because the underlying search space has rich semantics, is high-dimensional, and unstructured. Hence, a computationally brute-force approach would be unscalable and potentially infeasible. To address that problem, we develop a sophisticated approach of designing complex mutation operators. We define ‘patterns’ for constructing mutation operators and report a successful case study. Furthermore, the code of the evolved model transformation is required to have high maintainability and extensibility, that is, the code should be easily readable by domain experts. We report an evaluation of this approach in a software engineering case study.},
author = {Kühne, Thomas and Hamann, Heiko and Arifulina, Svetlana and Engels, Gregor},
booktitle = {Proceedings of the 19th European Conference on Genetic Programming (EuroGP 2016)},
pages = {278----293},
title = {{Patterns for Constructing Mutation Operators: Limiting the Search Space in a Software Engineering Application}},
doi = {10.1007/978-3-319-30668-1_18},
year = {2016},
}