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  • 1.
    Aguilar, Xavier
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Performance Monitoring, Analysis, and Real-Time Introspection on Large-Scale Parallel Systems2020Doctoral thesis, monograph (Other academic)
    Abstract [en]

    High-Performance Computing (HPC) has become an important scientific driver. A wide variety of research ranging for example from drug design to climate modelling is nowadays performed in HPC systems. Furthermore, the tremendous computer power of such HPC systems allows scientists to simulate problems that were unimaginable a few years ago. However, the continuous increase in size and complexity of HPC systems is turning the development of efficient parallel software into a difficult task. Therefore, the use of per- formance monitoring and analysis is a must in order to unveil inefficiencies in parallel software. Nevertheless, performance tools also face challenges as a result of the size of HPC systems, for example, coping with huge amounts of performance data generated.

    In this thesis, we propose a new model for performance characterisation of MPI applications that tackles the challenge of big performance data sets. Our approach uses Event Flow Graphs to balance the scalability of profiling techniques (generating performance reports with aggregated metrics) with the richness of information of tracing methods (generating files with sequences of time-stamped events). In other words, graphs allow to encode ordered se- quences of events without storing the whole sequence of such events, and therefore, they need much less memory and disk space, and are more scal- able. We demonstrate in this thesis how our Event Flow Graph model can be used as a trace compression method. Furthermore, we propose a method to automatically detect the structure of MPI applications using our Event Flow Graphs. This knowledge can afterwards be used to collect performance data in a smarter way, reducing for example the amount of redundant data collected. Finally, we demonstrate that our graphs can be used beyond trace compression and automatic analysis of performance data. We propose a new methodology to use Event Flow Graphs in the task of visual performance data exploration.

    In addition to the Event Flow Graph model, we also explore in this thesis the design and use of performance data introspection frameworks. Future HPC systems will be very dynamic environments providing extreme levels of parallelism, but with energy constraints, considerable resource sharing, and heterogeneous hardware. Thus, the use of real-time performance data to or- chestrate program execution in such a complex and dynamic environment will be a necessity. This thesis presents two different performance data introspec- tion frameworks that we have implemented. These introspection frameworks are easy to use, and provide performance data in real time with very low overhead. We demonstrate, among other things, how our approach can be used to reduce in real time the energy consumed by the system.

    The approaches proposed in this thesis have been validated in different HPC systems using multiple scientific kernels as well as real scientific applica- tions. The experiments show that our approaches in performance character- isation and performance data introspection are not intrusive at all, and can be a valuable contribution to help in the performance monitoring of future HPC systems.

  • 2.
    Özcan, Orcun Orkan
    et al.
    Univ Strasbourg, Inst Neurosci Cellulaires & Integrat, CNRS, F-67000 Strasbourg, France..
    Wang, Xiaolu
    Erasmus MC, Dept Neurosci, NL-1105 BA Rotterdam, Netherlands..
    Binda, Francesca
    Univ Strasbourg, Inst Neurosci Cellulaires & Integrat, CNRS, F-67000 Strasbourg, France..
    Dorgans, Kevin
    Univ Strasbourg, Inst Neurosci Cellulaires & Integrat, CNRS, F-67000 Strasbourg, France..
    De Zeeuw, Chris I.
    Erasmus MC, Dept Neurosci, NL-1105 BA Rotterdam, Netherlands.;Netherlands Inst Neurosci, Amsterdam, Netherlands..
    Gao, Zhenyu
    Erasmus MC, Dept Neurosci, NL-1105 BA Rotterdam, Netherlands..
    Aertsen, Ad
    Univ Freiburg, Fac Biol, D-79104 Freiburg, Germany.;Univ Freiburg, Bernstein Ctr Freiburg, D-79104 Freiburg, Germany..
    Kumar, Arvind
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Isope, Philippe
    Univ Strasbourg, Inst Neurosci Cellulaires & Integrat, CNRS, F-67000 Strasbourg, France..
    Differential Coding Strategies in Glutamatergic and GABAergic Neurons in the Medial Cerebellar Nucleus2020In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 40, no 1, p. 159-170Article in journal (Refereed)
    Abstract [en]

    The cerebellum drives motor coordination and sequencing of actions at the millisecond timescale through adaptive control of cerebellar nuclear output. Cerebellar nuclei integrate high-frequency information from both the cerebellar cortex and the two main excitatory inputs of the cerebellum: the mossy fibers and the climbing fiber collaterals. However, how nuclear cells process rate and timing of inputs carried by these inputs is still debated. Here, we investigate the influence of the cerebellar cortical output, the Purkinje cells, on identified cerebellar nuclei neurons in vivo in male mice. Using transgenic mice expressing Channelrhodopsin2 specifically in Purkinje cells and tetrode recordings in the medial nucleus, we identified two main groups of neurons based on the waveform of their action potentials. These two groups of neurons coincide with glutamatergic and GABAergic neurons identified by optotagging after Chrimson expression in VGLUT2-cre and GAD-cre mice, respectively. The glutamatergic-like neurons fire at high rate and respond to both rate and timing of Purkinje cell population inputs, whereas GABAergic-like neurons only respond to the mean population firing rate of Purkinje cells at high frequencies. Moreover, synchronous activation of Purkinje cells can entrain the glutamatergic-like, but not the GABAergic-like, cells over a wide range of frequencies. Our results suggest that the downstream effect of synchronous and rhythmic Purkinje cell discharges depends on the type of cerebellar nuclei neurons targeted.

  • 3.
    Finnveden, Lukas
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Jansson, Ylva
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Lindeberg, Tony
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    The problems with using STNs to align CNN feature maps2020Conference paper (Other academic)
    Abstract [en]

    Spatial transformer networks (STNs) were designed to enable CNNs to learn invariance to image transformations. STNs were originally proposed to transform CNN feature maps as well as input images. This enables the use of more complex features when predicting transformation parameters. However, since STNs perform a purely spatial transformation, they do not, in the general case, have the ability to align the feature maps of a transformed image and its original. We present a theoretical argument for this and investigate the practical implications, showing that this inability is coupled with decreased classification accuracy. We advocate taking advantage of more complex features in deeper layers by instead sharing parameters between the classification and the localisation network.

  • 4.
    Lindeberg, Tony
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Scale selection2020In: Computer Vision: A Reference Guide, Springer, 2020, 2Chapter in book (Other academic)
    Abstract [en]

    The notion of scale selection refers to methods for estimating characteristic scales in image data and for automatically determining locally appropriate scales in a scale-space representation, so as to adapt subsequent processing to the local image structure and compute scale invariant image features and image descriptors.

    An essential aspect of the approach is that it allows for a bottom-up determination of inherent scales of features and objects without first recognizing them or delimiting alternatively segmenting them from their surrounding.

    Scale selection methods have also been developed from other viewpoints of performing noise suppression and exploring top-down information.

  • 5. Dembrower, K.
    et al.
    Liu, Yue
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Azizpour, Hossein
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.
    Eklund, M.
    Smith, Kevin
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lindholm, P.
    Strand, F.
    Comparison of a deep learning risk score and standard mammographic density score for breast cancer risk prediction2020In: Radiology, ISSN 0033-8419, E-ISSN 1527-1315, Vol. 294, no 2, p. 265-272Article in journal (Refereed)
    Abstract [en]

    Background: Most risk prediction models for breast cancer are based on questionnaires and mammographic density assessments. By training a deep neural network, further information in the mammographic images can be considered. Purpose: To develop a risk score that is associated with future breast cancer and compare it with density-based models. Materials and Methods: In this retrospective study, all women aged 40-74 years within the Karolinska University Hospital uptake area in whom breast cancer was diagnosed in 2013-2014 were included along with healthy control subjects. Network development was based on cases diagnosed from 2008 to 2012. The deep learning (DL) risk score, dense area, and percentage density were calculated for the earliest available digital mammographic examination for each woman. Logistic regression models were fitted to determine the association with subsequent breast cancer. False-negative rates were obtained for the DL risk score, age-adjusted dense area, and age-adjusted percentage density. Results: A total of 2283 women, 278 of whom were later diagnosed with breast cancer, were evaluated. The age at mammography (mean, 55.7 years vs 54.6 years; P< .001), the dense area (mean, 38.2 cm2 vs 34.2 cm2; P< .001), and the percentage density (mean, 25.6% vs 24.0%; P< .001) were higher among women diagnosed with breast cancer than in those without a breast cancer diagnosis. The odds ratios and areas under the receiver operating characteristic curve (AUCs) were higher for age-adjusted DL risk score than for dense area and percentage density: 1.56 (95% confidence interval [CI]: 1.48, 1.64; AUC, 0.65), 1.31 (95% CI: 1.24, 1.38; AUC, 0.60), and 1.18 (95% CI: 1.11, 1.25; AUC, 0.57), respectively (P< .001 for AUC). The false-negative rate was lower: 31% (95% CI: 29%, 34%), 36% (95% CI: 33%, 39%; P = .006), and 39% (95% CI: 37%, 42%; P< .001); this difference was most pronounced for more aggressive cancers. Conclusion: Compared with density-based models, a deep neural network can more accurately predict which women are at risk for future breast cancer, with a lower false-negative rate for more aggressive cancers.

  • 6.
    Brocke, Ekaterina
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Method development for co-simulation of electrical-chemical systems in Neuroscience2020Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Multiscale modeling and simulation is a powerful approach for studying such phenomena in nature as learning and memory. In computational neuroscience, historically, methods and tools for neuronal modeling and simulations have been developed for studies focused on a single level of the neuronal organization. Once the community realized that the interaction of multiple systems acting at different temporal and spatial scales can lead to emerging properties of the phenomena under study, the interest in and need for models encompassing processes acting at multiple scales of time and space increased. Such models are called multiscale models.

    Multiscale modeling and simulation can be achieved in different ways. One of the possible solutions is to use an already existing foundation of formalisms and methods, and couple existing numerical algorithms and models during a simulation in a co-simulation, i.e. a joint simulation of subsystems. However, there are several obstacles on the way. First, a lack of interoperability of simulation environments makes it non-trivial to couple existing models in a single environment that supports multiscale simulation. Second, there is a decision to make regarding which variables to communicate between subsystems. The communication signal has a significant impact on the behavior of the whole multiscale system. Last but not least, an absence of a theory or general approach for the numerical coupling of existing mathematical formalisms makes the coupling of the numerical solvers a challenging task.

    The main contribution of this thesis is a numerical framework for multiscale co-simulation of electrical and chemical systems in neuroscience. A multiscale model that integrates a subcellular signaling system with the electrical activity of the neuron was developed. The thesis emphasizes the importance of numerically correct and efficient coupling of the systems of interest. Two coupling algorithms, named singlerate and multirate, differ in the rate of communication between the coupling subsystems, were proposed in the thesis. The algorithms, as well as test cases, were implemented in the MATLAB® environment. MATLAB was used to validate the accuracy and efficiency of the algorithms. Both algorithms showed an expected second order accuracy with the simulated electrical-chemical system. The guaranteed accuracy in the singlerate algorithm can be used as a trade-off for efficiency in the multirate algorithm. Thus, both algorithms can find its application in the proposed numerical framework for multiscale co-simulations. The framework exposes a modular organization with natural interfaces and could be used as a basis for the development of a generic tool for multiscale co-simulations.

    The thesis also presents an implementation of a new numerical method in the NEURON simulation environment, with benchmarks. The method can replace the standard discretization schema for the Hodgkin-Huxley type models. It can be beneficial in a co-simulation of large models where the Jacobian evaluation of the whole system becomes a very expensive operation.

    Finally, the thesis describes an extension of the MUlti-SImulation Coordinator tool (MUSIC). MUSIC is a library that is mainly used for co-simulations of spiking neural networks on a cluster. A series of important developments was done in MUSIC as the first step towards multiscale co-simulations. First, a new algorithm and an improvement of the existing parallel communication algorithms were implemented as described in the thesis. Then, a new communication scheduling algorithm was developed and implemented in the MUSIC library and analyzed. The numerical framework presented in the thesis could be implemented with MUSIC to allow efficient co-simulations of electrical-chemical systems.

  • 7.
    Lindeberg, Tony
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Provably scale-covariant continuous hierarchical networks based on scale-normalized differential expressions coupled in cascade2020In: Journal of Mathematical Imaging and Vision, ISSN 0924-9907, E-ISSN 1573-7683, Vol. 62, no 1, p. 120-148Article in journal (Refereed)
    Abstract [en]

    This article presents a theory for constructing hierarchical networks in such a way that the networks are guaranteed to be provably scale covariant. We first present a general sufficiency argument for obtaining scale covariance, which holds for a wide class of networks defined from linear and non-linear differential expressions expressed in terms of scale-normalized scale-space derivatives. Then, we present a more detailed development of one example of such a network constructed from a combination of mathematically derived models of receptive fields and biologically inspired computations. Based on a functional model of complex cells in terms of an oriented quasi quadrature combination of first- and second-order directional Gaussian derivatives, we couple such primitive computations in cascade over combinatorial expansions over image orientations. Scale-space properties of the computational primitives are analysed and we give explicit proofs of how the resulting representation allows for scale and rotation covariance. A prototype application to texture analysis is developed and it is demonstrated that a simplified mean-reduced representation of the resulting QuasiQuadNet leads to promising experimental results on three texture datasets.

  • 8.
    Mohagheghi Nejad, Mohammadreza
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany.
    Interaction of sensory and motor signals in the basal ganglia in health and disease2019Doctoral thesis, monograph (Other academic)
    Abstract [en]

    The basal ganglia, a set of deep forebrain nuclei, are among the brain regions involved in movement initiation and suppression. Although many studies have investigated the neural coding underlying these two aspects of movement, there are still questions that need to be addressed. In this thesis, I used computational models of motor thalamus and the basal ganglia at three different levels to improve the understanding of the neural coding our brain utilises to initiate and suppress movement. I used a Hodgkin-Huxley model of a thalamocortical neuron to investigate the transmission of a motor signal (i.e. movement initiation) from the basal ganglia output to the motor thalamus through post-inhibitory rebound spikes. I investigated the impact of pathological activity of the basal ganglia output (e.g. in Parkinson’s disease) and the impact of sensory responses in the basal ganglia output and cortical excitation to the thalamus on these signals. I showed that correlations in the basal ganglia output (representing pathological activity) disrupt the transmission of motor signals via rebound spikes by decreasing the signal-to-noise ratio and increasing trial-to-trial variability. In addition, I found that both the sensory responses and cortical inputs could either promote or suppress the generation of rebound spikes depending on their timing relative to the motor signal. Finally, in the model rebound spiking occurred despite the presence of moderate levels of excitation, indicating that rebound spiking might be feasible in a parameter regime relevant also in vivo.

    In addition to movement initiation, I investigated the role of basal ganglia in movement suppression using a spiking network model of the basal ganglia. I simulated a stop-signal task in the model by stimulating it with realistic patterns evoking movement-related activity in the striatum and substantia nigra pars reticulata (SNr) and evoking stop-related activity in subthalamic nucleus (STN) and arkypallidal neurons in globus pallidus externa (GPe Arky). I found that a Stop response in STN delayed initiation of movement that was detected by observing SNr activity. In addition, I showed that a Stop response in GPe Arky suppressed movement-related activity in the striatum and via direct pathway in SNr. However, the pattern of these suppressed movement-related activities did not match with previous experimental observations in successful Stop trials. I explained this mismatch using a biophysically detailed multicompartmental model of projection neurons in the striatum. I found that the long-lasting depolarisations at the level of the soma, resulting from dendritic plateau potentials evoked by clustered excitatory inputs at distal dendrites, could evoke movement-related activity in these striatal neurons. The inhibition from GPe Arky targeting the excited dendrites could fully suppress the movement-related activity matching with experimental recordings in successful Stop trials.

    In conclusion, the nigrothalamic model in this thesis provides novel insights into the transmission of motor signals from the basal ganglia to motor thalamus by suggesting new functional roles for active decorrelation and sensory responses in the basal ganglia, as well as cortical excitation of motor thalamus. Moreover, the simulation results of the Stop-signal task support the idea that the basal ganglia suppress movement in two steps: STN delays movement and then GPe Arky cancels movement.

  • 9.
    Gao, Chen-Yi
    et al.
    Chinese Acad Sci, Inst Theoret Phys, Key Lab Theoret Phys, Beijing 100190, Peoples R China.;Univ Chinese Acad Sci, Sch Phys Sci, Beijing 100049, Peoples R China..
    Cecconi, Fabio
    Sapienza Univ Roma, CNR ISC, Ple Moro 2, I-00185 Rome, Italy.;Sapienza Univ Roma, Dipartimento Fis, Ple Moro 2, I-00185 Rome, Italy..
    Vulpiani, Angelo
    Sapienza Univ Roma, CNR ISC, Ple Moro 2, I-00185 Rome, Italy.;Sapienza Univ Roma, Dipartimento Fis, Ple Moro 2, I-00185 Rome, Italy.;Accademia Lincei, Ctr Interdisciplinare B Segre, Rome, Italy..
    Zhou, Hai-Jun
    Chinese Acad Sci, Inst Theoret Phys, Key Lab Theoret Phys, Beijing 100190, Peoples R China.;Univ Chinese Acad Sci, Sch Phys Sci, Beijing 100049, Peoples R China..
    Aurell, Erik
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Aalto Univ, Dept Appl Phys, FIN-00076 Aalto, Finland.;Aalto Univ, Dept Comp Sci, FIN-00076 Aalto, Finland.;PSL Res Univ, Lab Phys Chim Theor, UMR CNRS Gulliver 7083, ESPCI, 10 Rue Vauquelin, F-75231 Paris, France..
    DCA for genome-wide epistasis analysis: the statistical genetics perspective2019In: Physical Biology, ISSN 1478-3967, E-ISSN 1478-3975, Vol. 16, no 2, article id 026002Article in journal (Refereed)
    Abstract [en]

    Direct coupling analysis (DCA) is a now widely used method to leverage statistical information from many similar biological systems to draw meaningful conclusions on each system separately. DCA has been applied with great success to sequences of homologous proteins, and also more recently to whole-genome population-wide sequencing data. We here argue that the use of DCA on the genome scale is contingent on fundamental issues of population genetics. DCA can be expected to yield meaningful results when a population is in the quasi-linkage equilibrium (QLE) phase studied by Kimura and others, but not, for instance, in a phase of clonal competition. We discuss how the exponential (Potts model) distributions emerge in QLE, and compare couplings to correlations obtained in a study of about 3000 genomes of the human pathogen Streptococcus pneumoniae.

  • 10.
    Köpp, Wiebke
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Weinkauf, Tino
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Temporal Treemaps: Static Visualization of Evolving Trees2019In: IEEE Transactions on Visualization and Computer Graphics, ISSN 1077-2626, E-ISSN 1941-0506, Vol. 25, no 1, p. 534-543Article in journal (Refereed)
    Abstract [en]

    We consider temporally evolving trees with changing topology and data: tree nodes may persist for a time range, merge or split, and the associated data may change. Essentially, one can think of this as a time series of trees with a node correspondence per hierarchy level between consecutive time steps. Existing visualization approaches for such data include animated 2D treemaps, where the dynamically changing layout makes it difficult to observe the data in its entirety. We present a method to visualize this dynamic data in a static, nested, and space-filling visualization. This is based on two major contributions: First, the layout constitutes a graph drawing problem. We approach it for the entire time span at once using a combination of a heuristic and simulated annealing. Second, we propose a rendering that emphasizes the hierarchy through an adaption of the classic cushion treemaps. We showcase the wide range of applicability using data from feature tracking in time-dependent scalar fields, evolution of file system hierarchies, and world population.

  • 11.
    Eriksson, Olivia
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, SeRC - Swedish e-Science Research Centre.
    Jauhiainen, Alexandra
    AstraZeneca, IMED Biotech Unit, Early Clin Dev, Biometr, Gothenburg, Sweden..
    Sasane, Sara Maad
    Lund Univ, Ctr Math Sci, Lund, Sweden..
    Kramer, Andrei
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Nair, Anu G.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sartorius, Carolina
    Lund Univ, Ctr Math Sci, Lund, Sweden..
    Hellgren Kotaleski, Jeanette
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, SeRC - Swedish e-Science Research Centre.
    Uncertainty quantification, propagation and characterization by Bayesian analysis combined with global sensitivity analysis applied to dynamical intracellular pathway models2019In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 35, no 2, p. 284-292Article in journal (Refereed)
    Abstract [en]

    Motivation: Dynamical models describing intracellular phenomena are increasing in size and complexity as more information is obtained from experiments. These models are often over-parameterized with respect to the quantitative data used for parameter estimation, resulting in uncertainty in the individual parameter estimates as well as in the predictions made from the model. Here we combine Bayesian analysis with global sensitivity analysis (GSA) in order to give better informed predictions; to point out weaker parts of the model that are important targets for further experiments, as well as to give guidance on parameters that are essential in distinguishing different qualitative output behaviours. Results: We used approximate Bayesian computation (ABC) to estimate the model parameters from experimental data, as well as to quantify the uncertainty in this estimation (inverse uncertainty quantification), resulting in a posterior distribution for the parameters. This parameter uncertainty was next propagated to a corresponding uncertainty in the predictions (forward uncertainty propagation), and a GSA was performed on the predictions using the posterior distribution as the possible values for the parameters. This methodology was applied on a relatively large model relevant for synaptic plasticity, using experimental data from several sources. We could hereby point out those parameters that by themselves have the largest contribution to the uncertainty of the prediction as well as identify parameters important to separate between qualitatively different predictions. This approach is useful both for experimental design as well as model building.

  • 12. Wiesenberger, M.
    et al.
    Einkemmer, L.
    Held, M.
    Gutierrez-Milla, A.
    Sáez, X.
    Iakymchuk, Roman
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Reproducibility, accuracy and performance of the FELTOR code and library on parallel computer architectures2019In: Computer Physics Communications, ISSN 0010-4655, E-ISSN 1879-2944, Vol. 238, p. 145-156Article in journal (Refereed)
    Abstract [en]

    FELTOR is a modular and free scientific software package. It allows developing platform independent code that runs on a variety of parallel computer architectures ranging from laptop CPUs to multi-GPU distributed memory systems. FELTOR consists of both a numerical library and a collection of application codes built on top of the library. Its main targets are two- and three-dimensional drift- and gyro-fluid simulations with discontinuous Galerkin methods as the main numerical discretization technique. We observe that numerical simulations of a recently developed gyro-fluid model produce non-deterministic results in parallel computations. First, we show how we restore accuracy and bitwise reproducibility algorithmically and programmatically. In particular, we adopt an implementation of the exactly rounded dot product based on long accumulators, which avoids accuracy losses especially in parallel applications. However, reproducibility and accuracy alone fail to indicate correct simulation behavior. In fact, in the physical model slightly different initial conditions lead to vastly different end states. This behavior translates to its numerical representation. Pointwise convergence, even in principle, becomes impossible for long simulation times. We briefly discuss alternative methods to ensure the correctness of results like the convergence of reduced physical quantities of interest, ensemble simulations, invariants or reduced simulation times. In a second part, we explore important performance tuning considerations. We identify latency and memory bandwidth as the main performance indicators of our routines. Based on these, we propose a parallel performance model that predicts the execution time of algorithms implemented in FELTOR and test our model on a selection of parallel hardware architectures. We are able to predict the execution time with a relative error of less than 25% for problem sizes between 10 −1 and 10 3 MB. Finally, we find that the product of latency and bandwidth gives a minimum array size per compute node to achieve a scaling efficiency above 50% (both strong and weak).

  • 13.
    Blackwell, Kim T.
    et al.
    George Mason Univ, Krasnow Inst Adv Study, Fairfax, VA 22030 USA. lackwell, Kim T..
    Salinas, Armando G.
    Tewatia, Parul
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    English, Brad
    Hellgren Kotaleski, Jeanette
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Lovinger, David M.
    Molecular mechanisms underlying striatal synaptic plasticity: relevance chronic alcohol consumption and seeking2019In: European Journal of Neuroscience, ISSN 0953-816X, E-ISSN 1460-9568, Vol. 49, no 6, p. 768-783Article in journal (Refereed)
    Abstract [en]

    The striatum, the input structure of the basal ganglia, is a major site learning and memory for goal-directed actions and habit formation. iny projection neurons of the striatum integrate cortical, thalamic, d nigral inputs to learn associations, with cortico-striatal synaptic asticity as a learning mechanism. Signaling molecules implicated in naptic plasticity are altered in alcohol withdrawal, which may ntribute to overly strong learning and increased alcohol seeking and nsumption. To understand how interactions among signaling molecules oduce synaptic plasticity, we implemented a mechanistic model of gnaling pathways activated by dopamine D1 receptors, acetylcholine ceptors, and glutamate. We use our novel, computationally efficient mulator, NeuroRD, to simulate stochastic interactions both within and tween dendritic spines. Dopamine release during theta burst and 20-Hz imulation was extrapolated from fast-scan cyclic voltammetry data llected in mouse striatal slices. Our results show that the combined tivity of several key plasticity molecules correctly predicts the currence of either LTP, LTD, or no plasticity for numerous perimental protocols. To investigate spatial interactions, we imulate two spines, either adjacent or separated on a 20-mu m ndritic segment. Our results show that molecules underlying LTP hibit spatial specificity, whereas 2-arachidonoylglycerol exhibits a atially diffuse elevation. We also implement changes in NMDA ceptors, adenylyl cyclase, and G protein signaling that have been asured following chronic alcohol treatment. Simulations under these nditions suggest that the molecular changes can predict changes in naptic plasticity, thereby accounting for some aspects of alcohol use sorder.

  • 14. Galal-Edeen, G. H.
    et al.
    Abdrabou, Y.
    Elgarf, Maha
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Hassan, H. M.
    HCI of Arabia: The challenges of HCI research in Egypt2019In: interactions, ISSN 1072-5520, E-ISSN 1558-3449, Vol. 26, no 3, p. 55-59Article in journal (Refereed)
  • 15.
    Orellana, Laura
    et al.
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Stockholm Univ, Dept Biochem & Biophys, S-11419 Stockholm, Sweden..
    Thorne, Amy H.
    Univ Calif San Diego, Ludwig Inst Canc Res, La Jolla, CA 92093 USA..
    Lema, Rafael
    Barcelona Inst Sci & Technol, Inst Res Biomed IRB Barcelona, Barcelona 08028, Catalonia, Spain..
    Gustavsson, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Parisian, Alison D.
    Univ Calif San Diego, Ludwig Inst Canc Res, La Jolla, CA 92093 USA..
    Hospital, Adam
    Barcelona Inst Sci & Technol, Inst Res Biomed IRB Barcelona, Barcelona 08028, Catalonia, Spain..
    Cordeiro, Tiago N.
    Univ Nova Lisboa, Inst Tecnol Quim & Biol Antonio Xavier, P-2780157 Oeiras, Portugal..
    Bernado, Pau
    Univ Montpellier, CNRS, INSERM, CBS, F-34090 Montpellier, France..
    Scott, Andrew M.
    Austin Hosp, Olivia Newton John Canc Res Inst, Heidelberg, Vic 3084, Australia.;La Trobe Univ, Sch Canc Med, Bundoora, Vic 3086, Australia..
    Brun-Heath, Isabelle
    Barcelona Inst Sci & Technol, Inst Res Biomed IRB Barcelona, Barcelona 08028, Catalonia, Spain..
    Lindahl, Erik
    KTH, Centres, Science for Life Laboratory, SciLifeLab. Stockholm Univ, Dept Biochem & Biophys, S-11419 Stockholm, Sweden..
    Cavenee, Webster K.
    Univ Calif San Diego, Ludwig Inst Canc Res, La Jolla, CA 92093 USA..
    Furnari, Frank B.
    Univ Calif San Diego, Ludwig Inst Canc Res, La Jolla, CA 92093 USA..
    Orozco, Modesto
    Barcelona Inst Sci & Technol, Inst Res Biomed IRB Barcelona, Barcelona 08028, Catalonia, Spain.;Univ Barcelona, Dept Biochem & Biomed, E-08028 Barcelona, Catalonia, Spain..
    Oncogenic mutations at the EGFR ectodomain structurally converge to remove a steric hindrance on a kinase-coupled cryptic epitope2019In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 116, no 20, p. 10009-10018Article in journal (Refereed)
    Abstract [en]

    Epidermal growth factor receptor (EGFR) signaling is initiated by a large ligand-favored conformational change of the extracellular domain (ECD) from a closed, self-inhibited tethered monomer, to an open untethered state, which exposes a loop required for strong dimerization and activation. In glioblastomas (GBMs), structurally heterogeneous missense and deletion mutations concentrate at the ECD for unclear reasons. We explore the conformational impact of GBM missense mutations, combining elastic network models (ENMs) with multiple molecular dynamics (MD) trajectories. Our simulations reveal that the main missense class, located at the I-II interface away from the self-inhibitory tether, can unexpectedly favor spontaneous untethering to a compact intermediate state, here validated by small-angle X-ray scattering (SAXS). Significantly, such intermediate is characterized by the rotation of a large ECD fragment (N-TR1), deleted in the most common GBM mutation, EGFRvIII, and that makes accessible a cryptic epitope characteristic of cancer cells. This observation suggested potential structural equivalence of missense and deletion ECD changes in GBMs. Corroborating this hypothesis, our FACS, in vitro, and in vivo data demonstrate that entirely different ECD variants all converge to remove N-TR1 steric hindrance from the 806-epitope, which we show is allosterically coupled to an intermediate kinase and hallmarks increased oncogenicity. Finally, the detected extraintracellular coupling allows for synergistic cotargeting of the intermediate with mAb806 and inhibitors, which is proved herein.

  • 16.
    Hahn, Gerald
    et al.
    Univ Pompeu Fabra, Ctr Brain & Cognit, Computat Neurosci Grp, Dept Informat & Commun Technol, Barcelona, Spain..
    Ponce-Alvarez, Adrian
    Univ Pompeu Fabra, Ctr Brain & Cognit, Computat Neurosci Grp, Dept Informat & Commun Technol, Barcelona, Spain..
    Deco, Gustavo
    Univ Pompeu Fabra, Ctr Brain & Cognit, Computat Neurosci Grp, Dept Informat & Commun Technol, Barcelona, Spain.;Univ Pompeu Fabra, Inst Catalana Recerca & Estudis Avancats, Barcelona, Spain..
    Aertsen, Ad
    Univ Freiburg, Fac Biol, Freiburg, Germany.;Univ Freiburg, Bernstein Ctr Freiburg, Freiburg, Germany..
    Kumar, Arvind
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Portraits of communication in neuronal networks2019In: Nature Reviews Neuroscience, ISSN 1471-003X, E-ISSN 1471-0048, Vol. 20, no 2, p. 117-127Article, review/survey (Refereed)
    Abstract [en]

    The brain is organized as a network of highly specialized networks of spiking neurons. To exploit such a modular architecture for computation, the brain has to be able to regulate the flow of spiking activity between these specialized networks. In this Opinion article, we review various prominent mechanisms that may underlie communication between neuronal networks. We show that communication between neuronal networks can be understood as trajectories in a two-dimensional state space, spanned by the properties of the input. Thus, we propose a common framework to understand neuronal communication mediated by seemingly different mechanisms. We also suggest that the nesting of slow (for example, alpha-band and theta-band) oscillations and fast (gamma-band) oscillations can serve as an important control mechanism that allows or prevents spiking signals to be routed between specific networks. We argue that slow oscillations can modulate the time required to establish network resonance or entrainment and, thereby, regulate communication between neuronal networks.

  • 17.
    Narasimhamurthy, Sai
    et al.
    Seagate Syst UK, London, England..
    Danilov, Nikita
    Seagate Syst UK, London, England..
    Wu, Sining
    Seagate Syst UK, London, England..
    Umanesan, Ganesan
    Seagate Syst UK, London, England..
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Rivas-Gomez, Sergio
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Peng, Ivy Bo
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Pleiter, Dirk
    Julich Supercomp Ctr, Julich, Germany..
    de Witt, Shaun
    Culham Ctr Fus Energy, Abingdon, Oxon, England..
    SAGE: Percipient Storage for Exascale Data Centric Computing2019In: Parallel Computing, ISSN 0167-8191, E-ISSN 1872-7336, Vol. 83, p. 22-33Article in journal (Refereed)
    Abstract [en]

    We aim to implement a Big Data/Extreme Computing (BDEC) capable system infrastructure as we head towards the era of Exascale computing - termed SAGE (Percipient StorAGe for Exascale Data Centric Computing). The SAGE system will be capable of storing and processing immense volumes of data at the Exascale regime, and provide the capability for Exascale class applications to use such a storage infrastructure. SAGE addresses the increasing overlaps between Big Data Analysis and HPC in an era of next-generation data centric computing that has developed due to the proliferation of massive data sources, such as large, dispersed scientific instruments and sensors, whose data needs to be processed, analysed and integrated into simulations to derive scientific and innovative insights. Indeed, Exascale I/O, as a problem that has not been sufficiently dealt with for simulation codes, is appropriately addressed by the SAGE platform. The objective of this paper is to discuss the software architecture of the SAGE system and look at early results we have obtained employing some of its key methodologies, as the system continues to evolve.

  • 18.
    Chen, Guang
    et al.
    Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China.;Tech Univ Munich, Robot Artificial Intelligence & Real Time Syst, Munich, Germany..
    Cao, Hu
    Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha, Hunan, Peoples R China..
    Ye, Canbo
    Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China..
    Zhang, Zhenyan
    Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China..
    Liu, Xingbo
    Tongji Univ, Coll Automot Engn, Shanghai, Peoples R China..
    Mo, Xuhui
    Hunan Univ, State Key Lab Adv Design & Mfg Vehicle Body, Changsha, Hunan, Peoples R China..
    Qu, Zhongnan
    Swiss Fed Inst Technol, Comp Engn & Networks Lab, Zurich, Switzerland..
    Conradt, Jörg
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Roehrbein, Florian
    Tech Univ Munich, Robot Artificial Intelligence & Real Time Syst, Munich, Germany..
    Knoll, Alois
    Tech Univ Munich, Robot Artificial Intelligence & Real Time Syst, Munich, Germany..
    Multi-Cue Event Information Fusion for Pedestrian Detection With Neuromorphic Vision Sensors2019In: Frontiers in Neurorobotics, ISSN 1662-5218, Vol. 13, article id 10Article in journal (Refereed)
    Abstract [en]

    Neuromorphic vision sensors are bio-inspired cameras that naturally capture the dynamics of a scene with ultra-low latency, filtering out redundant information with low power consumption. Few works are addressing the object detection with this sensor. In this work, we propose to develop pedestrian detectors that unlock the potential of the event data by leveraging multi-cue information and different fusion strategies. To make the best out of the event data, we introduce three different event-stream encoding methods based on Frequency, Surface of Active Event (SAE) and Leaky Integrate-and-Fire (LIF). We further integrate them into the state-of-the-art neural network architectures with two fusion approaches: the channel-level fusion of the raw feature space and decision-level fusion with the probability assignments. We present a qualitative and quantitative explanation why different encoding methods are chosen to evaluate the pedestrian detection and which method performs the best. We demonstrate the advantages of the decision-level fusion via leveraging multi-cue event information and show that our approach performs well on a self-annotated event-based pedestrian dataset with 8,736 event frames. This work paves the way of more fascinating perception applications with neuromorphic vision sensors.

  • 19.
    Lindeberg, Tony
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Provably scale-covariant networks from oriented quasi quadrature measures in cascade2019In: Scale Space and Variational Methods in Computer Vision / [ed] M. Burger, J. Lellmann and J. Modersitzki, Springer Berlin/Heidelberg, 2019, Vol. 11603, p. 328-340Conference paper (Refereed)
    Abstract [en]

    This article presents a continuous model for hierarchical networks based on a combination of mathematically derived models of receptive fields and biologically inspired computations.

    Based on a functional model of complex cells in terms of an oriented quasi quadrature combination of first- and second-order directional Gaussian derivatives, we couple such primitive computations in cascade over combinatorial expansions over image orientations. Scale-space properties of the computational primitives are analysed and it is shown that the resulting representation allows for provable scale and rotation covariance.

    A prototype application to texture analysis is developed and it is demonstrated that a simplified mean-reduced representation of the resulting QuasiQuadNet leads to promising experimental results on three texture datasets.

  • 20.
    Simmendinger, Christian
    et al.
    T Syst Solut Res, Stuttgart, Germany..
    Iakymchuk, Roman
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Cebamanos, Luis
    Univ Edinburgh, EPCC, Edinburgh, Midlothian, Scotland..
    Akhmetova, Dana
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Bartsch, Valeria
    Fraunhofer ITWM, HPC Dept, Kaiserslautern, Germany..
    Rotaru, Tiberiu
    Fraunhofer ITWM, Kaiserslautern, Germany..
    Rahn, Mirko
    Fraunhofer ITWM, HPC Dept, Kaiserslautern, Germany..
    Laure, Erwin
    KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC. KTH Royal Inst Technol, High Performance Comp, Stockholm, Sweden.;KTH Royal Inst Technol, PDC Ctr, High Performance Comp Ctr, Stockholm, Sweden..
    Markidis, Stefano
    KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH Royal Inst Technol, High Performance Comp, Stockholm, Sweden..
    Interoperability strategies for GASPI and MPI in large-scale scientific applications2019In: The international journal of high performance computing applications, ISSN 1094-3420, E-ISSN 1741-2846, Vol. 33, no 3, p. 554-568Article in journal (Refereed)
    Abstract [en]

    One of the main hurdles of partitioned global address space (PGAS) approaches is the dominance of message passing interface (MPI), which as a de facto standard appears in the code basis of many applications. To take advantage of the PGAS APIs like global address space programming interface (GASPI) without a major change in the code basis, interoperability between MPI and PGAS approaches needs to be ensured. In this article, we consider an interoperable GASPI/MPI implementation for the communication/performance crucial parts of the Ludwig and iPIC3D applications. To address the discovered performance limitations, we develop a novel strategy for significantly improved performance and interoperability between both APIs by leveraging GASPI shared windows and shared notifications. First results with a corresponding implementation in the MiniGhost proxy application and the Allreduce collective operation demonstrate the viability of this approach.

  • 21. Mirus, F.
    et al.
    Blouw, P.
    Stewart, T. C.
    Conradt, Jörg
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Predicting vehicle behaviour using LSTMs and a vector power representation for spatial positions2019In: ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN (i6doc.com) , 2019, p. 113-118Conference paper (Refereed)
    Abstract [en]

    Predicting future vehicle behaviour is an essential task to enable safe and situation-aware automated driving. In this paper, we propose to encapsulate spatial information of multiple objects in a semantic vector-representation. Assuming that future vehicle motion is influenced not only by past positions but also by the behaviour of other traffic participants, we use this representation as input for a Long Short-Term Memory (LSTM) network for sequence to sequence prediction of vehicle positions. We train and evaluate our system on real-world driving data collected mainly on highways in southern Germany and compare it to other models for reference.

  • 22.
    Wärnberg, Emil
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Dept.of Neuroscience,Karolinska Institutet, Sweden.
    Kumar, Arvind
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Perturbing low dimensional activity manifolds in spiking neuronal networks2019In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 15, no 5, article id e1007074Article in journal (Refereed)
    Abstract [en]

    Several recent studies have shown that neural activity in vivo tends to be constrained to a low-dimensional manifold. Such activity does not arise in simulated neural networks with homogeneous connectivity and it has been suggested that it is indicative of some other connectivity pattern in neuronal networks. In particular, this connectivity pattern appears to be constraining learning so that only neural activity patterns falling within the intrinsic manifold can be learned and elicited. Here, we use three different models of spiking neural networks (echo-state networks, the Neural Engineering Framework and Efficient Coding) to demonstrate how the intrinsic manifold can be made a direct consequence of the circuit connectivity. Using this relationship between the circuit connectivity and the intrinsic manifold, we show that learning of patterns outside the intrinsic manifold corresponds to much larger changes in synaptic weights than learning of patterns within the intrinsic manifold. Assuming larger changes to synaptic weights requires extensive learning, this observation provides an explanation of why learning is easier when it does not require the neural activity to leave its intrinsic manifold.

  • 23.
    Zhou, Hongyang
    et al.
    Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI 48109 USA..
    Toth, Gabor
    Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI 48109 USA..
    Jia, Xianzhe
    Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI 48109 USA..
    Chen, Yuxi
    Univ Michigan, Dept Climate & Space Sci & Engn, Ann Arbor, MI 48109 USA..
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Embedded Kinetic Simulation of Ganymede's Magnetosphere: Improvements and Inferences2019In: Journal of Geophysical Research - Space Physics, ISSN 2169-9380, E-ISSN 2169-9402, Vol. 124, no 7, p. 5441-5460Article in journal (Refereed)
    Abstract [en]

    The largest moon in the solar system, Ganymede, is also the only moon known to possess a strong intrinsic magnetic field and a corresponding magnetosphere. Using the new version of Hall magnetohydrodynamic with embedded particle-in-cell model with a self-consistently coupled resistive body representing the electrical properties of the moon's interior, improved inner boundary conditions, and the flexibility of coupling different grid geometries, we achieve better match of magnetic field with measurements for all six Galileo flybys. The G2 flyby comparisons of plasma bulk flow velocities with the Galileo Plasma Subsystem data support the oxygen ion assumption inside Ganymede's magnetosphere. Crescent shape, nongyrotropic, and nonisotropic ion distributions are identified from the coupled model. Furthermore, we have derived the energy fluxes associated with the upstream magnetopause reconnection of similar to 10(-7) W/cm(2) based on our model results and found a maximum of 40% contribution to the total peak auroral emissions.

  • 24. Mirus, F.
    et al.
    Zorn, B.
    Conradt, Jörg
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Short-term trajectory planning using reinforcement learning within a neuromorphic control architecture2019In: ESANN 2019 - Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN (i6doc.com) , 2019, p. 649-654Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a first step towards neuromorphic vehicle control. We propose a modular and hierarchical system architecture entirely implemented in a spiking neuron substrate, which allows for the adjustment of individual components through either supervised or reinforcement learning as well as future deployment on dedicated neuromorphic hardware. In a sample instantiation, we investigate automated training of a neuromorphic trajectory selection module using reinforcement learning to demonstrate the general feasibility of our approach. We evaluate our system using the open-source race car simulator TORCS.

  • 25.
    Martinez Mayorquin, Ramon Heberto
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Lansner, Anders
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Herman, Pawel
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Probabilistic associative learning suffices for learning the temporal structure of multiple sequences2019In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, no 8, article id e0220161Article in journal (Refereed)
    Abstract [en]

    From memorizing a musical tune to navigating a well known route, many of our underlying behaviors have a strong temporal component. While the mechanisms behind the sequential nature of the underlying brain activity are likely multifarious and multi-scale, in this work we attempt to characterize to what degree some of this properties can be explained as a consequence of simple associative learning. To this end, we employ a parsimonious firing-rate attractor network equipped with the Hebbian-like Bayesian Confidence Propagating Neural Network (BCPNN) learning rule relying on synaptic traces with asymmetric temporal characteristics. The proposed network model is able to encode and reproduce temporal aspects of the input, and offers internal control of the recall dynamics by gain modulation. We provide an analytical characterisation of the relationship between the structure of the weight matrix, the dynamical network parameters and the temporal aspects of sequence recall. We also present a computational study of the performance of the system under the effects of noise for an extensive region of the parameter space. Finally, we show how the inclusion of modularity in our network structure facilitates the learning and recall of multiple overlapping sequences even in a noisy regime.

  • 26.
    Heining, Katharina
    et al.
    Univ Freiburg, Dept Microsyst Engn IMTEK, Biomicrotechnol, Fac Engn, D-79110 Freiburg, Germany.;Univ Freiburg, Bernstein Ctr Freiburg, D-79104 Freiburg, Germany.;Univ Freiburg, Fac Biol, D-79104 Freiburg, Germany..
    Kilias, Antje
    Univ Freiburg, Dept Microsyst Engn IMTEK, Biomicrotechnol, Fac Engn, D-79110 Freiburg, Germany.;Univ Freiburg, Bernstein Ctr Freiburg, D-79104 Freiburg, Germany.;Univ Freiburg, Fac Biol, D-79104 Freiburg, Germany..
    Janz, Philipp
    Univ Freiburg, Fac Biol, D-79104 Freiburg, Germany.;Univ Freiburg, Dept Neurosurg, Med Ctr, Expt Epilepsy Res, D-79106 Freiburg, Germany..
    Haeussler, Ute
    Univ Freiburg, Dept Neurosurg, Med Ctr, Expt Epilepsy Res, D-79106 Freiburg, Germany.;Univ Freiburg, BrainLinks BrainTools Cluster Excellence, D-79110 Freiburg, Germany..
    Kumar, Arvind
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). Univ Freiburg, Bernstein Ctr Freiburg, D-79104 Freiburg, Germany..
    Haas, Carola A.
    Univ Freiburg, Bernstein Ctr Freiburg, D-79104 Freiburg, Germany.;Univ Freiburg, Dept Neurosurg, Med Ctr, Expt Epilepsy Res, D-79106 Freiburg, Germany.;Univ Freiburg, BrainLinks BrainTools Cluster Excellence, D-79110 Freiburg, Germany..
    Egert, Ulrich
    Univ Freiburg, Dept Microsyst Engn IMTEK, Biomicrotechnol, Fac Engn, D-79110 Freiburg, Germany.;Univ Freiburg, Bernstein Ctr Freiburg, D-79104 Freiburg, Germany.;Univ Freiburg, BrainLinks BrainTools Cluster Excellence, D-79110 Freiburg, Germany..
    Bursts with High and Low Load of Epileptiform Spikes Show Contex-Dependent Correlations in Epileptic Mice2019In: ENEURO, ISSN 2373-2822, Vol. 6, no 5, article id UNSP ENEURO.0299-18.2019Article in journal (Refereed)
    Abstract [en]

    Hypersynchronous network activity is the defining hallmark of epilepsy and manifests in a wide spectrum of phenomena, of which electrographic activity during seizures is only one extreme. The aim of this study was to differentiate between different types of epileptiform activity (EA) patterns and investigate their temporal succession and interactions. We analyzed local field potentials (LFPs) from freely behaving male mice that had received an intrahippocampal kainate injection to model mesial temporal lobe epilepsy (MTLE). Epileptiform spikes occurred in distinct bursts. Using machine learning, we derived a scale reflecting the spike load of bursts and three main burst categories that we labeled high-load, medium-load, and low-load bursts. We found that bursts of these categories were non-randomly distributed in time. High-load bursts formed clusters and were typically surrounded by transition phases with increased rates of medium-load and low-load bursts. In apparent contradiction to this, increased rates of low-load bursts were also associated with longer background phases, i.e., periods lacking high-load bursting. Furthermore, the rate of low-load bursts was more strongly correlated with the duration of background phases than the overall rate of epileptiform spikes. Our findings are consistent with the hypothesis that low-level EA could promote network stability but could also participate in transitions towards major epileptiform events, depending on the current state of the network.

  • 27.
    Iakymchuk, Roman
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Graillat, Stef
    Sorbonne Univ, Paris, France..
    Defour, David
    Univ Perpignan, Perpignan, France..
    Quintana-Orti, Enrique S.
    Univ Jaime I, Castellon de La Plana, Spain..
    Hierarchical approach for deriving a reproducible unblocked LU factorization2019In: The international journal of high performance computing applications, ISSN 1094-3420, E-ISSN 1741-2846, Vol. 33, no 5, p. 791-803Article in journal (Refereed)
    Abstract [en]

    We propose a reproducible variant of the unblocked LU factorization for graphics processor units (GPUs). For this purpose, we build upon Level-1/2 BLAS kernels that deliver correctly-rounded and reproducible results for the dot (inner) product, vector scaling, and the matrix-vector product. In addition, we draw a strategy to enhance the accuracy of the triangular solve via iterative refinement. Following a bottom-up approach, we finally construct a reproducible unblocked implementation of the LU factorization for GPUs, which accommodates partial pivoting for stability and can be eventually integrated in a high performance and stable algorithm for the (blocked) LU factorization.

  • 28. Aronsson, Sanna
    et al.
    Artman, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Media Technology and Interaction Design, MID.
    Lindquist, Sinna
    Mikael, Mitchell
    Persson, Tomas
    KTH, School of Electrical Engineering and Computer Science (EECS), Media Technology and Interaction Design, MID.
    Ramberg, Robert
    Stockholms Universitet.
    Romero, Mario
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    van de Vehn, Pontus
    Supporting after action review in simulator mission training: Co-creating visualization concepts for training of fast-jet fighter pilots2019In: The Journal of Defence Modeling and Simulation: Applications, Methodology, Technology, ISSN 1548-5129, E-ISSN 1557-380X, Vol. 16, no 3, p. 219-231Article in journal (Refereed)
    Abstract [en]

    This article presents the design and evaluation of visualization concepts supporting After Action Review (AAR) in simulator mission training of fast-jet fighter pilots. The visualization concepts were designed based on three key characteristics of representations: re-representation, graphical constraining, and computational offloading. The visualization concepts represent combined parameters of missile launch and threat range, the former meant to elicit discussions about the prerequisites for launching missiles, and the latter to present details of what threats a certain aircraft is facing at a specific moment. The visualization concepts were designed to: 1) perceptually and cognitively offload mental workload from participants in support of determining relevant situations to discuss; 2) re-represent parameters in a format that facilitates reading-off of crucial information; and 3) graphically constrain plausible interpretations. Through a series of workshop iterations, two visualization concepts were developed and evaluated with 11 pilots and instructors. All pilots were unanimous in their opinion that the visualization concepts should be implemented as part of the AAR. Offloading, in terms of finding interesting events in the dynamic and unique training sessions, was the most important guiding concept, while re-representation and graphical constraining enabled a more structured and grounded collaboration during the AAR.

  • 29.
    Orellana, Laura
    et al.
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, S-10691 Stockholm, Sweden..
    Gustavsson, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Bergh, Cathrine
    KTH, School of Engineering Sciences (SCI), Applied Physics, Biophysics. KTH, Centres, SeRC - Swedish e-Science Research Centre.
    Yoluk, Ozge
    KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Engineering Sciences (SCI), Applied Physics, Biophysics. Univ Maryland, Sch Pharm, Dept Pharmaceut Sci, Baltimore, MD 21201 USA..
    Lindahl, Erik
    KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Engineering Sciences (SCI), Applied Physics, Biophysics. Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, S-10691 Stockholm, Sweden..
    eBDIMS server: protein transition pathways with ensemble analysis in 2D-motion spaces2019In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 35, no 18, p. 3505-3507Article in journal (Refereed)
    Abstract [en]

    A Summary: Understanding how proteins transition between different conformers, and how conformers relate to each other in terms of structure and function, is not trivial. Here, we present an online tool for transition pathway generation between two protein conformations using Elastic Network Driven Brownian Dynamics Importance Sampling, a coarse-grained simulation algorithm, which spontaneously predicts transition intermediates trapped experimentally. In addition to path-generation, the server provides an interactive 2D-motion landscape graphical representation of the transitions or any additional conformers to explore their structural relationships.

  • 30.
    Peng, Ivy B.
    et al.
    Oak Ridge Natl Lab, Oak Ridge, TN 37830 USA..
    Vetter, Jeffrey S.
    Oak Ridge Natl Lab, Oak Ridge, TN 37830 USA..
    Moore, Shirley
    Oak Ridge Natl Lab, Oak Ridge, TN 37830 USA..
    Joydeep, Rakshit
    Intel Labs, Santa Clara, CA USA..
    Markidis, Stefano
    KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Analyzing the Suitability of Contemporary 3D-Stacked PIM Architectures for HPC Scientific Applications2019In: CF '19 - PROCEEDINGS OF THE 16TH ACM INTERNATIONAL CONFERENCE ON COMPUTING FRONTIERS, ASSOC COMPUTING MACHINERY , 2019, p. 256-262Conference paper (Refereed)
    Abstract [en]

    Scaling off-chip bandwidth is challenging due to fundamental limitations, such as a fixed pin count and plateauing signaling rates. Recently, vendors have turned to 2.5D and 3D stacking to closely integrate system components. Interestingly, these technologies can integrate a logic layer under multiple memory dies, enabling computing capability inside a memory stack. This trend in stacking is making PIM architectures commercially viable. In this work, we investigate the suitability of offloading kernels in scientific applications onto 3D stacked PIM architectures. We evaluate several hardware constraints resulted from the stacked structure. We perform extensive simulation experiments and indepth analysis to quantify the impact of application locality in TI,Bs, data caches, and memory stacks. Our results also identify design optimization areas in software and hardware for HPC scientific applications.

  • 31.
    Aguilar, Xavier
    et al.
    KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Jordan, H.
    Heller, T.
    Hirsch, A.
    Fahringer, T.
    Laure, Erwin
    KTH, Centres, SeRC - Swedish e-Science Research Centre. KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    An On-Line Performance Introspection Framework for Task-Based Runtime Systems2019In: 19th International Conference on Computational Science, ICCS 2019, Springer Verlag , 2019, p. 238-252Conference paper (Refereed)
    Abstract [en]

    The expected high levels of parallelism together with the heterogeneity and complexity of new computing systems pose many challenges to current software. New programming approaches and runtime systems that can simplify the development of parallel applications are needed. Task-based runtime systems have emerged as a good solution to cope with high levels of parallelism, while providing software portability, and easing program development. However, these runtime systems require real-time information on the state of the system to properly orchestrate program execution and optimise resource utilisation. In this paper, we present a lightweight monitoring infrastructure developed within the AllScale Runtime System, a task-based runtime system for extreme scale. This monitoring component provides real-time introspection capabilities that help the runtime scheduler in its decision-making process and adaptation, while introducing minimum overhead. In addition, the monitoring component provides several post-mortem reports as well as real-time data visualisation that can be of great help in the task of performance debugging.

  • 32.
    Rivas Gomez, Sergio
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Brabazon, K.
    Perks, O.
    Narasimhamurthy, S.
    Decoupled Strategy for Imbalanced Workloads in MapReduce Frameworks2019In: Proceedings - 20th International Conference on High Performance Computing and Communications, 16th International Conference on Smart City and 4th International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2018, Institute of Electrical and Electronics Engineers (IEEE), 2019, p. 921-927Conference paper (Refereed)
    Abstract [en]

    In this work, we consider the integration of MPI one-sided communication and non-blocking I/O in HPC-centric MapReduce frameworks. Using a decoupled strategy, we aim to overlap the Map and Reduce phases of the algorithm by allowing processes to communicate and synchronize using solely one-sided operations. Hence, we effectively increase the performance in situations where the workload per process becomes unexpectedly unbalanced. Using a Word-Count implementation and a large dataset from the Purdue MapReduce Benchmarks Suite (PUMA), we demonstrate that our approach can provide up to 23% performance improvement on average compared to a reference MapReduce implementation that uses state-of-the-art MPI collective communication and I/O.

  • 33.
    Nguyen, Van Dang
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Jansson, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Goude, Anders
    Uppsala University, Uppsala, Sweden.
    Hoffman, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Direct Finite Element Simulation of the Turbulent Flow Past a Vertical Axis Wind Turbine2019In: Renewable energy, ISSN 0960-1481, E-ISSN 1879-0682, Vol. 135, p. 238-247Article in journal (Refereed)
    Abstract [en]

    There is today a significant interest in harvesting renewable energy, specifically wind energy, in offshore and urban environments. Vertical axis wind turbines get increasing attention since they are able to capture the wind from any direction. They are relatively easy to install and to transport, cheaper to build and maintain, and quite safe for humans and birds. Detailed computer simulations of the fluid dynamics of wind turbines provide an enhanced understanding of the technology and may guide design improvements. In this paper, we simulate the turbulent flow past a vertical axis wind turbine for a range of rotation angles in parked and rotating conditions. We propose the method of Direct Finite Element Simulation in a rotating ALE framework, abbreviated as DFS-ALE. The simulation results are validated against experimental data in the form of force measurements. We find that the simulation results are stable with respect to mesh refinement and that we capture well the general shape of the variation of force measurements over the rotation angles.

  • 34.
    Nguyen, Van Dang
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Jansson, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Goude, Anders
    Hoffman, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Technical Report -- Comparison of Direct Finite Element Simulation with Actuator Line Models and Vortex Models for Simulation of Turbulent Flow Past a Vertical Axis wind Turbine2019Report (Other (popular science, discussion, etc.))
    Abstract [en]

    We compare three different methodologies for simulation of turbulent flow past a vertical axis wind turbine: (i) full resolution of the turbine blades in a Direct Finite Element Simulation (DFS), (ii) implicit representation of the turbine blades in a 3D Actuator Line Method (ALM), and (iii) implicit representation of the turbine blades as sources in a Vortex Model (VM). The integrated normal force on one blade is computed for a range of azimuthal angles, and is compared to experimental data for the different tip speed ratios, 2.55, 3.44 and 4.09.

  • 35.
    Rivas-Gomez, Sergio
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    High-Performance I/O Programming Models for Exascale Computing2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The success of the exascale supercomputer is largely dependent on novel breakthroughs that overcome the increasing demands for high-performance I/O on HPC. Scientists are aggressively taking advantage of the available compute power of petascale supercomputers to run larger scale and higher-fidelity simulations. At the same time, data-intensive workloads have recently become dominant as well. Such use-cases inherently pose additional stress into the I/O subsystem, mostly due to the elevated number of I/O transactions.

    As a consequence, three critical challenges arise that are of paramount importance at exascale. First, while the concurrency of next-generation supercomputers is expected to increase up to 1000x, the bandwidth and access latency of the I/O subsystem is projected to remain roughly constant in comparison. Storage is, therefore, on the verge of becoming a serious bottleneck. Second, despite upcoming supercomputers expected to integrate emerging non-volatile memory technologies to compensate for some of these limitations, existing programming models and interfaces (e.g., MPI-IO) might not provide any clear technical advantage when targeting distributed intra-node storage, let alone byte-addressable persistent memories. And third, even though compute nodes becoming heterogeneous can provide benefits in terms of performance and thermal dissipation, this technological transformation implicitly increases the programming complexity. Hence, making it difficult for scientific applications to take advantage of these developments.

    In this thesis, we explore how programming models and interfaces must evolve to address the aforementioned challenges. We present MPI storage windows, a novel concept that proposes utilizing the MPI one-sided communication model and MPI windows as a unified interface to program memory and storage. We then demonstrate how MPI one-sided can provide benefits on data analytics frameworks following a decoupled strategy, while integrating seamless fault-tolerance and out-of-core execution. Furthermore, we introduce persistent coarrays to enable transparent resiliency in Coarray Fortran, supporting the "failed images" feature recently introduced into the standard. Finally, we propose a global memory abstraction layer, inspired by the memory-mapped I/O mechanism of the OS, to expose different storage technologies using conventional memory operations.

    The outcomes from these contributions are expected to have a considerable impact in a wide-variety of scientific applications on HPC, both in current and next-generation supercomputers.

  • 36.
    Saikia, Himangshu
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST). KTH Royal Inst Technol, Stockholm, Sweden..
    Yang, Fangkai
    KTH.
    Peters, Christopher
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Priority driven Local Optimization for Crowd Simulation2019In: AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS, ASSOC COMPUTING MACHINERY , 2019, p. 2180-2182Conference paper (Refereed)
    Abstract [en]

    We provide an initial model and preliminary findings of a lookahead based local optimization scheme for collision resolution between agents in large goal-directed crowd simulations. Considering crowd simulation to be a global optimization problem, we break down this large problem into smaller problems where each potential collision resolution step is independently optimized in terms of a criticality measure. Agents resolved earlier in order of criticality, maintain the optimized velocity obtained, for the resolution of agents that come later in that order. Hence, the problem is converted to a low dimensional optimization problem of one or two agents where all other obstacles are static or deterministically dynamic. We illustrate the performance of our method on four well known test scenarios.

  • 37.
    Ahmed, Laeeq
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Scalable Analysis of Large Datasets in Life Sciences2019Doctoral thesis, monograph (Other academic)
    Abstract [en]

    We are experiencing a deluge of data in all fields of scientific and business research, particularly in the life sciences, due to the development of better instrumentation and the rapid advancements that have occurred in information technology in recent times. There are major challenges when it comes to handling such large amounts of data. These range from the practicalities of managing these large volumes of data, to understanding the meaning and practical implications of the data.

    In this thesis, I present parallel methods to efficiently manage, process, analyse and visualize large sets of data from several life sciences fields at a rapid rate, while building and utilizing various machine learning techniques in a novel way. Most of the work is centred on applying the latest Big Data Analytics frameworks for creating efficient virtual screening strategies while working with large datasets. Virtual screening is a method in cheminformatics used for Drug discovery by searching large libraries of molecule structures. I also present a method for the analysis of large Electroencephalography data in real time. Electroencephalography is one of the main techniques used to measure the brain electrical activity.

    First, I evaluate the suitability of Spark, a parallel framework for large datasets, for performing parallel ligand-based virtual screening. As a case study, I classify molecular library using prebuilt classification models to filter out the active molecules. I also demonstrate a strategy to create cloud-ready pipelines for structure-based virtual screening. The major advantages of this strategy are increased productivity and high throughput. In this work, I show that Spark can be applied to virtual screening, and that it is, in general, an appropriate solution for large-scale parallel pipelining. Moreover, I illustrate how Big Data analytics are valuable in working with life sciences datasets.

    Secondly, I present a method to further reduce the overall time of the structured-based virtual screening strategy using machine learning and a conformal-prediction-based iterative modelling strategy. The idea is to only dock those molecules that have a better than average chance of being an inhibitor when searching for molecules that could potentially be used as drugs. Using machine learning models from this work, I built a web service to predict the target profile of multiple compounds against ready-made models for a list of targets where 3D structures are available. These target predictions can be used to understand off-target effects, for example in the early stages of drug discovery projects.

    Thirdly, I present a method to detect seizures in long term Electroencephalography readings - this method works in real time taking the ongoing readings in as live data streams. The method involves tackling the challenges of real-time decision-making, storing large datasets in memory and updating the prediction model with newly produced data at a rapid rate. The resulting algorithm not only classifies seizures in real time, it also learns the threshold in real time. I also present a new feature "top-k amplitude measure" for classifying which parts of the data correspond to seizures. Furthermore, this feature helps to reduce the amount of data that needs to be processed in the subsequent steps.

  • 38.
    Sishtla, Chaitanya Prasad
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Olshevsky, Viacheslav
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Chien, Wei Der
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computational Science and Technology (CST).
    Laure, Erwin
    KTH, School of Electrical Engineering and Computer Science (EECS), Centres, Centre for High Performance Computing, PDC.
    Particle-in-Cell Simulations of Plasma Dynamics in Cometary Environment2019In: Journal of Physics: Conference Series, Institute of Physics Publishing (IOPP), 2019, Vol. 1225, no 1, article id 012009Conference paper (Refereed)
    Abstract [en]

    We perform and analyze global Particle-in-Cell (PIC) simulations of the interaction between solar wind and an outgassing comet with the goal of studying the plasma kinetic dynamics of a cometary environment. To achieve this, we design and implement a new numerical method in the iPIC3D code to model outgassing from the comet: new plasma particles are ejected from the comet "surface" at each computational cycle. Our simulations show that a bow shock is formed as a result of the interaction between solar wind and outgassed particles. The analysis of distribution functions for the PIC simulations shows that at the bow shock part of the incoming solar wind, ions are reflected while electrons are heated. This work attempts to reveal kinetic effects in the atmosphere of an outgassing comet using a fully kinetic Particle-in-Cell model.

  • 39.
    Rivas-Gomez, Sergio
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Fanfarillo, Alessandro
    National Center for Atmospheric Research, Boulder, CO, United States..
    Valat, Sebastien
    Atos, 1 Rue de Provence, 38130 Echirolles, France.
    Laferriere, Christophe
    Atos, 1 Rue de Provence, 38130 Echirolles, France.
    Couvee, Philippe
    Atos, 1 Rue de Provence, 38130 Echirolles, France.
    Narasimhamurthy, Sai
    Seagate Syst UK, Havant PO9 1SA, England..
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    uMMAP-IO: User-level Memory-mapped I/O for HPC2019In: Proceedings of the 26th IEEE International Conference on High-Performance Computing, Data, and Analytics (HiPC'19),, Institute of Electrical and Electronics Engineers (IEEE), 2019Conference paper (Refereed)
    Abstract [en]

    The integration of local storage technologies alongside traditional parallel file systems on HPC clusters, is expected to rise the programming complexity on scientific applications aiming to take advantage of the increased-level of heterogeneity. In this work, we present uMMAP-IO, a user-level memory-mapped I/O implementation that simplifies data management on multi-tier storage subsystems. Compared to the memory-mapped I/O mechanism of the OS, our approach features per-allocation configurable settings (e.g., segment size) and transparently enables access to a diverse range of memory and storage technologies, such as the burst buffer I/O accelerators. Preliminary results indicate that uMMAP-IO provides at least 5-10x better performance on representative workloads in comparison with the standard memory-mapped I/O of the OS, and approximately 20-50% degradation on average compared to using conventional memory allocations without storage support up to 8192 processes.

  • 40.
    Nguyen, Van Dang
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    High Performance Finite Element Methods with Application to Simulation of Vertical Axis Wind Turbines and Diffusion MRI2019Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Finite element methods have been developed over decades, and together with the growth of computer power, they become more and more important in dealing with large-scale simulations in science and industry.The objective of this thesis is to develop high-performance finite element methods, with two concrete applications: computational fluid dynamics (CFD) with simulation of turbulent flow past a vertical axis wind turbine (VAWT), and computational diffusion magnetic resonance imaging (CDMRI). The thesis presents contributions in the form of both new numerical methods for high-performance computing frameworks and efficient, tested software, published open source as part of the FEniCS/FEniCS-HPC platform. More specifically, we have four main contributions through the thesis work.

    First, we develop a DFS-ALE method which combines the Direct finite element simulation method (DFS) with the Arbitrary Lagrangian-Eulerian method (ALE) to solve the Navier-Stokes equations for a rotating turbine. This method is enhanced with dual-based a posteriori error control and automated mesh adaptation. Turbulent boundary layers are modeled by a slip boundary condition to avoid a full resolution which is impossible even with the most powerful computers available today. The method is validated against experimental data with a good agreement.

    Second, we propose a partition of unity finite element method to tackle interface problems. In CFD, it allows for imposing slip velocity boundary conditions on conforming internal interfaces for a fluid-structure interaction model. In CDMRI, it helps to overcome the difficulties that the standard approaches have when imposing the microscopic heterogeneity of the biological tissues and allows for efficient solutions of the Bloch-Torrey equation in heterogeneous domains. The method facilitates a straightforward implementation on the FEniCS/ FEniCS-HPC platform. The method is validated against reference solutions, and the implementation shows a strong parallel scalability.

    Third, we propose a finite element discretization on manifolds in order to efficiently simulate the diffusion MRI signal in domains that have a thin layer or a thin tube geometrical structure. The method helps to significantly reduce the required simulation time, computer memory, and difficulties associated with mesh generation, while maintaining the accuracy. Thus, it opens the possibility to simulate complicated structures at a low cost, for a better understanding of diffusion MRI in the brain.

    Finally, we propose an efficient portable simulation framework that integrates recent advanced techniques in both mathematics and computer science to enable the users to perform simulations with the Cloud computing technology. The simulation framework consists of Python, IPython and C++ solvers working either on a web browser with Google Colaboratory notebooks or on the Google Cloud Platform with MPI parallelization.

  • 41.
    Petras, Argyrios
    et al.
    Basque Ctr Appl Math, Alameda Mazarredo 14, Bilbao 48009, Spain..
    Leoni, Massimiliano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). Basque Ctr Appl Math, Alameda Mazarredo 14, Bilbao 48009, Spain..
    Guerra, Jose M.
    Hosp Santa Creu & Sant Pau, Dept Cardiol, Barcelona, Spain..
    Jansson, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). Basque Ctr Appl Math, Alameda Mazarredo 14, Bilbao 48009, Spain..
    Gerardo-Giorda, Luca
    Basque Ctr Appl Math, Alameda Mazarredo 14, Bilbao 48009, Spain..
    A computational model of open-irrigated radiofrequency catheter ablation accounting for mechanical properties of the cardiac tissue2019In: International Journal for Numerical Methods in Biomedical Engineering, ISSN 2040-7939, E-ISSN 2040-7947, article id e3232Article in journal (Refereed)
    Abstract [en]

    Radiofrequency catheter ablation (RFCA) is an effective treatment for cardiac arrhythmias. Although generally safe, it is not completely exempt from the risk of complications. The great flexibility of computational models can be a major asset in optimizing interventional strategies if they can produce sufficiently precise estimations of the generated lesion for a given ablation protocol. This requires an accurate description of the catheter tip and the cardiac tissue. In particular, the deformation of the tissue under the catheter pressure during the ablation is an important aspect that is overlooked in the existing literature, which resorts to a sharp insertion of the catheter into an undeformed geometry. As the lesion size depends on the power dissipated in the tissue and the latter depends on the percentage of the electrode surface in contact with the tissue itself, the sharp insertion geometry has the tendency to overestimate the lesion obtained, which is a consequence of the tissue temperature rise overestimation. In this paper, we introduce a full 3D computational model that takes into account the tissue elasticity and is able to capture tissue deformation and realistic power dissipation in the tissue. Numerical results in FEniCS-HPC are provided to validate the model against experimental data and to compare the lesions obtained with the new model and with the classical ones featuring a sharp electrode insertion in the tissue.

  • 42.
    Nguyen, Van Dang
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Leoni, Massimiliano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Dancheva, Tamara
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Jansson, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Hoffman, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Wassermann, Demian
    Li, Jing-Rebecca
    Portable simulation framework for diffusion MRI2019In: Journal of magnetic resonance, ISSN 1090-7807, E-ISSN 1096-0856, Vol. 309, article id 106611Article in journal (Refereed)
    Abstract [en]

    The numerical simulation of the diffusion MRI signal arising from complex tissue micro-structures is helpful for understanding and interpreting imaging data as well as for designing and optimizing MRI sequences. The discretization of the Bloch-Torrey equation by finite elements is a more recently developed approach for this purpose, in contrast to random walk simulations, which has a longer history. While finite elements discretization is more difficult to implement than random walk simulations, the approach benefits from a long history of theoretical and numerical developments by the mathematical and engineering communities. In particular, software packages for the automated solutions of partial differential equations using finite elements discretization, such as FEniCS, are undergoing active support and development. However, because diffusion MRI simulation is a relatively new application area, there is still a gap between the simulation needs of the MRI community and the available tools provided by finite elements software packages. In this paper, we address two potential difficulties in using FEniCS for diffusion MRI simulation. First, we simplified software installation by the use of FEniCS containers that are completely portable across multiple platforms. Second, we provide a portable simulation framework based on Python and whose code is open source. This simulation framework can be seamlessly integrated with cloud computing resources such as Google Colaboratory notebooks working on a web browser or with Google Cloud Platform with MPI parallelization. We show examples illustrating the accuracy, the computational times, and parallel computing capabilities. The framework contributes to reproducible science and open-source software in computational diffusion MRI with the hope that it will help to speed up method developments and stimulate research collaborations.

  • 43.
    Petras, Argyrios
    et al.
    BCAM, Bilbao 48009, Spain..
    Leoni, Massimiliano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Guerra, Jose M.
    Hosp Santa Creu & Sant Pau, CIBERCV, Barcelona 08041, Spain..
    Jansson, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Gerardo-Giorda, Luca
    BCAM, Bilbao 48009, Spain..
    Tissue Drives Lesion: Computational Evidence of Interspecies Variability in Cardiac Radiofrequency Ablation2019In: FUNCTIONAL IMAGING AND MODELING OF THE HEART, FIMH 2019 / [ed] Coudiere, Y Ozenne, V Vigmond, E Zemzemi, N, SPRINGER INTERNATIONAL PUBLISHING AG , 2019, p. 139-146Conference paper (Refereed)
    Abstract [en]

    Radiofrequency catheter ablation (RFCA) is widely used for the treatment of various types of cardiac arrhythmias. Typically, the efficacy and the safety of the ablation protocols used in the clinics are derived from tests carried out on animal specimens, including swines. However, these experimental findings cannot be immediately translated to clinical practice on human patients, due to the difference in the physical properties of the types of tissue. Computational models can assist in the quantification of this variability and can provide insights in the results of the RFCA for different species. In this work, we consider a standard ablation protocol of 10 g force, 30 W power for 30 s. We simulate its application on a porcine cardiac tissue, a human ventricle and a human atrium. Using a recently developed computational model that accounts for the mechanical properties of the tissue, we explore the onset and the growth of the lesion along time by tracking its depth and width, and we compare the lesion size and dimensions at the end of the ablation.

  • 44.
    Filipović, Marko
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). Bernstein Center Freiburg and Faculty of Biology, University of Freiburg, Germany.
    Characterisation of inputs and outputs of striatal medium spiny neurons in health and disease2019Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Striatal medium spiny neurons (MSNs) play a crucial role in various motor and cognitive functions. They are separated into those belonging to the direct pathway (dMSNs) and the indirect pathway (iMSNs) of the basal ganglia, depending on whether they express D1 or D2 type dopamine receptors, respectively. In this thesis I investigated the input processing of both MSN types, the characteristics of dMSN outputs, and the effect that aberrant iMSN activity has on the subthalamic nucleus-globus pallidus externa (STN-GPe) network.In order to verify a previous result from a computational study claiming that dMSNs should receive either more or stronger total input than iMSNs, I performed an analysis of in vivo whole-cell MSN recordings in healthy and dopamine (DA) depleted (6OHDA) anesthetized mice. To test this prediction, I compared subthreshold membrane potential fluctuations and spike-triggered average membrane potentials of the two MSN types. I found that dMSNs in healthy mice exhibited considerably larger fluctuations over a wide frequency range, as well as significantly faster  depolarization towards the spiking threshold than iMSNs. However, these effects were not present in recordings from 6OHDA animals. Together, these findings strongly suggest that dMSNs do  receive stronger total input than iMSNs in healthy condition.I also examined how different concentrations of dopamine affect neural trial-by-trial (or response) variability in a biophysically detailed compartmental model of a direct-pathway MSN.  Some of the sources of trial-by-trial variability include synaptic noise, neural refractory period, and ongoing neural activity. The focus of this study was on the effects of two particular  properties of the synaptic input: correlations of synaptic input rates, and the balance between excitatory and inhibitory inputs (E-I balance). The model demonstrates that dopamine is in  general a significant diminisher of trial-by-trial variability, but that its efficacy depends on the properties of synaptic input. Moreover, input rate correlations and changes in the E-I balance by themselves also proved to have a marked impact on the response variability.Finally, I investigated the beta-band phase properties of the STN-GPe network, known for its exaggerated beta-band oscillations during Parkinson’s disease (PD). The current state-of-the-art  computational model of the network can replicate both transient and persistent beta oscillations, but fails to capture the beta-band phase alignment between the two nuclei as seen in human  recordings. This was particularly evident during simulations of the PD condition, where STN or GPe were receiving additional stimulation in order to induce pathological levels of beta-band  activity. Here I show that by manipulating the percentage of the neurons in either population that receives stimulation it is possible to increase STN-GPe phase difference heterogeneity.  Furthermore, a similar effect can be achieved by adjusting synaptic transmission delays between the two populations. Quantifying the difference between human recordings and network  simulations, I provide the set of parameters for which the model produces the greatest correspondence with experimental results.

  • 45.
    Wendt, Fabian
    et al.
    NREL, 15013 Denver West Pkwy, Golden, CO 80401 USA..
    Nielsen, Kim
    Ramboll Grp AS, Hannemanns Alle 53, DK-2300 Copenhagen S, Denmark.;Aalborg Univ, Dept Civil Engn, Thomas Mann Vej 23, DK-9220 Aalborg O, Denmark..
    Hoffman, Johan
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Numerical Analysis, NA.
    Jansson, Johan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Leoni, Massimiliano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Yasutaka, Imai
    Saga Univ, Inst Ocean Energy, Honjo 1, Saga 8408502, Japan..
    Ocean Energy Systems Wave Energy Modelling Task: Modelling, Verification and Validation of Wave Energy Converters2019In: Journal of Marine Science and Engineering, E-ISSN 2077-1312, Vol. 7, no 11, article id 379Article in journal (Refereed)
    Abstract [en]

    The International Energy Agency Technology Collaboration Programme for Ocean Energy Systems (OES) initiated the OES Wave Energy Conversion Modelling Task, which focused on the verification and validation of numerical models for simulating wave energy converters (WECs). The long-term goal is to assess the accuracy of and establish confidence in the use of numerical models used in design as well as power performance assessment of WECs. To establish this confidence, the authors used different existing computational modelling tools to simulate given tasks to identify uncertainties related to simulation methodologies: (i) linear potential flow methods; (ii) weakly nonlinear Froude-Krylov methods; and (iii) fully nonlinear methods (fully nonlinear potential flow and Navier-Stokes models). This article summarizes the code-to-code task and code-to-experiment task that have been performed so far in this project, with a focus on investigating the impact of different levels of nonlinearities in the numerical models. Two different WECs were studied and simulated. The first was a heaving semi-submerged sphere, where free-decay tests and both regular and irregular wave cases were investigated in a code-to-code comparison. The second case was a heaving float corresponding to a physical model tested in a wave tank. We considered radiation, diffraction, and regular wave cases and compared quantities, such as the WEC motion, power output and hydrodynamic loading.

  • 46.
    Bruce, Neil J.
    et al.
    Heidelberg Inst Theoret Studies, Mol & Cellular Modeling Grp, Schloss Heidelberg, Germany..
    Narzi, Daniele
    Ecole Polytech Fed Lausanne, Inst Sci & Ingn Chim, CH-1015 Lausanne, Switzerland..
    Trpevski, Daniel
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Electrical Engineering and Computer Science (EECS).
    van Keulen, Siri C.
    Ecole Polytech Fed Lausanne, Inst Sci & Ingn Chim, CH-1015 Lausanne, Switzerland.;Stanford Univ, Dept Comp Sci, Stanford, CA 94305 USA..
    Nair, Anu G.
    Univ Zurich, Inst Mol Life Sci, Zurich, Switzerland..
    Rothlisberger, Ursula
    Ecole Polytech Fed Lausanne, Inst Sci & Ingn Chim, CH-1015 Lausanne, Switzerland..
    Wade, Rebecca C.
    Heidelberg Inst Theoret Studies, Mol & Cellular Modeling Grp, Schloss Heidelberg, Germany.;Heidelberg Univ, Ctr Mol Biol ZMBH, DKFZ ZMBH Alliance, Heidelberg, Germany.;Heidelberg Univ, Interdisciplinary Ctr Sci Comp IWR, Heidelberg, Germany..
    Carloni, Paolo
    Rhein Westfal TH Aachen, Dept Phys, Aachen, Germany.;Rhein Westfal TH Aachen, Dept Neurobiol, Aachen, Germany.;Forschungszentrum Julich, Inst Neurosci & Med INM 11, Julich, Germany.;Forschungszentrum Julich, Inst Neurosci & Med INM 9, Julich, Germany.;Forschungszentrum Julich, Inst Adv Simulat IAS 5, Julich, Germany..
    Hällgren Kotaleski, Jeanette
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Regulation of adenylyl cyclase 5 in striatal neurons confers the ability to detect coincident neuromodulatory signals2019In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 15, no 10, article id e1007382Article in journal (Refereed)
    Abstract [en]

    Author summary Adenylyl cyclases (ACs) are enzymes that can translate extracellular signals into the intracellular molecule cAMP, which is thus a 2nd messenger of extracellular events. The brain expresses nine membrane-bound AC variants, and AC5 is the dominant form in the striatum. The striatum is the input stage of the basal ganglia, a brain structure involved in reward learning, i.e. the learning of behaviors that lead to rewarding stimuli (such as food, water, sugar, etc). During reward learning, cAMP production is crucial for strengthening the synapses from cortical neurons onto the striatal principal neurons, and its formation is dependent on several neuromodulatory systems such as dopamine and acetylcholine. It is, however, not understood how AC5 is activated by transient (subsecond) changes in the neuromodulatory signals. Here we combine several computational tools, from molecular dynamics and Brownian dynamics simulations to bioinformatics approaches, to inform and constrain a kinetic model of the AC5-dependent signaling system. We use this model to show how the specific molecular properties of AC5 can detect particular combinations of co-occuring transient changes in the neuromodulatory signals which thus result in a supralinear/synergistic cAMP production. Our results also provide insights into the computational capabilities of the different AC isoforms. Long-term potentiation and depression of synaptic activity in response to stimuli is a key factor in reinforcement learning. Strengthening of the corticostriatal synapses depends on the second messenger cAMP, whose synthesis is catalysed by the enzyme adenylyl cyclase 5 (AC5), which is itself regulated by the stimulatory G alpha(olf) and inhibitory G alpha(i) proteins. AC isoforms have been suggested to act as coincidence detectors, promoting cellular responses only when convergent regulatory signals occur close in time. However, the mechanism for this is currently unclear, and seems to lie in their diverse regulation patterns. Despite attempts to isolate the ternary complex, it is not known if G alpha(olf) and G alpha(i) can bind to AC5 simultaneously, nor what activity the complex would have. Using protein structure-based molecular dynamics simulations, we show that this complex is stable and inactive. These simulations, along with Brownian dynamics simulations to estimate protein association rates constants, constrain a kinetic model that shows that the presence of this ternary inactive complex is crucial for AC5's ability to detect coincident signals, producing a synergistic increase in cAMP. These results reveal some of the prerequisites for corticostriatal synaptic plasticity, and explain recent experimental data on cAMP concentrations following receptor activation. Moreover, they provide insights into the regulatory mechanisms that control signal processing by different AC isoforms.

  • 47.
    Natesan, Dinesh
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). Manipal Academy of Higher Education, Manipal, 576104, India; National Centre for Biological Sciences, Tata Institute of Fundamental Research, GKVK campus, Bellary road, Bangalore, 560065, India .
    Saxena, Nitesh
    Tata Inst Fundamental Res, Natl Ctr Biol Sci, GKVK Campus,Bellary Rd, Bangalore 560065, Karnataka, India..
    Ekeberg, Örjan
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Sane, Sanjay P.
    Tata Inst Fundamental Res, Natl Ctr Biol Sci, GKVK Campus,Bellary Rd, Bangalore 560065, Karnataka, India..
    Tuneable reflexes control antennal positioning in flying hawkmoths2019In: Nature Communications, ISSN 2041-1723, E-ISSN 2041-1723, Vol. 10, article id 5593Article in journal (Refereed)
    Abstract [en]

    Complex behaviours may be viewed as sequences of modular actions, each elicited by specific sensory cues in their characteristic timescales. From this perspective, we can construct models in which unitary behavioural modules are hierarchically placed in context of related actions. Here, we analyse antennal positioning reflex in hawkmoths as a tuneable behavioural unit. Mechanosensory feedback from two antennal structures, Bohm's bristles (BB) and Johnston's organs (JO), determines antennal position. At flight onset, antennae attain a specific position, which is maintained by feedback from BB. Simultaneously, JO senses deflections in flagellum-pedicel joint due to frontal airflow, to modulate its steady-state position. Restricting JO abolishes positional modulation but maintains stability against perturbations. Linear feedback models are sufficient to predict antennal dynamics at various set-points. We modelled antennal positioning as a hierarchical neural-circuit in which fast BB feedback maintains instantaneous set-point, but slow JO feedback modulates it, thereby elucidating mechanisms underlying its robustness and flexibility.

  • 48.
    Chrysanthidis, Nikolaos
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). Faculty of Engineering, School of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece.
    Fiebig, Florian
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). Institute for Adaptive and Neural Computation, Edinburgh University, Edinburgh, EH8 9AB, United Kingdom.
    Lansner, Anders
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST). Department of Mathematics, Stockholm University, Stockholm, 10691, Sweden.
    Introducing double bouquet cells into a modular cortical associative memory model2019In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 47, no 2-3, p. 223-230Article in journal (Refereed)
    Abstract [en]

    We present an electrophysiological model of double bouquet cells and integrate them into an established cortical columnar microcircuit model that has previously been used as a spiking attractor model for memory. Learning in that model relies on a Hebbian-Bayesian learning rule to condition recurrent connectivity between pyramidal cells. We here demonstrate that the inclusion of a biophysically plausible double bouquet cell model can solve earlier concerns about learning rules that simultaneously learn excitation and inhibition and might thus violate Dale's principle. We show that learning ability and resulting effective connectivity between functional columns of previous network models is preserved when pyramidal synapses onto double bouquet cells are plastic under the same Hebbian-Bayesian learning rule. The proposed architecture draws on experimental evidence on double bouquet cells and effectively solves the problem of duplexed learning of inhibition and excitation by replacing recurrent inhibition between pyramidal cells in functional columns of different stimulus selectivity with a plastic disynaptic pathway. We thus show that the resulting change to the microcircuit architecture improves the model's biological plausibility without otherwise impacting the model's spiking activity, basic operation, and learning abilities.

  • 49.
    Wallden, Marcus
    et al.
    Osaka Univ, Grad Sch Informat Sci & Technol, Suita, Osaka 5650871, Japan..
    Markidis, Stefano
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Okita, Masao
    Osaka Univ, Grad Sch Informat Sci & Technol, Suita, Osaka 5650871, Japan..
    Ino, Fumihiko
    Osaka Univ, Grad Sch Informat Sci & Technol, Suita, Osaka 5650871, Japan..
    Memory Efficient Load Balancing for Distributed Large-Scale Volume Rendering Using a Two-Layered Group Structure2019In: IEICE transactions on information and systems, ISSN 0916-8532, E-ISSN 1745-1361, Vol. E102D, no 12, p. 2306-2316Article in journal (Refereed)
    Abstract [en]

    We propose a novel compositing pipeline and a dynamic load balancing technique for volume rendering which utilizes a two-layered group structure to achieve effective and scalable load balancing. The technique enables each process to render data from non-contiguous regions of the volume with minimal impact on the total render time. We demonstrate the effectiveness of the proposed technique by performing a set of experiments on a modern GPU cluster. The experiments show that using the technique results in up to a 35.7% lower worst-case memory usage as compared to a dynamic k-d tree load balancing technique, whilst simultaneously achieving similar or higher render performance. The proposed technique was also able to lower the amount of transferred data during the load balancing stage by up to 72.2%. The technique has the potential to be used in many scenarios where other dynamic load balancing techniques have proved to be inadequate, such as during large-scale visualization.

  • 50.
    Andreozzi, Emilio
    et al.
    Univ Naples Federico II, Dept Elect Engn & Informat Technol DIETI, Naples, Italy.;Ist Clin Sci Maugeri IRCCS, Dept Bioengn, Telese Terme Inst, Telese Terme, BN, Italy..
    Carannante, Ilaria
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    D'Addio, Giovanni
    Ist Clin Sci Maugeri IRCCS, Dept Bioengn, Telese Terme Inst, Telese Terme, BN, Italy..
    Cesarelli, Mario
    Univ Naples Federico II, Dept Elect Engn & Informat Technol DIETI, Naples, Italy.;Ist Clin Sci Maugeri IRCCS, Dept Bioengn, Telese Terme Inst, Telese Terme, BN, Italy..
    Balbi, Pietro
    Ist Clin Sci Maugeri IRCCS, Lab Computat Neurophysiol, Telese Terme Inst, Telese Terme, BN, Italy..
    Phenomenological models of Na(V)1.5. A side by side, procedural, hands-on comparison between Hodgkin-Huxley and kinetic formalisms2019In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 9, article id 17493Article in journal (Refereed)
    Abstract [en]

    Computational models of ion channels represent the building blocks of conductance-based, biologically inspired models of neurons and neural networks. Ion channels are still widely modelled by means of the formalism developed by the seminal work of Hodgkin and Huxley (HH), although the electrophysiological features of the channels are currently known to be better fitted by means of kinetic Markov-type models. The present study is aimed at showing why simplified Markov-type kinetic models are more suitable for ion channels modelling as compared to HH ones, and how a manual optimization process can be rationally carried out for both. Previously published experimental data of an illustrative ion channel (Na(V)1.5) are exploited to develop a step by step optimization of the two models in close comparison. A conflicting practical limitation is recognized for the HH model, which only supplies one parameter to model two distinct electrophysiological behaviours. In addition, a step by step procedure is provided to correctly optimize the kinetic Markov-type model. Simplified Markov-type kinetic models are currently the best option to closely approximate the known complexity of the macroscopic currents of ion channels. Their optimization can be achieved through a rationally guided procedure, and allows to obtain models with a computational burden that is comparable with HH models one.

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