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  • 1. Abel, John H.
    et al.
    Drawert, Brian
    Hellander, Andreas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Petzold, Linda R.
    GillesPy: A Python package for stochastic model building and simulation2016In: IEEE Life Sciences Letters, E-ISSN 2332-7685, Vol. 2, p. 35-38Article in journal (Refereed)
  • 2. Ahmed, Laeeq
    et al.
    Georgiev, Valentin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Capuccini, Marco
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Toor, Salman
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Schaal, Wesley
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Laure, Erwin
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Efficient iterative virtual screening with Apache Spark and conformal prediction2018In: Journal of Cheminformatics, E-ISSN 1758-2946, Vol. 10, article id 8Article in journal (Refereed)
  • 3.
    Ait-Mlouk, Addi
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Alawadi, Sadi
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Toor, Salman
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Hellander, Andreas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    FedQAS: Privacy-Aware Machine Reading Comprehension with Federated Learning2022In: Applied Sciences, E-ISSN 2076-3417, Vol. 12, no 6, article id 3130Article in journal (Refereed)
    Abstract [en]

    Machine reading comprehension (MRC) of text data is a challenging task in Natural Language Processing (NLP), with a lot of ongoing research fueled by the release of the Stanford Question Answering Dataset (SQuAD) and Conversational Question Answering (CoQA). It is considered to be an effort to teach computers how to "understand" a text, and then to be able to answer questions about it using deep learning. However, until now, large-scale training on private text data and knowledge sharing has been missing for this NLP task. Hence, we present FedQAS, a privacy-preserving machine reading system capable of leveraging large-scale private data without the need to pool those datasets in a central location. The proposed approach combines transformer models and federated learning technologies. The system is developed using the FEDn framework and deployed as a proof-of-concept alliance initiative. FedQAS is flexible, language-agnostic, and allows intuitive participation and execution of local model training. In addition, we present the architecture and implementation of the system, as well as provide a reference evaluation based on the SQuAD dataset, to showcase how it overcomes data privacy issues and enables knowledge sharing between alliance members in a Federated learning setting.

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  • 4.
    Al Khatib, Sultan M.
    et al.
    Al Balqa Appl Univ BAU, Prince Abdullah Bin Ghazi Fac Informat & Commun Te, Dept Software Engn, Al Salt 19117, Jordan..
    Alkharabsheh, Khalid
    Al Balqa Appl Univ BAU, Prince Abdullah Bin Ghazi Fac Informat & Commun Te, Dept Software Engn, Al Salt 19117, Jordan..
    Alawadi, Sadi
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. Halmstad Univ, Ctr Appl Intelligent Syst Res, Sch Informat Technol, S-30118 Halmstad, Sweden.
    Selection of human evaluators for design smell detection using dragonfly optimization algorithm: An empirical study2023In: Information and Software Technology, ISSN 0950-5849, E-ISSN 1873-6025, Vol. 155, article id 107120Article in journal (Refereed)
    Abstract [en]

    Context: Design smell detection is considered an efficient activity that decreases maintainability expenses and improves software quality. Human context plays an essential role in this domain.Objective: In this paper, we propose a search-based approach to optimize the selection of human evaluators for design smell detection.Method: For this purpose, Dragonfly Algorithm (DA) is employed to identify the optimal or near-optimal human evaluator's profiles. An online survey is designed and asks the evaluators to evaluate a sample of classes for the presence of god class design smell. The Kappa-Fleiss test has been used to validate the proposed approach. Results: The results show that the dragonfly optimization algorithm can be utilized effectively to decrease the efforts (time, cost ) of design smell detection concerning the identification of the number and the optimal or near-optimal profile of human experts required for the evaluation process.Conclusions: A Search-based approach can be effectively used for improving a god-class design smell detection. Consequently, this leads to minimizing the maintenance cost.

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  • 5.
    Alkharabsheh, Khalid
    et al.
    Al Balqa Appl Univ BAU, Prince Abdullah bin Ghazi Fac Informat & Commun T, Dept Software Engn, Salt, Jordan.
    Alawadi, Sadi
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. Halmstad Univ, Sch Informat Technol, Ctr Appl Intelligent Syst Res, S-30118 Halmstad, Sweden.
    Ignaim, Karam
    Al Balqa Appl Univ BAU, Prince Abdullah bin Ghazi Fac Informat & Commun T, Dept Software Engn, Salt, Jordan.
    Zanoon, Nabeel
    Al Balqa Appl Univ BAU, Appl Sci Dept, Aqaba Coll, Salt, Jordan.
    Crespo, Yania
    Univ Valladolid, Escuela Ingn Informat, Dept Informat, Campus Miguel Delibes,Paseo Belen 15, Valladolid 47011, Spain.
    Manso, Esperanza
    Al Balqa Appl Univ BAU, Prince Abdullah bin Ghazi Fac Informat & Commun T, Dept Software Engn, Salt, Jordan.;Univ Valladolid, Escuela Ingn Informat, Dept Informat, Campus Miguel Delibes,Paseo Belen 15, Valladolid 47011, Spain.
    Taboada, Jose A.
    Univ Santiago Compostela, Ctr Singular Invest Tecnol Intelixent, CiTIUS, Santiago De Compostela 15782, Spain.
    Prioritization of god class design smell: A multi-criteria based approach2022In: JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES, ISSN 1319-1578, Vol. 34, no 10, p. 9332-9342Article in journal (Refereed)
    Abstract [en]

    Context: Design smell Prioritization is a significant activity that tunes the process of software quality enhancement and raises its life cycle.

    Objective: A multi-criteria merge strategy for Design Smell prioritization is described. The strategy is exemplified with the case of God Class Design Smell.

    Method: An empirical adjustment of the strategy is performed using a dataset of 24 open source projects. Empirical evaluation was conducted in order to check how is the top ranked God Classes obtained by the proposed technique compared against the top ranked God class according to the opinion of developers involved in each of the projects in the dataset.

    Results: Results of the evaluation show the strategy should be improved. Analysis of the differences between projects where respondents answer correlates with the strategy and those projects where there is no correlation should be done.

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  • 6. AL-Naday, Mays
    et al.
    Reed, Martin
    Dobre, Vlad
    Toor, Salman
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Volckaert, Bruno
    De Turck, Filip
    Service-based Federated Deep Reinforcement Learning for Anomaly Detection in Fog Ecosystems2023In: 2023 26th Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN), Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 121-128Conference paper (Refereed)
    Abstract [en]

    With Digital transformation, the diversity of services and infrastructure in backhaul fog network(s) is rising to unprecedented levels. This is causing a rising threat of a wider range of cyber attacks coupled with a growing integration of constrained range of infrastructure, particularly seen at the network edge. Deep reinforcement-based learning is an attractive approach to detecting attacks, as it allows less dependency on labeled data with better ability to classify different attacks. However, current approaches to learning are known to be computationally expensive (cost) and the learning experience can be negatively impacted by the presence of outliers and noise (quality). This work tackles both the cost and quality challenges with a novel service-based federated deep reinforcement learning solution, enabling anomaly detection and attack classification at a reduced data cost and with better quality. The federated settings in the proposed approach enable multiple edge units to create clusters that follow a bottom-up learning approach. The proposed solution adapts deep Q-learning Network (DQN) for service-tunable flow classification, and introduces a novel federated DQN (FDQN) for federated learning. Through such targeted training and validation, variation in data patterns and noise is reduced. This leads to improved performance per service with lower training cost. Performance and cost of the solution, along with sensitivity to exploration parameters are evaluated using an example publicly available dataset (UNSW-NB15). Evaluation results show the proposed solution to maintain detection accuracy with lower data supply, while improving the classification rate by a factor of ≈ 2.

  • 7.
    Andrejev, Andrej
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Toor, Salman
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Hellander, Andreas
    Holmgren, Sverker
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Risch, Tore
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    Scientific analysis by queries in extended SPARQL over a scalable e-Science data store2013In: Proc. 9th International Conference on e-Science, Los Alamitos, CA: IEEE Computer Society, 2013, p. 98-106Conference paper (Refereed)
  • 8. Antolín, Roberto
    et al.
    Nettelblad, Carl
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Gorjanc, Gregor
    Money, Daniel
    Hickey, John M.
    A hybrid method for the imputation of genomic data in livestock populations2017In: Genetics Selection Evolution, ISSN 0999-193X, E-ISSN 1297-9686, Vol. 49, article id 30Article in journal (Refereed)
  • 9. Anzt, Hartwig
    et al.
    Lukarski, Dimitar
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Tomov, Stanimire
    Dongarra, Jack
    Self-adaptive multiprecision preconditioners on multicore and manycore architectures2015In: High Performance Computing for Computational Science – VECPAR 2014, Springer, 2015, p. 115-123Conference paper (Refereed)
  • 10. Appleton, Owen
    et al.
    Cameron, David
    Cernák, Jozef
    Dóbé, Péter
    Ellert, Mattias
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, High Energy Physics.
    Frågåt, Thomas
    Grønager, Michael
    Johansson, Daniel
    Jönemo, Johan
    Kleist, Josva
    Kocan, Marek
    Konstantinov, Aleksandr
    Kónya, Balázs
    Márton, Iván
    Mohn, Bjarte
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, High Energy Physics.
    Möller, Steffen
    Müller, Henning
    Nagy, Zsombor
    Nilsen, Jon K.
    Ould-Saada, Farid
    Pajchel, Katarina
    Qiang, Weizhong
    Read, Alexander
    Rosendahl, Peter
    Röczei, Gábor
    Savko, Martin
    Skou Andersen, Martin
    Smirnova, Oxana
    Stefán, Péter
    Szalai, Ferenc
    Taga, Adrian
    Toor, Salman Z.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Wäänänen, Anders
    Zhou, Xin
    The next-generation ARC middleware2010In: Annales des télécommunications, ISSN 0003-4347, E-ISSN 1958-9395, Vol. 65, p. 771-776Article in journal (Refereed)
  • 11. Arjmand, Doghonay
    et al.
    Engblom, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Kreiss, Gunilla
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Temporal upscaling in micromagnetism via heterogeneous multiscale methods2019In: Journal of Computational and Applied Mathematics, ISSN 0377-0427, E-ISSN 1879-1778, Vol. 345, p. 99-113Article in journal (Refereed)
  • 12.
    Arndt, Daniel
    et al.
    Oak Ridge Natl Lab, Computat Sci & Engn Div, Scalable Algorithms & Coupled Phys Grp, 1 Bethel Valley Rd, Oak Ridge, TN 37831 USA..
    Bangerth, Wolfgang
    Colorado State Univ, Dept Math, Ft Collins, CO 80523 USA.;Colorado State Univ, Dept Geosci, Ft Collins, CO 80523 USA..
    Blais, Bruno
    Polytech Montreal, Dept Chem Engn, Res Unit Ind Flows Proc URPEI, POB 6079, Montreal, PQ H3C 3A7, Canada..
    Fehling, Marc
    Colorado State Univ, Dept Math, Ft Collins, CO 80523 USA..
    Gassmoller, Rene
    Univ Florida, Dept Geol Sci, 1843 Stadium Rd, Gainesville, FL 32611 USA..
    Heister, Timo
    Clemson Univ, Sch Math & Stat Sci, Clemson, SC 29634 USA..
    Heltai, Luca
    Scuola Int Super Studi Avanzati, SISSA, Via Bonomea 265, I-34136 Trieste, Italy..
    Koecher, Uwe
    Helmut Schmidt Univ, Univ Fed Armed Forces Hamburg, Chair Numer Math, Holstenhofweg 85, D-22043 Hamburg, Germany..
    Kronbichler, Martin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. Tech Univ Munich, Inst Computat Mech, Boltzmannstr 15, D-85748 Garching, Germany.
    Maier, Matthias
    Texas A&M Univ, Dept Math, 3368 TAMU, College Stn, TX 77845 USA..
    Munch, Peter
    Tech Univ Munich, Inst Computat Mech, Boltzmannstr 15, D-85748 Garching, Germany.;Helmholtz Zentrum Hereon, Inst Mat Syst Modeling, Max Planck Str 1, D-21502 Geesthacht, Germany..
    Pelteret, Jean-Paul
    Proell, Sebastian
    Tech Univ Munich, Inst Computat Mech, Boltzmannstr 15, D-85748 Garching, Germany..
    Simon, Konrad
    Univ Hamburg, Dept Math, Ctr Earth Syst Res & Sustainabil CEN, Grindelberg 5, D-20144 Hamburg, Germany..
    Turcksin, Bruno
    Oak Ridge Natl Lab, Computat Sci & Engn Div, Scalable Algorithms & Coupled Phys Grp, 1 Bethel Valley Rd, Oak Ridge, TN 37831 USA..
    Wells, David
    Univ N Carolina, Dept Math, Chapel Hill, NC 27516 USA..
    Zhang, Jiaqi
    Clemson Univ, Sch Math & Stat Sci, Clemson, SC 29634 USA..
    The deal. II library, Version 9.32021In: Journal of Numerical Mathematics, ISSN 1570-2820, E-ISSN 1569-3953, Vol. 29, no 3, p. 171-186Article in journal (Refereed)
    Abstract [en]

    This paper provides an overview of the new features of the finite element library deal . II, version 9.3.

  • 13.
    Artemov, Anton G.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Approximate multiplication of nearly sparse matrices with decay in a fully recursive distributed task-based parallel framework2020Manuscript (preprint) (Other academic)
    Abstract [en]

    Abstract.In this paper we consider parallel implementations of approximate multiplication oflarge matrices with exponential decay of elements. Such matrices arise in computations related to electronic structure calculations and some other fields of computational science. Commonly, sparsity is introduced by dropping out small entries (truncation) of input matrices. Another approach, the sparse approximate multiplication algorithm [M. Challacombe and N. Bock, arXiv preprint1011.3534, 2010] performs truncation of sub-matrix products. We consider these two methods and their combination, i.e. truncation of both input matrices and sub-matrix products. Implementations done using the Chunks and Tasks programming model and library [E. H. Rubensson and E. Rudberg,Parallel Comput., 40:328343, 2014] are presented and discussed. We show that the absolute error in the Frobenius norm behaves as and  for all three methods, where  is the matrix size and  is the truncation threshold. We compare the methods on a model problem and show that the combined method outperforms the original two. The methods are also applied to matrices coming from large chemical systems with  atoms. We showthat the combination of the two methods achieves better weak scaling by reducing the amount of communication by a factor of .

  • 14.
    Artemov, Anton G.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Inverse factorization in electronic structure theory: Analysis and parallelization2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    This licentiate thesis is a part of an effort to run large electronic structure calculations in modern computational environments with distributed memory. The ultimate goal is to model materials consisting of millions of atoms at the level of quantum mechanics. In particular, the thesis focuses on different aspects of a computational problem of inverse factorization of Hermitian positive definite matrices. The considered aspects are numerical properties of the algorithms and parallelization. Not only is an efficient and scalable computation of inverse factors necessary in order to be able to run large scale electronic computations based on the Hartree–Fock or Kohn–Sham approaches with the self-consistent field procedure, but it can be applied more generally for preconditioner construction.

    Parallelization of algorithms with unknown load and data distributions requires a paradigm shift in programming. In this thesis we also discuss a few parallel programming models with focus on task-based models, and, more specifically, the Chunks and Tasks model.

    List of papers
    1. Localized inverse factorization
    Open this publication in new window or tab >>Localized inverse factorization
    2021 (English)In: IMA Journal of Numerical Analysis, ISSN 0272-4979, E-ISSN 1464-3642, Vol. 41, no 1, p. 729-763Article in journal (Refereed) Published
    Abstract [en]

    We propose a localized divide and conquer algorithm for inverse factorization S-1 = ZZ* of Hermitian positive definite matrices S with localized structure, e.g. exponential decay with respect to some given distance function on the index set of S. The algorithm is a reformulation of recursive inverse factorization (Rubensson et al. (2008) Recursive inverse factorization. J. Chem. Phys., 128, 104105) but makes use of localized operations only. At each level of the recursion, the problem is cut into two subproblems and their solutions are combined using iterative refinement (Niklasson (2004) Iterative refinement method for the approximate factorization of a matrix inverse. Phys. Rev. B, 70, 193102) to give a solution to the original problem. The two subproblems can be solved in parallel without any communication and, using the localized formulation, the cost of combining their results is negligible compared to the overall cost for sufficiently large systems and appropriate partitions of the problem. We also present an alternative derivation of iterative refinement based on a sign matrix formulation, analyze the stability and propose a parameterless stopping criterion. We present bounds for the initial factorization error and the number of iterations in terms of the condition number of S when the starting guess is given by the solution of the two subproblems in the binary recursion. These bounds are used in theoretical results for the decay properties of the involved matrices. We demonstrate the localization properties of our algorithm for matrices corresponding to nearest neighbor overlap on one-, two- and three-dimensional lattices, as well as basis set overlap matrices generated using the Hartree-Fock and Kohn-Sham density functional theory electronic structure program Ergo (Rudberg et al. (2018) Ergo: an open-source program for linear-scaling electronic structure. SoftwareX, 7, 107). We evaluate the parallel performance of our implementation based on the chunks and tasks programming model, showing that the proposed localization of the algorithm results in a dramatic reduction of communication costs.

    National Category
    Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-381327 (URN)10.1093/imanum/drz075 (DOI)000727196700021 ()
    Projects
    eSSENCE
    Funder
    Swedish Research Council, 621-2012-3861
    Available from: 2020-04-28 Created: 2019-04-08 Last updated: 2022-01-10Bibliographically approved
    2. Parallelization and scalability analysis of inverse factorization using the chunks and tasks programming model
    Open this publication in new window or tab >>Parallelization and scalability analysis of inverse factorization using the chunks and tasks programming model
    2019 (English)In: Parallel Computing, ISSN 0167-8191, E-ISSN 1872-7336, Vol. 89, p. 102548:1-12, article id 102548Article in journal (Refereed) Published
    National Category
    Computational Mathematics Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-381329 (URN)10.1016/j.parco.2019.102548 (DOI)000498316500003 ()
    Projects
    eSSENCE
    Available from: 2019-09-02 Created: 2019-04-08 Last updated: 2021-06-17Bibliographically approved
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  • 15.
    Artemov, Anton G.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Parallelization of dynamic algorithms for electronic structure calculations2021Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The aim of electronic structure calculations is to simulate behavior of complex materials by resolving interactions between electrons and nuclei in atoms at the level of quantum mechanics. Progress in the field allows to reduce the computational complexity of the solution methods to linear so that the computational time scales proportionally to the size of the physical system. To solve large scale problems one uses parallel computers and scalable codes. Often the scalability is limited by the data distribution.

    This thesis focuses on a number of problems arising in electronic structure calculations, such as inverse factorization of Hermitian positive definite matrices, approximate sparse matrix multiplication, and density matrix purification methods. No assumptions are made about the data distribution, instead, it is explored dynamically.

    The thesis consists of an introduction and five papers. Particularly, in Paper I we present a new theoretical framework for localized matrices with exponential decay of elements. We describe a new localized method for inverse factorization of Hermitian positive definite matrices. We show that it has reduced communication costs compared to other widely used parallel methods. In Paper II we present a parallel implementation of the method within the Chunks and Tasks programming model and do a scalability analysis based on critical path length estimation.

    We focus on the density matrix purification technique and its core operation, sparse matrix-matrix multiplication, in Papers III and IV. We analyze the sparse approximate matrix multiplication algorithm with the proposed localization framework, add a prior truncation step, and derive the asymptotic behavior of the Frobenius norm of the error. We employ the sparse approximate multiplication algorithm in the density matrix purification process and propose a method to control the error norm by choosing the right truncation threshold value. 

    We present a new version of the Chunks and Tasks matrix library in Paper V. The library functionality and architecture are described and discussed. The efficiency of the library is demonstrated in a few computational experiments.

    List of papers
    1. Localized inverse factorization
    Open this publication in new window or tab >>Localized inverse factorization
    2021 (English)In: IMA Journal of Numerical Analysis, ISSN 0272-4979, E-ISSN 1464-3642, Vol. 41, no 1, p. 729-763Article in journal (Refereed) Published
    Abstract [en]

    We propose a localized divide and conquer algorithm for inverse factorization S-1 = ZZ* of Hermitian positive definite matrices S with localized structure, e.g. exponential decay with respect to some given distance function on the index set of S. The algorithm is a reformulation of recursive inverse factorization (Rubensson et al. (2008) Recursive inverse factorization. J. Chem. Phys., 128, 104105) but makes use of localized operations only. At each level of the recursion, the problem is cut into two subproblems and their solutions are combined using iterative refinement (Niklasson (2004) Iterative refinement method for the approximate factorization of a matrix inverse. Phys. Rev. B, 70, 193102) to give a solution to the original problem. The two subproblems can be solved in parallel without any communication and, using the localized formulation, the cost of combining their results is negligible compared to the overall cost for sufficiently large systems and appropriate partitions of the problem. We also present an alternative derivation of iterative refinement based on a sign matrix formulation, analyze the stability and propose a parameterless stopping criterion. We present bounds for the initial factorization error and the number of iterations in terms of the condition number of S when the starting guess is given by the solution of the two subproblems in the binary recursion. These bounds are used in theoretical results for the decay properties of the involved matrices. We demonstrate the localization properties of our algorithm for matrices corresponding to nearest neighbor overlap on one-, two- and three-dimensional lattices, as well as basis set overlap matrices generated using the Hartree-Fock and Kohn-Sham density functional theory electronic structure program Ergo (Rudberg et al. (2018) Ergo: an open-source program for linear-scaling electronic structure. SoftwareX, 7, 107). We evaluate the parallel performance of our implementation based on the chunks and tasks programming model, showing that the proposed localization of the algorithm results in a dramatic reduction of communication costs.

    National Category
    Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-381327 (URN)10.1093/imanum/drz075 (DOI)000727196700021 ()
    Projects
    eSSENCE
    Funder
    Swedish Research Council, 621-2012-3861
    Available from: 2020-04-28 Created: 2019-04-08 Last updated: 2022-01-10Bibliographically approved
    2. Parallelization and scalability analysis of inverse factorization using the chunks and tasks programming model
    Open this publication in new window or tab >>Parallelization and scalability analysis of inverse factorization using the chunks and tasks programming model
    2019 (English)In: Parallel Computing, ISSN 0167-8191, E-ISSN 1872-7336, Vol. 89, p. 102548:1-12, article id 102548Article in journal (Refereed) Published
    National Category
    Computational Mathematics Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-381329 (URN)10.1016/j.parco.2019.102548 (DOI)000498316500003 ()
    Projects
    eSSENCE
    Available from: 2019-09-02 Created: 2019-04-08 Last updated: 2021-06-17Bibliographically approved
    3. Approximate multiplication of nearly sparse matrices with decay in a fully recursive distributed task-based parallel framework
    Open this publication in new window or tab >>Approximate multiplication of nearly sparse matrices with decay in a fully recursive distributed task-based parallel framework
    2020 (English)Manuscript (preprint) (Other academic)
    Abstract [en]

    Abstract.In this paper we consider parallel implementations of approximate multiplication oflarge matrices with exponential decay of elements. Such matrices arise in computations related to electronic structure calculations and some other fields of computational science. Commonly, sparsity is introduced by dropping out small entries (truncation) of input matrices. Another approach, the sparse approximate multiplication algorithm [M. Challacombe and N. Bock, arXiv preprint1011.3534, 2010] performs truncation of sub-matrix products. We consider these two methods and their combination, i.e. truncation of both input matrices and sub-matrix products. Implementations done using the Chunks and Tasks programming model and library [E. H. Rubensson and E. Rudberg,Parallel Comput., 40:328343, 2014] are presented and discussed. We show that the absolute error in the Frobenius norm behaves as and  for all three methods, where  is the matrix size and  is the truncation threshold. We compare the methods on a model problem and show that the combined method outperforms the original two. The methods are also applied to matrices coming from large chemical systems with  atoms. We showthat the combination of the two methods achieves better weak scaling by reducing the amount of communication by a factor of .

    National Category
    Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-425136 (URN)
    Projects
    eSSENCE - An eScience Collaboration
    Available from: 2020-11-12 Created: 2020-11-12 Last updated: 2023-10-26Bibliographically approved
    4. Sparse approximate matrix-matrix multiplication for density matrix purification with error control
    Open this publication in new window or tab >>Sparse approximate matrix-matrix multiplication for density matrix purification with error control
    2021 (English)In: Journal of Computational Physics, ISSN 0021-9991, E-ISSN 1090-2716, Vol. 438, article id 110354Article in journal (Refereed) Published
    Abstract [en]

    We propose an accelerated density matrix purification scheme with error control. The method makes use of the scale-and-fold acceleration technique and screening of submatrix products in the block-sparse matrix-matrix multiplies to reduce the computational cost. An error bound and a parameter sweep are combined to select a threshold value for the screening, such that the error can be controlled. We evaluate the performance of the method in comparison to purification without acceleration and without submatrix product screening.

    Place, publisher, year, edition, pages
    Elsevier, 2021
    Keywords
    Electronic structure calculations, Density matrix methods, Error control, Sparse matrices
    National Category
    Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-425141 (URN)10.1016/j.jcp.2021.110354 (DOI)000663409500011 ()
    Projects
    eSSENCE
    Funder
    eSSENCE - An eScience CollaborationSwedish National Infrastructure for Computing (SNIC)
    Available from: 2020-11-12 Created: 2020-11-12 Last updated: 2024-01-15Bibliographically approved
    5. The Chunks and Tasks Matrix Library
    Open this publication in new window or tab >>The Chunks and Tasks Matrix Library
    2022 (English)In: SoftwareX, E-ISSN 2352-7110, Vol. 19, article id 101159Article in journal (Refereed) Published
    Abstract [en]

    We present a C++ header-only parallel sparse matrix library, based on sparse quadtree representation of matrices using the Chunks and Tasks programming model. The library implements a number of sparse matrix algorithms for distributed memory parallelization that are able to dynamically exploit data locality to avoid movement of data. This is demonstrated for the example of block-sparse matrix-matrix multiplication applied to three sequences of matrices with different nonzero structure, using the CHT-MPI 2.0 runtime library implementation of the Chunks and Tasks model. The runtime library succeeds to dynamically load balance the calculation regardless of the sparsity structure.

    Place, publisher, year, edition, pages
    Elsevier, 2022
    National Category
    Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-426148 (URN)10.1016/j.softx.2022.101159 (DOI)000879382700003 ()
    Projects
    eSSENCE - An eScience Collaboration
    Note

    Title in thesis list of papers: The Chunks and Tasks matrix library 2.0

    Available from: 2020-11-25 Created: 2020-11-25 Last updated: 2023-01-12Bibliographically approved
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  • 16.
    Artemov, Anton G.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Rubensson, Emanuel H.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Sparse approximate matrix-matrix multiplication for density matrix purification with error control2021In: Journal of Computational Physics, ISSN 0021-9991, E-ISSN 1090-2716, Vol. 438, article id 110354Article in journal (Refereed)
    Abstract [en]

    We propose an accelerated density matrix purification scheme with error control. The method makes use of the scale-and-fold acceleration technique and screening of submatrix products in the block-sparse matrix-matrix multiplies to reduce the computational cost. An error bound and a parameter sweep are combined to select a threshold value for the screening, such that the error can be controlled. We evaluate the performance of the method in comparison to purification without acceleration and without submatrix product screening.

    Download full text (pdf)
    fulltext
  • 17.
    Artemov, Anton G.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Rudberg, Elias
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Rubensson, Emanuel H.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Parallelization and scalability analysis of inverse factorization using the chunks and tasks programming model2019In: Parallel Computing, ISSN 0167-8191, E-ISSN 1872-7336, Vol. 89, p. 102548:1-12, article id 102548Article in journal (Refereed)
  • 18.
    Ausmees, Kristiina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Efficient computational methods for applications in genomics2019Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    During the last two decades, advances in molecular technology have facilitated the sequencing and analysis of ancient DNA recovered from archaeological finds, contributing to novel insights into human evolutionary history. As more ancient genetic information has become available, the need for specialized methods of analysis has also increased. In this thesis, we investigate statistical and computational models for analysis of genetic data, with a particular focus on the context of ancient DNA.

    The main focus is on imputation, or the inference of missing genotypes based on observed sequence data. We present results from a systematic evaluation of a common imputation pipeline on empirical ancient samples, and show that imputed data can constitute a realistic option for population-genetic analyses. We also discuss preliminary results from a simulation study comparing two methods of phasing and imputation, which suggest that the parametric Li and Stephens framework may be more robust to extremely low levels of sparsity than the parsimonious Browning and Browning model.

    An evaluation of methods to handle missing data in the application of PCA for dimensionality reduction of genotype data is also presented. We illustrate that non-overlapping sequence data can lead to artifacts in projected scores, and evaluate different methods for handling unobserved genotypes.

    In genomics, as in other fields of research, increasing sizes of data sets are placing larger demands on efficient data management and compute infrastructures. The last part of this thesis addresses the use of cloud resources for facilitating such analysis. We present two different cloud-based solutions, and exemplify them on applications from genomics.

    List of papers
    1. An empirical evaluation of genotype imputation of ancient DNA
    Open this publication in new window or tab >>An empirical evaluation of genotype imputation of ancient DNA
    2022 (English)In: G3: Genes, Genomes, Genetics, E-ISSN 2160-1836, Vol. 12, no 6, article id jkac089Article in journal (Refereed) Published
    Abstract [en]

    With capabilities of sequencing ancient DNA to high coverage often limited by sample quality or cost, imputation of missing genotypes presents a possibility to increase the power of inference as well as cost-effectiveness for the analysis of ancient data. However, the high degree of uncertainty often associated with ancient DNA poses several methodological challenges, and performance of imputation methods in this context has not been fully explored. To gain further insights, we performed a systematic evaluation of imputation of ancient data using Beagle v4.0 and reference data from phase 3 of the 1000 Genomes project, investigating the effects of coverage, phased reference, and study sample size. Making use of five ancient individuals with high-coverage data available, we evaluated imputed data for accuracy, reference bias, and genetic affinities as captured by principal component analysis. We obtained genotype concordance levels of over 99% for data with 1× coverage, and similar levels of accuracy and reference bias at levels as low as 0.75×. Our findings suggest that using imputed data can be a realistic option for various population genetic analyses even for data in coverage ranges below 1×. We also show that a large and varied phased reference panel as well as the inclusion of low- to moderate-coverage ancient individuals in the study sample can increase imputation performance, particularly for rare alleles. In-depth analysis of imputed data with respect to genetic variants and allele frequencies gave further insight into the nature of errors arising during imputation, and can provide practical guidelines for postprocessing and validation prior to downstream analysis.

    Place, publisher, year, edition, pages
    Oxford University Press, 2022
    National Category
    Computational Mathematics Genetics
    Research subject
    Scientific Computing
    Identifiers
    urn:nbn:se:uu:diva-396336 (URN)10.1093/g3journal/jkac089 (DOI)000791204600001 ()35482488 (PubMedID)
    Projects
    eSSENCE
    Funder
    Swedish Research Council Formas, 2020-00712
    Available from: 2019-11-04 Created: 2019-11-04 Last updated: 2024-01-17Bibliographically approved
    2. Evaluation of methods handling missing data in PCA on genotype data: Applications for ancient DNA
    Open this publication in new window or tab >>Evaluation of methods handling missing data in PCA on genotype data: Applications for ancient DNA
    2019 (English)Report (Other academic)
    Series
    Technical report / Department of Information Technology, Uppsala University, ISSN 1404-3203 ; 2019-009
    National Category
    Computational Mathematics Genetics
    Identifiers
    urn:nbn:se:uu:diva-396346 (URN)
    Projects
    eSSENCE
    Available from: 2019-11-04 Created: 2019-11-04 Last updated: 2022-03-28Bibliographically approved
    3. BAMSI: a multi-cloud service for scalable distributed filtering of massive genome data
    Open this publication in new window or tab >>BAMSI: a multi-cloud service for scalable distributed filtering of massive genome data
    Show others...
    2018 (English)In: BMC Bioinformatics, E-ISSN 1471-2105, Vol. 19, p. 240:1-11, article id 240Article in journal (Refereed) Published
    National Category
    Software Engineering Genetics
    Identifiers
    urn:nbn:se:uu:diva-360033 (URN)10.1186/s12859-018-2241-z (DOI)000436517200001 ()29940842 (PubMedID)
    Projects
    eSSENCE
    Available from: 2018-06-26 Created: 2018-09-09 Last updated: 2024-01-17Bibliographically approved
    4. SWEEP: Accelerating scientific research through scalable serverless workflows
    Open this publication in new window or tab >>SWEEP: Accelerating scientific research through scalable serverless workflows
    Show others...
    2019 (English)In: Companion Proc. 12th International Conference on Utility and Cloud Computing, New York: ACM Press, 2019, p. 43-50Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    New York: ACM Press, 2019
    National Category
    Software Engineering
    Identifiers
    urn:nbn:se:uu:diva-396405 (URN)10.1145/3368235.3368839 (DOI)978-1-4503-7044-8 (ISBN)
    Conference
    UCC 2019
    Projects
    eSSENCE
    Available from: 2019-12-02 Created: 2019-11-04 Last updated: 2022-03-28Bibliographically approved
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  • 19.
    Ausmees, Kristiina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Evaluation of methods handling missing data in PCA on genotype data: Applications for ancient DNA2019Report (Other academic)
  • 20.
    Ausmees, Kristiina
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    John, Aji
    Toor, Salman Z.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Hellander, Andreas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Nettelblad, Carl
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    BAMSI: a multi-cloud service for scalable distributed filtering of massive genome data2018In: BMC Bioinformatics, E-ISSN 1471-2105, Vol. 19, p. 240:1-11, article id 240Article in journal (Refereed)
  • 21.
    Ausmees, Kristiina
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Nettelblad, Carl
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. Uppsala University, Science for Life Laboratory, SciLifeLab.
    A deep learning framework for characterization of genotype data2022In: G3: Genes, Genomes, Genetics, E-ISSN 2160-1836, Vol. 12, no 3, article id jkac020Article in journal (Refereed)
    Abstract [en]

    Dimensionality reduction is a data transformation technique widely used in various fields of genomics research. The application of dimensionality reduction to genotype data is known to capture genetic similarity between individuals, and is used for visualization of genetic variation, identification of population structure as well as ancestry mapping. Among frequently used methods are principal component analysis, which is a linear transform that often misses more fine-scale structures, and neighbor-graph based methods which focus on local relationships rather than large-scale patterns. Deep learning models are a type of nonlinear machine learning method in which the features used in data transformation are decided by the model in a data-driven manner, rather than by the researcher, and have been shown to present a promising alternative to traditional statistical methods for various applications in omics research. In this study, we propose a deep learning model based on a convolutional autoencoder architecture for dimensionality reduction of genotype data. Using a highly diverse cohort of human samples, we demonstrate that the model can identify population clusters and provide richer visual information in comparison to principal component analysis, while preserving global geometry to a higher extent than t-SNE and UMAP, yielding results that are comparable to an alternative deep learning approach based on variational autoencoders. We also discuss the use of the methodology for more general characterization of genotype data, showing that it preserves spatial properties in the form of decay of linkage disequilibrium with distance along the genome and demonstrating its use as a genetic clustering method, comparing results to the ADMIXTURE software frequently used in population genetic studies.

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  • 22.
    Ausmees, Kristiina
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Nettelblad, Carl
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Achieving improved accuracy for imputation of ancient DNA2023In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 39, no 1, article id btac738Article in journal (Refereed)
    Abstract [en]

    Motivation

    Genotype imputation has the potential to increase the amount of information that can be gained from the often limited biological material available in ancient samples. As many widely used tools have been developed with modern data in mind, their design is not necessarily reflective of the requirements in studies of ancient DNA. Here, we investigate if an imputation method based on the full probabilistic Li and Stephens model of haplotype frequencies might be beneficial for the particular challenges posed by ancient data.

    Results

    We present an implementation called prophaser and compare imputation performance to two alternative pipelines that have been used in the ancient DNA community based on the Beagle software. Considering empirical ancient data downsampled to lower coverages as well as present-day samples with artificially thinned genotypes, we show that the proposed method is advantageous at lower coverages, where it yields improved accuracy and ability to capture rare variation. The software prophaser is optimized for running in a massively parallel manner and achieved reasonable runtimes on the experiments performed when executed on a GPU.

    Download full text (pdf)
    fulltext
  • 23.
    Ausmees, Kristiina
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Sanchez-Quinto, Federico
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Human Evolution. Instituto Nacional de Medicina Genómica (INMEGEN) , Mexico City 14610, Mexico.
    Jakobsson, Mattias
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Human Evolution.
    Nettelblad, Carl
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    An empirical evaluation of genotype imputation of ancient DNA2022In: G3: Genes, Genomes, Genetics, E-ISSN 2160-1836, Vol. 12, no 6, article id jkac089Article in journal (Refereed)
    Abstract [en]

    With capabilities of sequencing ancient DNA to high coverage often limited by sample quality or cost, imputation of missing genotypes presents a possibility to increase the power of inference as well as cost-effectiveness for the analysis of ancient data. However, the high degree of uncertainty often associated with ancient DNA poses several methodological challenges, and performance of imputation methods in this context has not been fully explored. To gain further insights, we performed a systematic evaluation of imputation of ancient data using Beagle v4.0 and reference data from phase 3 of the 1000 Genomes project, investigating the effects of coverage, phased reference, and study sample size. Making use of five ancient individuals with high-coverage data available, we evaluated imputed data for accuracy, reference bias, and genetic affinities as captured by principal component analysis. We obtained genotype concordance levels of over 99% for data with 1× coverage, and similar levels of accuracy and reference bias at levels as low as 0.75×. Our findings suggest that using imputed data can be a realistic option for various population genetic analyses even for data in coverage ranges below 1×. We also show that a large and varied phased reference panel as well as the inclusion of low- to moderate-coverage ancient individuals in the study sample can increase imputation performance, particularly for rare alleles. In-depth analysis of imputed data with respect to genetic variants and allele frequencies gave further insight into the nature of errors arising during imputation, and can provide practical guidelines for postprocessing and validation prior to downstream analysis.

    Download full text (pdf)
    fulltext
  • 24. Awaysheh, F. M.
    et al.
    Aladwan, M. N.
    Alazab, M.
    Alawadi, Sadi
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Cabaleiro, J. C.
    Pena, T. F.
    Security by Design for Big Data Frameworks Over Cloud Computing2022In: IEEE transactions on engineering management, ISSN 0018-9391, E-ISSN 1558-0040, Vol. 69, no 6, p. 3676-3693Article in journal (Refereed)
    Abstract [en]

    Cloud deployment architectures have become a preferable computation model of Big Data (BD) operations. Their scalability, flexibility, and cost-effectiveness motivated this trend. In a such deployment model, the data are no longer physically maintained under the user’s direct control, which raises new security concerns. In this context, BD security plays a decisive role in the widespread adoption of cloud architectures. However, it is challenging to develop a comprehensive security plan unless it is based on a preliminary analysis that ensures a realistic secure assembly and addresses domain-specific vulnerabilities. This article presents a novel security-by-design framework for BD frameworks deployment over cloud computing (BigCloud). In particular, it relies on a systematic security analysis methodology and a completely automated security assessment framework. Our framework enables the mapping of BigCloud security domain knowledge to the best practices in the design phase. We validated the proposed framework by implementing an Apache Hadoop stack use case. The study findings demonstrate its effectiveness in improving awareness of security aspects and reducing security design time. It also evaluates the strengths and limitations of the proposed framework, from which it highlights the main existing and open challenges in the BigCloud-related area.

  • 25.
    Axelsson, Owe
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Dravins, Ivo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis. Faculty of Mathematics, Ruhr UniversityBochum, Bochum, Germany.
    Neytcheva, Maya
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Stage-parallel preconditioners for implicit Runge-Kutta methods of arbitrarily high order, linear problems2023In: Numerical Linear Algebra with Applications, ISSN 1070-5325, E-ISSN 1099-1506, article id e2532Article in journal (Refereed)
    Abstract [en]

    Fully implicit Runge–Kutta methods offer the possibility to use high order accurate time discretization to match space discretization accuracy, an issue of significant importance for many large scale problems of current interest, where we may have fine space resolution with many millions of spatial degrees of freedom and long time intervals. In this work, we consider strongly A-stable implicit Runge–Kutta methods of arbitrary order of accuracy, based on Radau quadratures. For the arising large algebraic systems we introduce efficient preconditioners, that (1) use only real arithmetic, (2) demonstrate robustness with respect to problem and discretization parameters, and (3) allow for fully stage-parallel solution. The preconditioners are based on the observation that the lower-triangular part of the coefficient matrices in the Butcher tableau has larger in magnitude values, compared to the corresponding strictly upper-triangular part. We analyze the spectrum of the corresponding preconditioned systems and illustrate their performance with numerical experiments. Even though the observation has been made some time ago, its impact on constructing stage-parallel preconditioners has not yet been done and its systematic study constitutes the novelty of this article.

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  • 26.
    Axelsson, Owe
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. The Czech Academy of Sciences, Institute of Geonics, Ostrava, Czech Republic.
    Neytcheva, Maya
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
    Numerical solution methods for implicit Runge-Kutta methods of arbitrarily high order2020In: Proceedings of the conference Algoritmy 2020 / [ed] Peter Frolkovič, Karol Mikula, Daniel Ševčovič, 2020, Vol. 7, p. 11-20Conference paper (Refereed)
    Abstract [en]

    In this study we consider an efficient implementation of Implicit Runge-Kutta methods for solving large systems of ordinary differential equations that originate from finite element discretization of the heat and similar equations, to be solved on large time intervals. The main contribution of this work is to show how to implement a fully stage-parallel version of the method, utilizing the dominance of the block lower triangular part of the quadrature matrix, and to illustrate it numerically. Its usage for the solution of algebraic-differential equations is also touched.

  • 27.
    Axelsson, Owe
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
    Neytcheva, Maya
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
    Karátson, János
    ELTE University.
    Preconditioned iterative solution methods for linear systems arising in PDE-constrained optimization2019In: Robust and Constrained Optimization: Methods and Applications / [ed] Dewey Clark, Hauppauge, New York: Nova Science Publishers, Inc., 2019, p. 85-148Chapter in book (Refereed)
    Abstract [en]

    This chapter is devoted to the preconditioned iterative solution of a particular type of linear systems, mainly involving matrices of a two-by-two block form with square matrix blocks. Such systems arise in the finite element (FE) solution of optimal control problems for partial differential equations (PDEs) in various applications. Owing to the large scale of the problems, they must be solved by an iterative method. The choice of proper preconditioners is then crucial. We propose several types of preconditioning methods and make an analytical comparison of them, leading to estimates of linear convergence. We also discuss superlinear convergence estimates of such preconditioning methods and mesh-independence.

  • 28.
    Bai, Zhong-Zhi
    et al.
    Chinese Acad Sci, Acad Math & Syst Sci, Inst Computat Math & Sci Engn Comp, State Key Lab Sci Engn Comp, POB 2719, Beijing 100190, Peoples R China..
    Neytcheva, Maya
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
    Reichel, Lothar
    Kent State Univ, Dept Math Sci, Kent, OH 44242 USA..
    Editorial: Novel methods and theories in numerical algebra with interdisciplinary applications2018In: Numerical Linear Algebra with Applications, ISSN 1070-5325, E-ISSN 1099-1506, Vol. 25, no 4, article id e2181Article in journal (Other academic)
  • 29.
    Bauer, Pavol
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Parallelism and efficiency in discrete-event simulation2015Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Discrete-event models depict systems where a discrete state is repeatedly altered by instantaneous changes in time, the events of the model. Such models have gained popularity in fields such as Computational Systems Biology or Computational Epidemiology due to the high modeling flexibility and the possibility to easily combine stochastic and deterministic dynamics. However, the system size of modern discrete-event models is growing and/or they need to be simulated at long time periods. Thus, efficient simulation algorithms are required, as well as the possibility to harness the compute potential of modern multicore computers. Due to the sequential design of simulators, parallelization of discrete event simulations is not trivial. This thesis discusses event-based modeling and sensitivity analysis and also examines ways to increase the efficiency of discrete-event simulations and to scale models involving deterministic and stochastic spatial dynamics on a large number of processor cores.

    List of papers
    1. Sensitivity estimation and inverse problems in spatial stochastic models of chemical kinetics
    Open this publication in new window or tab >>Sensitivity estimation and inverse problems in spatial stochastic models of chemical kinetics
    2015 (English)In: Numerical Mathematics and Advanced Applications: ENUMATH 2013, Springer, 2015, p. 519-527Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    Springer, 2015
    Series
    Lecture Notes in Computational Science and Engineering ; 103
    National Category
    Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-237184 (URN)10.1007/978-3-319-10705-9_51 (DOI)978-3-319-10704-2 (ISBN)
    Conference
    ENUMATH 2013
    Projects
    eSSENCEUPMARC
    Available from: 2014-10-31 Created: 2014-11-28 Last updated: 2018-11-12Bibliographically approved
    2. Fast event-based epidemiological simulations on national scales
    Open this publication in new window or tab >>Fast event-based epidemiological simulations on national scales
    2016 (English)In: The international journal of high performance computing applications, ISSN 1094-3420, E-ISSN 1741-2846, Vol. 30, p. 438-453Article in journal (Refereed) Published
    National Category
    Computer Sciences Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-264751 (URN)10.1177/1094342016635723 (DOI)000387763100005 ()
    Projects
    UPMARCeSSENCE
    Available from: 2016-04-11 Created: 2015-10-16 Last updated: 2018-11-12Bibliographically approved
    3. Efficient inter-process synchronization for parallel discrete event simulation on multicores
    Open this publication in new window or tab >>Efficient inter-process synchronization for parallel discrete event simulation on multicores
    2015 (English)In: Proc. 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, New York: ACM Press, 2015, p. 183-194Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    New York: ACM Press, 2015
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-260199 (URN)10.1145/2769458.2769476 (DOI)978-1-4503-3583-6 (ISBN)
    Conference
    SIGSIM-PADS 2015
    Projects
    UPMARC
    Available from: 2015-06-10 Created: 2015-08-17 Last updated: 2018-11-12Bibliographically approved
    Download full text (pdf)
    fulltext
  • 30.
    Bauer, Pavol
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Parallelism in Event-Based Computations with Applications in Biology2017Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Event-based models find frequent usage in fields such as computational physics and biology as they may contain both continuous and discrete state variables and may incorporate both deterministic and stochastic state transitions. If the state transitions are stochastic, computer-generated random numbers are used to obtain the model solution. This type of event-based computations is also known as Monte-Carlo simulation.

    In this thesis, I study different approaches to execute event-based computations on parallel computers. This ultimately allows users to retrieve their simulation results in a fraction of the original computation time. As system sizes grow continuously or models have to be simulated at longer time scales, this is a necessary approach for current computational tasks.

    More specifically, I propose several ways to asynchronously simulate such models on parallel shared-memory computers, for example using parallel discrete-event simulation or task-based computing. The particular event-based models studied herein find applications in systems biology, computational epidemiology and computational neuroscience.

    In the presented studies, the proposed methods allow for high efficiency of the parallel simulation, typically scaling well with the number of used computer cores. As the scaling typically depends on individual model properties, the studies also investigate which quantities have the greatest impact on the simulation performance.

    Finally, the presented studies include other insights into event-based computations, such as methods how to estimate parameter sensitivity in stochastic models and how to simulate models that include both deterministic and stochastic state transitions.

    List of papers
    1. Sensitivity estimation and inverse problems in spatial stochastic models of chemical kinetics
    Open this publication in new window or tab >>Sensitivity estimation and inverse problems in spatial stochastic models of chemical kinetics
    2015 (English)In: Numerical Mathematics and Advanced Applications: ENUMATH 2013, Springer, 2015, p. 519-527Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    Springer, 2015
    Series
    Lecture Notes in Computational Science and Engineering ; 103
    National Category
    Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-237184 (URN)10.1007/978-3-319-10705-9_51 (DOI)978-3-319-10704-2 (ISBN)
    Conference
    ENUMATH 2013
    Projects
    eSSENCEUPMARC
    Available from: 2014-10-31 Created: 2014-11-28 Last updated: 2018-11-12Bibliographically approved
    2. Multiscale modelling via split-step methods in neural firing
    Open this publication in new window or tab >>Multiscale modelling via split-step methods in neural firing
    2018 (English)In: Mathematical and Computer Modelling of Dynamical Systems, ISSN 1387-3954, E-ISSN 1744-5051, Vol. 24, p. 426-445Article in journal (Refereed) Published
    National Category
    Computational Mathematics Neurosciences
    Identifiers
    urn:nbn:se:uu:diva-332008 (URN)10.1080/13873954.2018.1488740 (DOI)000440605300005 ()
    Projects
    UPMARCeSSENCE
    Available from: 2018-08-01 Created: 2017-10-22 Last updated: 2018-11-19Bibliographically approved
    3. Fast event-based epidemiological simulations on national scales
    Open this publication in new window or tab >>Fast event-based epidemiological simulations on national scales
    2016 (English)In: The international journal of high performance computing applications, ISSN 1094-3420, E-ISSN 1741-2846, Vol. 30, p. 438-453Article in journal (Refereed) Published
    National Category
    Computer Sciences Computational Mathematics
    Identifiers
    urn:nbn:se:uu:diva-264751 (URN)10.1177/1094342016635723 (DOI)000387763100005 ()
    Projects
    UPMARCeSSENCE
    Available from: 2016-04-11 Created: 2015-10-16 Last updated: 2018-11-12Bibliographically approved
    4. Efficient inter-process synchronization for parallel discrete event simulation on multicores
    Open this publication in new window or tab >>Efficient inter-process synchronization for parallel discrete event simulation on multicores
    2015 (English)In: Proc. 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, New York: ACM Press, 2015, p. 183-194Conference paper, Published paper (Refereed)
    Place, publisher, year, edition, pages
    New York: ACM Press, 2015
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-260199 (URN)10.1145/2769458.2769476 (DOI)978-1-4503-3583-6 (ISBN)
    Conference
    SIGSIM-PADS 2015
    Projects
    UPMARC
    Available from: 2015-06-10 Created: 2015-08-17 Last updated: 2018-11-12Bibliographically approved
    5. Exposing inter-process information for efficient parallel discrete event simulation of spatial stochastic systems
    Open this publication in new window or tab >>Exposing inter-process information for efficient parallel discrete event simulation of spatial stochastic systems
    2017 (English)In: Proc. 5th ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, New York: ACM Press, 2017, p. 53-64Conference paper, Published paper (Refereed)
    Abstract [en]

    We present a new efficient approach to the parallelization of discrete event simulators for multicore computers, which is based on exposing and disseminating essential information between processors. We aim specifically at simulation models with a spatial structure, where time intervals between successive events are highly variable and without lower bounds. In Parallel Discrete Event Simulation (PDES), the model is distributed onto parallel processes. A key challenge in PDES is that each process must continuously decide when to pause its local simulation in order to reduce the risk of expensive rollbacks caused by future "delayed"' incoming events from other processes. A process could make such decisions optimally if it would know the timestamps of future incoming events. Unfortunately, this information is often not available in PDES algorithms. We present an approach to designing efficient PDES algorithms, in which an existing natural parallelization of PDES is restructured in order to expose and disseminate more precise information about future incoming events to each LP. We have implemented our approach in a parallel simulator for spatially extended Markovian processes, intended for simulating, e.g., chemical reactions, biological and epidemiological processes. On 32 cores, our implementation exhibits speedup that significantly outweighs the overhead incurred by the refinement. We also show that our resulting simulator is superior in performance to existing simulators for comparable models, achieving for 32 cores an average speedup of 20 relative to an efficient sequential implementation.

    Place, publisher, year, edition, pages
    New York: ACM Press, 2017
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:uu:diva-328367 (URN)10.1145/3064911.3064916 (DOI)000631675200007 ()978-1-4503-4489-0 (ISBN)
    Conference
    SIGSIM-PADS 2017
    Projects
    UPMARC
    Available from: 2017-05-16 Created: 2017-08-22 Last updated: 2024-01-23Bibliographically approved
    Download full text (pdf)
    fulltext
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  • 31.
    Bauer, Pavol
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Engblom, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Sensitivity estimation and inverse problems in spatial stochastic models of chemical kinetics2015In: Numerical Mathematics and Advanced Applications: ENUMATH 2013, Springer, 2015, p. 519-527Conference paper (Refereed)
  • 32.
    Bauer, Pavol
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Engblom, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Mikulovic, Sanja
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Neuroscience, Developmental Genetics.
    Senek, Aleksandar
    Multiscale modelling via split-step methods in neural firing2018In: Mathematical and Computer Modelling of Dynamical Systems, ISSN 1387-3954, E-ISSN 1744-5051, Vol. 24, p. 426-445Article in journal (Refereed)
  • 33.
    Bauer, Pavol
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Engblom, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Widgren, Stefan
    Fast event-based epidemiological simulations on national scales2016In: The international journal of high performance computing applications, ISSN 1094-3420, E-ISSN 1741-2846, Vol. 30, p. 438-453Article in journal (Refereed)
  • 34.
    Bauer, Pavol
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Lindén, Jonatan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Engblom, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Jonsson, Bengt
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Efficient inter-process synchronization for parallel discrete event simulation on multicores2015In: Proc. 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, New York: ACM Press, 2015, p. 183-194Conference paper (Refereed)
  • 35. Benchaib, Mohamed Amine
    et al.
    Bouchnita, Anass
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Volpert, Vitaly
    Makhoute, Abdelkader
    Mathematical modeling reveals that the administration of EGF can promote the elimination of lymph node metastases by PD-1/PD-L1 blockade2019In: Frontiers in Bioengineering and Biotechnology, E-ISSN 2296-4185, Vol. 7, article id 104Article in journal (Refereed)
  • 36.
    Berglund, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Eckerdal, Anna
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Learning practice and theory in programming education: Students’ lived experience2015In: Proc. 3rd International Conference on Learning and Teaching in Computing and Engineering, Los Alamitos, CA: IEEE Computer Society, 2015, p. 180-186Conference paper (Refereed)
    Download full text (pdf)
    fulltext
  • 37.
    Berglund, Anders
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Eckerdal, Anna
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Learning to program: A discussion on the interplay of theory and practice2015In: Proc. 1st Al Baha University and Uppsala University Symposium on Quality in Computing Education, 2015, p. 16-18Conference paper (Refereed)
  • 38.
    Blamey, Ben
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Hellander, Andreas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Toor, Salman
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Apache Spark Streaming, Kafka and HarmonicIO: A performance benchmark and architecture comparison for enterprise and scientific computing2020In: Benchmarking, Measuring, and Optimizing, Springer, 2020, p. 335-347Conference paper (Refereed)
  • 39.
    Blamey, Ben
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Toor, Salman
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
    Dahlö, Martin
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wieslander, Håkan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Harrison, Philip J.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Sintorn, Ida-Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Vironova AB.
    Sabirsh, Alan
    AstraZeneca.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Hellander, Andreas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Rapid development of cloud-native intelligent data pipelines for scientific data streams using the HASTE Toolkit2021In: GigaScience, E-ISSN 2047-217X, Vol. 10, no 3, p. 1-14, article id giab018Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Large streamed datasets, characteristic of life science applications, are often resource-intensive to process, transport and store. We propose a pipeline model, a design pattern for scientific pipelines, where an incoming stream of scientific data is organized into a tiered or ordered "data hierarchy". We introduce the HASTE Toolkit, a proof-of-concept cloud-native software toolkit based on this pipeline model, to partition and prioritize data streams to optimize use of limited computing resources.

    FINDINGS: In our pipeline model, an "interestingness function" assigns an interestingness score to data objects in the stream, inducing a data hierarchy. From this score, a "policy" guides decisions on how to prioritize computational resource use for a given object. The HASTE Toolkit is a collection of tools to adopt this approach. We evaluate with 2 microscopy imaging case studies. The first is a high content screening experiment, where images are analyzed in an on-premise container cloud to prioritize storage and subsequent computation. The second considers edge processing of images for upload into the public cloud for real-time control of a transmission electron microscope.

    CONCLUSIONS: Through our evaluation, we created smart data pipelines capable of effective use of storage, compute, and network resources, enabling more efficient data-intensive experiments. We note a beneficial separation between scientific concerns of data priority, and the implementation of this behaviour for different resources in different deployment contexts. The toolkit allows intelligent prioritization to be `bolted on' to new and existing systems - and is intended for use with a range of technologies in different deployment scenarios.

    Download full text (pdf)
    fulltext
  • 40.
    Blamey, Ben
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Wrede, Fredrik
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Karlsson, Johan
    Hellander, Andreas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Toor, Salman
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Adapting the secretary hiring problem for optimal hot–cold tier placement under top-K workloads2019In: Proc. 19th International Symposium on Cluster, Cloud, and Grid Computing, Los Alamitos, CA: IEEE Computer Society, 2019, p. 576-583Conference paper (Refereed)
  • 41.
    Blanc, Emilie
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Engblom, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Hellander, Andreas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Lötstedt, Per
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis.
    Mesoscopic modeling of stochastic reaction–diffusion kinetics in the subdiffusive regime2016In: Multiscale Modeling & simulation, ISSN 1540-3459, E-ISSN 1540-3467, Vol. 14, p. 668-707Article in journal (Refereed)
  • 42.
    Bonito, Andrea
    et al.
    Texas A&M Univ, Dept Math, College Stn, TX 77843 USA..
    Nazarov, Murtazo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Numerical Simulations of Surface Quasi-Geostrophic Flows on Periodic Domains2021In: SIAM Journal on Scientific Computing, ISSN 1064-8275, E-ISSN 1095-7197, Vol. 43, no 2, p. B405-B430Article in journal (Refereed)
    Abstract [en]

    We propose a novel algorithm for the approximation of surface quasi-geostrophic (SQG) flows modeled by a nonlinear partial differential equation coupling transport and fractional diffusion phenomena. The time discretization consists of an explicit strong-stability-preserving three-stage Runge-Kutta method while a flux-corrected-transport (FCT) method coupled with Dunford-Taylor representations of fractional operators is advocated for the space discretization. Standard continuous piecewise linear finite elements are employed, and the algorithm does not have restrictions on the mesh structure or on the computational domain. In the inviscid case, we show that the resulting scheme satisfies a discrete maximum principle property under a standard CFL condition and observe, in practice, its second order accuracy in space. The algorithm successfully approximates several benchmarks with sharp transitions and fine structures typical of SQG flows. In addition, theoretical Kolmogorov energy decay rates are observed on a freely decaying atmospheric turbulence simulation.

  • 43.
    Borcea, Liliana
    et al.
    University of Michigan, Department of Mathematics, Ann Arbor, Michigan, USA...
    Garnier, Josselin
    Institut Polytechnique de Paris, Centre de Mathématiques Appliquées, Ecole Polytechnique, Palaiseau, France...
    Mamonov, Alexander V.
    University of Houston, Department of Mathematics, Houston, Texas, USA...
    Zimmerling, Jörn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. University of Michigan, Department of Mathematics, Ann Arbor, Michigan, USA.
    Waveform inversion via reduced order modeling2023In: Geophysics, ISSN 0016-8033, E-ISSN 1942-2156, Vol. 88, no 2, p. R175-R191Article in journal (Refereed)
    Abstract [en]

    We introduce a novel approach to waveform inversion, based on a data driven reduced order model (ROM) of the wave operator. The presentation is for the acous- tic wave equation, but the approach can be extended to elastic or electromagnetic waves. The data are time resolved measurements of the pressure wave gathered by an acquisition system which probes the unknown medium with pulses and measures the generated waves. We propose to solve the inverse problem of velocity es- timation by minimizing the square misfit between the ROM computed from the recorded data and the ROM computed from the modeled data, at the current guess of the velocity. We give the step by step computation of the ROM, which depends nonlinearly on the data and yet can be obtained from them in a non-iterative fash- ion, using efficient methods from linear algebra. We also explain how to make the ROM robust to data inac- curacy. The ROM computation requires the full array response matrix gathered with colocated sources and receivers. However, we show that the computation can deal with an approximation of this matrix, obtained from towed-streamer data using interpolation and reci- procity on-the-fly.

    While the full-waveform inversion approach of nonlin- ear least-squares data fitting is challenging without low frequency information, due to multiple minima of the data fit objective function, we show that the ROM mis- fit objective function has a better behavior, even for a poor initial guess. We also show by an explicit com- putation of the objective functions in a simple setting that the ROM misfit objective function has convexity properties, whereas the least squares data fit objective function displays multiple local minima.

  • 44.
    Bouchnita, Anass
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Hellander, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Hellander, Andreas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    A 3D multiscale model to explore the role of EGFR overexpression in tumourigenesis2019In: Bulletin of Mathematical Biology, ISSN 0092-8240, E-ISSN 1522-9602, Vol. 81, p. 2323-2344Article in journal (Refereed)
  • 45.
    Bouchnita, Anass
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Volpert, Vitaly
    A multiscale model of platelet-fibrin thrombus growth in the flow2019In: Computers & Fluids, ISSN 0045-7930, E-ISSN 1879-0747, Vol. 184, p. 10-20Article in journal (Refereed)
  • 46.
    Bouchnita, Anass
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Volpert, Vitaly
    Koury, Mark J.
    Hellander, Andreas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    A multiscale model to design therapeutic strategies that overcome drug resistance to tyrosine kinase inhibitors in multiple myeloma2020In: Mathematical Biosciences, ISSN 0025-5564, E-ISSN 1879-3134, Vol. 319, article id 108293Article in journal (Refereed)
  • 47.
    Bouderlique, Thibault
    et al.
    Med Univ Vienna, Ctr Brain Res, Dept Neuroimmunol, A-1090 Vienna, Austria..
    Petersen, Julian
    Med Univ Vienna, Ctr Brain Res, Dept Neuroimmunol, A-1090 Vienna, Austria.;Univ Leipzig, Med Ctr, Dept Orthodont, Leipzig, Germany..
    Faure, Louis
    Med Univ Vienna, Ctr Brain Res, Dept Neuroimmunol, A-1090 Vienna, Austria..
    Abed-Navandi, Daniel
    Haus Meeres, A-1060 Vienna, Austria..
    Bouchnita, Anass
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Univ Texas Austin, Dept Integrat Biol, Austin, TX 78712 USA..
    Mueller, Benjamin
    Univ Amsterdam, Dept Freshwater & Marine Ecol, NL-1090 GE Amsterdam, Netherlands.;CARMABI Fdn, Willemstad, Curacao..
    Nazarov, Murtazo
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Englmaier, Lukas
    Med Univ Vienna, Ctr Brain Res, Dept Neuroimmunol, A-1090 Vienna, Austria..
    Tesarova, Marketa
    Brno Univ Technol, Cent European Inst Technol, Brno, Czech Republic..
    Frade, Pedro R.
    Nat Hist Museum Vienna, A-1010 Vienna, Austria..
    Zikmund, Tomas
    Brno Univ Technol, Cent European Inst Technol, Brno, Czech Republic..
    Koehne, Till
    Univ Leipzig, Med Ctr, Dept Orthodont, Leipzig, Germany..
    Kaiser, Jozef
    Brno Univ Technol, Cent European Inst Technol, Brno, Czech Republic..
    Fried, Kaj
    Karolinska Inst, Dept Neurosci, S-17177 Stockholm, Sweden..
    Wild, Christian
    Fac Biol & Chem Bremen, Dept Marine Ecol, D-28359 Bremen, Germany..
    Pantos, Olga
    Inst Environm Sci & Res, 27 Creyke Rd, Christchurch 8041, New Zealand..
    Hellander, Andreas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Bythell, John
    Newcastle Univ, Sch Nat & Environm Sci, Newcastle Upon Tyne NE1 7RU, Tyne & Wear, England..
    Adameyko, Igor
    Med Univ Vienna, Ctr Brain Res, Dept Neuroimmunol, A-1090 Vienna, Austria.;Karolinska Inst, Dept Physiol & Pharmacol, S-17177 Stockholm, Sweden..
    Surface flow for colonial integration in reef-building corals2022In: Current Biology, ISSN 0960-9822, E-ISSN 1879-0445, Vol. 32, no 12, p. 2596-2609Article in journal (Refereed)
    Abstract [en]

    Reef-building corals are endangered animals with a complex colonial organization. Physiological mechanisms connecting multiple polyps and integrating them into a coral colony are still enigmatic. Using live imaging, particle tracking, and mathematical modeling, we reveal how corals connect individual polyps and form integrated polyp groups via species-specific, complex, and stable networks of currents at their surface. These currents involve surface mucus of different concentrations, which regulate joint feeding of the colony. Inside the coral, within the gastrovascular system, we expose the complexity of bidirectional branching streams that connect individual polyps. This system of canals extends the surface area by 4-fold and might improve communication, nutrient supply, and symbiont transfer. Thus, individual polyps integrate via complex liquid dynamics on the surface and inside the colony.

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  • 48. Boustedt, Jonas
    et al.
    Eckerdal, Anna
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    McCartney, Robert
    Sanders, Kate
    Thomas, Lynda
    Zander, Carol
    Students' perceptions of the differences between formal and informal learning2011In: Proc. 7th International Computing Education Research Workshop, New York: ACM Press , 2011, p. 61-68Conference paper (Refereed)
  • 49.
    Bronstein, Samuel
    et al.
    Department of Mathematics and Applications, ENS Paris.
    Engblom, Stefan
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Numerical Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
    Marin, Robin
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Bayesian inference in epidemics: linear noise analysis2023In: Mathematical Biosciences and Engineering, ISSN 1547-1063, E-ISSN 1551-0018, Vol. 20, no 2, p. 4128-4152Article in journal (Refereed)
    Abstract [en]

    This paper offers a qualitative insight into the convergence of bayesian parameter inference in a setup which mimics the modeling of the spread of a disease with associated disease measurements. Specifically, we are interested in the Bayesian model’s convergence with increasing amounts of data under measurement limitations. Depending on how weakly informative the disease measurements are, we offer a kind of ‘best case’ as well as a ‘worst case’ analysis where, in the former case, we assume that the prevalence is directly accessible, while in the latter that only a binary signal corresponding toa prevalence detection threshold is available. Both cases are studied under an assumed so-called linear noise approximation as to the true dynamics. Numerical experiments test the sharpness of our results when confronted with more realistic situations for which analytical results are unavailable.

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    fulltext
  • 50. Brumm, Bernd
    et al.
    Kieri, Emil
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    A matrix-free Legendre spectral method for initial–boundary value problems2016In: Electronic Transactions on Numerical Analysis, E-ISSN 1068-9613, Vol. 45, p. 283-304Article in journal (Refereed)
1234567 1 - 50 of 456
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