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  • 1. Aartsen, M. G.
    et al.
    Abbasi, R.
    Ackermann, M.
    Adams, J.
    Aguilar, J. A.
    Ahlers, M.
    Altmann, D.
    Arguelles, C.
    Auffenberg, J.
    Bai, X.
    Baker, M.
    Barwick, S. W.
    Baum, V.
    Bay, R.
    Beatty, J. J.
    Tjus, J. Becker
    Becker, K. -H
    BenZvi, S.
    Berghaus, P.
    Berley, D.
    Bernardini, E.
    Bernhard, A.
    Besson, D. Z.
    Binder, G.
    Bindig, D.
    Bissok, M.
    Blaufuss, E.
    Blumenthal, J.
    Boersma, David J.
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, High Energy Physics.
    Bohm, C.
    Bose, D.
    Boeser, S.
    Botner, Olga
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, High Energy Physics.
    Brayeur, L.
    Bretz, H. -P
    Brown, A. M.
    Bruijn, R.
    Casey, J.
    Casier, M.
    Chirkin, D.
    Christov, A.
    Christy, B.
    Clark, K.
    Classen, L.
    Clevermann, F.
    Coenders, S.
    Cohen, S.
    Cowen, D. F.
    Silva, A. H. Cruz
    Danninger, M.
    Daughhetee, J.
    Davis, J. C.
    Day, M.
    De Clercq, C.
    De Ridder, S.
    Desiati, P.
    de Vries, K. D.
    de With, M.
    DeYoung, T.
    Diaz-Velez, J. C.
    Dunkman, M.
    Eagan, R.
    Eberhardt, B.
    Eichmann, B.
    Eisch, J.
    Euler, S.
    Evenson, P. A.
    Fadiran, O.
    Fazely, A. R.
    Fedynitch, A.
    Feintzeig, J.
    Feusels, T.
    Filimonov, K.
    Finley, C.
    Fischer-Wasels, T.
    Flis, S.
    Franckowiak, A.
    Frantzen, K.
    Fuchs, T.
    Gaisser, T. K.
    Gallagher, J.
    Gerhardt, L.
    Gladstone, L.
    Glusenkamp, T.
    Goldschmidt, A.
    Golup, G.
    Gonzalez, J. G.
    Goodman, J. A.
    Gora, D.
    Grandmont, D. T.
    Grant, D.
    Gretskov, P.
    Groh, J. C.
    Gross, A.
    Ha, C.
    Ismail, A. Haj
    Hallen, P.
    Hallgren, Allan
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, High Energy Physics.
    Halzen, F.
    Hanson, K.
    Hebecker, D.
    Heereman, D.
    Heinen, D.
    Helbing, K.
    Hellauer, R.
    Hickford, S.
    Hill, G. C.
    Hoffman, K. D.
    Hoffmann, R.
    Homeier, A.
    Hoshina, K.
    Huang, F.
    Huelsnitz, W.
    Hulth, P. O.
    Hultqvist, K.
    Hussain, S.
    Ishihara, A.
    Jacobi, E.
    Jacobsen, J.
    Jagielski, K.
    Japaridze, G. S.
    Jero, K.
    Jlelati, O.
    Kaminsky, B.
    Kappes, A.
    Karg, T.
    Karle, A.
    Kauer, M.
    Kelley, J. L.
    Kiryluk, J.
    Klaes, J.
    Klein, S. R.
    Koehne, J. -H
    Kohnen, G.
    Kolanoski, H.
    Koepke, L.
    Kopper, C.
    Kopper, S.
    Koskinen, D. J.
    Kowalski, M.
    Krasberg, M.
    Kriesten, A.
    Krings, K.
    Kroll, G.
    Kunnen, J.
    Kurahashi, N.
    Kuwabara, T.
    Labare, M.
    Landsman, H.
    Larson, M. J.
    Lesiak-Bzdak, M.
    Leuermann, M.
    Leute, J.
    Luenemann, J.
    Macias, O.
    Madsen, J.
    Maggi, G.
    Maruyama, R.
    Mase, K.
    Matis, H. S.
    McNally, F.
    Meagher, K.
    Merck, M.
    Merino, G.
    Meures, T.
    Miarecki, S.
    Middell, E.
    Milke, N.
    Miller, J.
    Mohrmann, L.
    Montaruli, T.
    Morse, R.
    Nahnhauer, R.
    Naumann, U.
    Niederhausen, H.
    Nowicki, S. C.
    Nygren, D. R.
    Obertacke, A.
    Odrowski, S.
    Olivas, A.
    Omairat, A.
    O'Murchadha, A.
    Paul, L.
    Pepper, J. A.
    de los Heros, Carlos Perez
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, High Energy Physics.
    Pfendner, C.
    Pieloth, D.
    Pinat, E.
    Posselt, J.
    Price, P. B.
    Przybylski, G. T.
    Quinnan, M.
    Raedel, L.
    Rae, I.
    Rameez, M.
    Rawlins, K.
    Redl, P.
    Reimann, R.
    Resconi, E.
    Rhode, W.
    Ribordy, M.
    Richman, M.
    Riedel, B.
    Rodrigues, J. P.
    Rott, C.
    Ruhe, T.
    Ruzybayev, B.
    Ryckbosch, D.
    Saba, S. M.
    Sander, H. -G
    Santander, M.
    Sarkar, S.
    Schatto, K.
    Scheriau, F.
    Schmidt, T.
    Schmitz, M.
    Schoenen, S.
    Schoeneberg, S.
    Schoenwald, A.
    Schukraft, A.
    Schulte, L.
    Schultz, D.
    Schulz, O.
    Secke, D.
    Sestayo, Y.
    Seunarine, S.
    Shanidze, R.
    Sheremata, C.
    Smith, M. W. E.
    Soldin, D.
    Spiczak, G. M.
    Spiering, C.
    Stamatikos, M.
    Stanev, T.
    Stanisha, N. A.
    Stasik, A.
    Stezelberger, T.
    Stokstad, R. G.
    Stoessl, A.
    Strahler, E. A.
    Ström, Rickard
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, High Energy Physics.
    Strotjohann, N. L.
    Sullivan, G. W.
    Taavola, Henric
    Uppsala University, Disciplinary Domain of Science and Technology, Physics, Department of Physics and Astronomy, High Energy Physics.
    Taboada, I.
    Tamburro, A.
    Tepe, A.
    Ter-Antonyan, S.
    Tesic, G.
    Tilav, S.
    Toale, P. A.
    Tobin, M. N.
    Toscano, S.
    Tselengidou, M.
    Unger, E.
    Usner, M.
    Vallecorsa, S.
    van Eijndhoven, N.
    van Overloop, A.
    van Santen, J.
    Vehring, M.
    Voge, M.
    Vraeghe, M.
    Walck, C.
    Waldenmaier, T.
    Wallraff, M.
    Weaver, Ch.
    Wellons, M.
    Wendt, C.
    Westerhoff, S.
    Whitehorn, N.
    Wiebe, K.
    Wiebusch, C. H.
    Williams, D. R.
    Wissing, H.
    Wolf, M.
    Wood, T. R.
    Woschnagg, K.
    Xu, D. L.
    Xu, X. W.
    Yanez, J. P.
    Yodh, G.
    Yoshida, S.
    Zarzhitsky, P.
    Ziemann, J.
    Zierke, S.
    Zoll, M.
    The IceProd framework: Distributed data processing for the IceCube neutrino observatory2015In: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 75, p. 198-211Article in journal (Refereed)
    Abstract [en]

    IceCube is a one-gigaton instrument located at the geographic South Pole, designed to detect cosmic neutrinos, identify the particle nature of dark matter, and study high-energy neutrinos themselves. Simulation of the IceCube detector and processing of data require a significant amount of computational resources. This paper presents the first detailed description of IceProd, a lightweight distributed management system designed to meet these requirements. It is driven by a central database in order to manage mass production of simulations and analysis of data produced by the IceCube detector. IceProd runs as a separate layer on top of other middleware and can take advantage of a variety of computing resources, including grids and batch systems such as CREAM, HTCondor, and PBS. This is accomplished by a set of dedicated daemons that process job submission in a coordinated fashion through the use of middleware plugins that serve to abstract the details of job submission and job management from the framework. (C) 2014 Elsevier Inc. All rights reserved.

  • 2.
    Lampka, Kai
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems.
    Forsberg, Björn
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Spiliopoulos, Vasileios
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Architecture and Computer Communication.
    Keep it cool and in time: With runtime monitoring to thermal-aware execution speeds for deadline constrained systems2016In: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 95, p. 79-91Article in journal (Refereed)
    Abstract [en]

    The Dynamic Power and Thermal Management (DPTM) system of Dynamic Voltage Frequency Scaling (DVFS) enabled processors compensates peak temperatures by slowing or even powering parts of the system down. While ensuring the integrity of computations, this comes with the drawback of losing performance. In the context of hard real-time systems, such unpredictable losses in performance are unacceptable, as they may lead to deadline misses which may yet compromise the integrity of the system. To safely execute hard real-time workloads on such systems, this article presents an online scheme for assigning speeds in such a way that (a) the system executes at low clock speed as often as possible, while (b) deadline violations are strictly ruled out. The proposed scheme is compared with an offline scheme which has complete knowledge about arrival times and execution demands of the workload. The benchmarking shows that for a workload which is always very close to the modelled maximum, our approach performs on-par with the offline scheme. In case of a workload which diverges from the modelled maximum more often, the speed assignments produced by our scheme become more pessimistic, as to ensure that all deadlines are met.

  • 3.
    Sagonas, Konstantinos
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. Natl Tech Univ Athens, Sch Elect & Comp Engn, Athens, Greece..
    Winblad, Kjell
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science.
    A contention adapting approach to concurrent ordered sets2018In: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 115, p. 1-19Article in journal (Refereed)
    Abstract [en]

    With multicores being ubiquitous, concurrent data structures are increasingly important. This article proposes a novel approach to concurrent data structure design where the data structure dynamically adapts its synchronization granularity based on the detected contention and the amount of data that operations are accessing. This approach not only has the potential to reduce overheads associated with synchronization in uncontended scenarios, but can also be beneficial when the amount of data that operations are accessing atomically is unknown. Using this adaptive approach we create a contention adapting search tree (CA tree) that can be used to implement concurrent ordered sets and maps with support for range queries and bulk operations. We provide detailed proof sketches for the linearizability as well as deadlock and livelock freedom of CA tree operations. We experimentally compare CA trees to state-of-the-art concurrent data structures and show that CA trees beat the best of the data structures that we compare against by over 50% in scenarios that contain basic set operations and range queries, outperform them by more than 1200% in scenarios that also contain range updates, and offer performance and scalability that is better than many of them on workloads that only contain basic set operations.

  • 4.
    Veanes, Margus
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Computing Science Department.
    Barklund, Jonas
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Computing Science Department.
    Natural cycletrees: Flexible interconnection graphs1996In: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 33, no 1, p. 44-54Article in journal (Refereed)
    Abstract [en]

    Natural cycletrees, formally defined in this paper, is a subclass of Hamiltonian graphs with maximum degree 3 that contain a binary spanning tree. A natural cycletree used as an interconnection network thus supports directly broadcasting through the binary tree as well as nearest-neighbor communication through the cycle. Natural cycletrees have several other interesting properties; e.g., they are planar, easily extensible, and can be contracted using the same methods as for binary trees. The main results of the paper are: (i) Given an arbitrary basic binary spanning treeT, there exists a natural cycletree with a minimal number of edges forT. (ii) A natural cycletree has a very simple router. We give a superfast parallel algorithm that can establish near optimal router data for that router.

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