Abstract: The MapReduce community is progressively replacing the classic Hadoop with Yarn, the second-generation Hadoop (MapReduce 2.0). This transition is being made due to many reasons, but primarily because of some scalability drawbacks of the classic Hadoop. The new framework has appropriately addressed this issue and is being praised for its multi-functionality. In this paper we carry out a probabilistic analysis that emphasizes some reliability concerns of Yarn at the job master level. This is a critical point, since the failures of a job master involves the failure of all the workers managed by such a master. In this paper, we propose Diarchy, a novel system for the management of job masters. Its aim is to increase the reliability of Yarn, based on the sharing and backup of responsibilities between two masters working as peers. The evaluation results show that Diarchy outperforms the reliability performance of Yarn in different setups, regardless of cluster size, type of job, or average failure rate and suggest a positive impact of this approach compared to the traditional, single-master Hadoop architecture.

Abstract: OpenFOAM is a powerful Open-Source (GPLv3) Computational Fluid Dynamics tool box with a rising adoption in both academia and industry due to its continuously growing set of features and the lack of license costs. Our previously developed discrete adjoint version of OpenFOAM allows us to calculate derivatives of arbitrary objectives with respect to a potentially very large number of input parameters at a relative (to a single primal flow simulation) computational cost which is independent of that number. Discrete adjoint OpenFOAM enables us to run gradient-based methods such as topology optimization efficiently. Up until recently only a serial version was available limiting both the computing performance and the amount of memory available for the solution of the problem. In this paper we describe a first parallel version of discrete adjoint OpenFOAM based on our adjoint MPI library.

Abstract: Exascale studies project reliability challenges for future HPC systems. We propose the Global View Resilience (GVR) system, a library that enables applications to add resilience in a portable, application-controlled fashion using versioned distributed arrays. We describe GVR’s interfaces to distributed arrays, versioning, and cross-layer error recovery. Using several large applications (OpenMC, preconditioned conjugate gradient (PCG) solver, ddcMD, and Chombo), we evaluate the programmer effort to add resilience. The required changes are small (<2% LOC), localized, and machine-independent, requiring no software architecture changes. We also measure the overhead of adding GVR versioning and show that generally overheads <2 % are achieved. Thus, we conclude that GVR’s interfaces and implementation are flexible, portable, and create a gentle-slope path to tolerate growing error rates in future systems.

Abstract: High throughput computing (HTC) has aided the scientific community in the analysis of vast amounts of data and computational jobs in distributed environments. To manage these large workloads, several systems have been developed to efficiently allocate and provide access to distributed resources. Many of these systems rely on job characteristics estimates (e.g., job runtime) to characterize the workload behavior, which in practice is hard to obtain. In this work, we perform an exploratory analysis of the CMS experiment workload using the statistical recursive partitioning method and conditional inference trees to identify patterns that characterize particular behaviors of the workload. We then propose an estimation process to predict job characteristics based on the collected data. Experimental results show that our process estimates job runtime with 75% of accuracy on average, and produces nearly optimal predictions for disk and memory consumption.

Abstract: Modeling workflow performance is crucial for finding optimal configuration parameters and optimizing execution times. We apply the method of surrogate-based modeling to performance tuning of MapReduce jobs. We build a surrogate model defined by a multivariate polynomial containing a variable for each parameter to be tuned. For illustrative purposes, we focus on just two parameters: the number of parallel mappers and the number of parallel reducers. We demonstrate that an accurate performance model can be built sampling a small set of the parameter space. We compare the accuracy and cost of building the model when using different sampling methods as well as when using different modeling approaches. We conclude that the surrogate-based approach we describe is both less expensive in terms of sampling time and more accurate than other well-known tuning methods.

Abstract: During the last decade the techniques of complex network analysis have found application in climate research. The main idea consists in embedding the characteristics of climate variables, e.g., temperature, pressure or rainfall, into the topology of complex networks by appropriate linear and nonlinear measures. Applying such measures on climate time series leads to defining links between their corresponding locations on the studied region, whereas the locations are the network’s nodes. The resulted networks, consequently, are analysed using the various network analysis tools present in literature in order to get a better insight on the processes, patterns and interactions occurring in climate system. In this regard we present ClimNet; a complete set of software tools to construct climate networks based on a wide range of linear (cross correlation) and nonlinear (Information theoretic) measures. The presented software will allow the construction of large networks’ adjacency matrices from climate time series while supporting functions to tune relationships to different time-scales by means of symbolic ordinal analysis. The provided tools have been used in the production of various original contributions in climate research. This work presents an in-depth description of the implemented statistical functions widely used to construct climate networks. Additionally, a general overview of the architecture of the developed software is provided as well as a brief analysis of application examples.

Abstract: This paper investigates the relationship between the underlying complexity of urban agent-based models and the performance of optimisation algorithms. In particular, we address the problem of optimal green space allocation within a densely populated urban area. We find that a simple monocentric urban growth model may not contain enough complexity to be able to take complete advantage of advanced optimisation techniques such as Genetic Algorithms (GA) and that, in fact, simple greedy baselines can find a better policy for these simple models. We then turn to more realistic urban models and show that the performance of GA increases with model complexity and uncertainty level.

Abstract: Carbon nanotubes possess many interesting properties, which make them a promising material for a variety of applications. In this paper, we present a unified framework for the simulation of mechanical behavior of carbon nanotubes. It allows the creation, simulation and visualization of these structures, extending previous work by the research group ”MISMO” at TU Darmstadt. In particular, we develop and integrate a new iterative solving procedure, employing the conjugate gradient method, that drastically reduces the memory consumption in comparison to the existing approaches. The increase in operations for the memory saving approach is partially offset by a well scaling shared-memory parallelization. In addition the hotspots in the code have been vectorized. Altogether, the resulting simulation framework enables the simulation of complex carbon nanotubes on commodity multicore desktop computers.

Abstract: Mechatronic systems play a significant role in different types of industry, especially in transportation, aerospace, automotive and manufacturing. Although their multidisciplinary nature provides enormous functionalities, it is still one of the substantial challenges which frequently impede their design process. Notably, the conceptual design phase aggregates various engineering disciplines, project and business management fields, where different methods, modeling languages and software tools are applied. Therefore, an integrated environment is required to intimately engage the different domains together. This paper outlines a model-based research approach for an integrated conceptual design evaluation of mechatronic systems using SysML. Particularly, the state of the art is highlighted, most important challenges, remaining problems in this field and a novel solution is proposed, named SysDICE, combining model based system engineering and artificial intelligence techniques to support for achieving efficient design.

Abstract: Computational Science tackles complex problems by definition. These problems concern people not only in large scale, but in their day-to-day life. With the development of computing facilities, novel application areas can legitimately benefit from the existing experience in the field. Nevertheless, the lack of reusability, the growing in complexity, and the “computing-oriented” nature of the actual solutions call for several improvements. Among these, raising the level of abstraction is the one we address in this paper. As an illustration we can mention the problem of the validity of the experimentations which depends on the validity of the defined programs (bugs not in the experiment and data but in the simulators/validators!). This raise the needs for leveraging on knowledge / expertise. In the software and systems modeling community, research on domain-specific modeling languages (DSMLs) is focused since the last decade on providing technologies for developing languages and tools that allow domain experts to develop system solutions efficiently. In this vision paper, based on concrete experiments, we claim that DSMLs can bridge the gap between the (problem) space in which scientist work and the implementation (programming) space. Incorporating domain-specific concepts and high-quality development experience into DSMLs can significantly improve scientist productivity and experimentation quality.

Abstract: This paper presents a space mapping algorithm for airfoil shape optimization enhanced with adjoint sensitivities. The surrogate-based algorithm utilizes low-cost derivative information obtained through adjoint sensitivities to improve the space mapping matching between a high-fidelity airfoil model, evaluated through expensive CFD simulations, and its fast surrogate. Here, the airfoil surrogate model is constructed though low-fidelity CFD simulations. As a result, the design process can be performed at a low computational cost in terms of the number of high-fidelity CFD simulations. The adjoint sensitivities are also exploited to speed up the surrogate optimization process. Our method is applied to a constrained drag minimization problem in two-dimensional inviscid transonic flow. The problem is solved for several low-fidelity model termination criteria. The results show that when compared with direct gradient-based optimization with adjoint sensitivities, the proposed approach requires 49-78% less computational cost while still obtaining a comparable airfoil design.

Abstract: This paper introduces a new sampling technique called Opposite-Center Learning (OCL) intended for convergence speedup of meta-heuristic optimization algorithms. It comprises an extension of Opposition-Based Learning (OBL), a simple scheme that manages to boost numerous optimization methods by considering the opposite points of candidate solutions. In contrast to OBL, OCL has a theoretical foundation – the opposite center point is defined as the optimal choice in pair-wise sampling of the search space given a random starting point. A concise analytical background is provided. Computationally the opposite center point is approximated by a lightweight Monte Carlo scheme for arbitrary dimension. Empirical results up to dimension 20 confirm that OCL outperforms OBL and random sampling: the points generated by OCL have shorter expected distances to a uniformly distributed global optimum. To further test its practical performance, OCL is applied to differential evolution (DE). This novel scheme for continuous optimization named Opposite-Center DE (OCDE) employs OCL for population initialization and generation jumping. Numerical experiments on a set of benchmark functions for dimensions 10 and 30 reveal that OCDE on average improves the convergence rates by 38% and 27% compared to the original DE and the Opposition-based DE (ODE), respectively, while remaining fully robust. Most promising are the observations that the accelerations shown by OCDE and OCL increase with problem dimensionality.

Abstract: Coherent x-ray diffractive imaging is a novel imaging technique that utilizes phase retrieval and nonlinear optimization methods to image matter at nanometer scales. We explore how the convergence properties of a popular phase retrieval algorithm, Fienup’s HIO, behave by introducing a reduced dimensionality problem allowing us to visualize convergence to local minima and the globally optimal solution. We then introduce generalizations of HIO that improve upon the original algorithm’s ability to converge to the globally optimal solution.

Abstract: A methodology for a rapid design optimization of integrated photonic couplers is presented. The proposed technique exploits variable-fidelity electromagnetic (EM) simulation models, additive response correction for accommodating the discrepancies between the EM models of various fidelities, and local response surface approximations for a fine tuning of the final design. A specific example of a 1,555 nm coupler is considered with an optimum design obtained at a computational cost corresponding to about 24 high-fidelity EM simulations of the structure.

Abstract: Discriminative learning of Restricted Boltzmann Machines has been recently introduced as an alternative to provide a self-contained approach for both unsupervised feature learning and classification purposes. However, one of the main problems faced by researchers interested in such approach concerns with a proper selection of its parameters, which play an important role in its final performance. In this paper, we introduced some meta-heuristic techniques for this purpose, as well as we showed they can be more accurate than a random search, that is commonly used by some works.

Abstract: In this paper, we formulate the permeability inverse problem in the Bayesian framework using total variation (TV) and $\ell_p$ regularization prior. We use the Markov Chain Monte Carlo (MCMC) method for sampling the posterior distribution to solve the ill-posed inverse problem. We present simulations to estimate the distribution for each pixel for the image reconstruction of the absolute permeability.

Abstract: This paper proposes a new enhanced velocity method to directly construct a flux-continuous velocity approximation with multipoint flux mixed finite element method on subdomains. This gives an efficient way to perform simulations on multiblock domains with non-matching hexahedral grids. We develop a reasonable assumption on geometry, discuss implementation issues, and give several numerical results with slightly compressible single phase flow.

Abstract: In this work, a numerical scheme is implemented to solve Stokes equations based on cell-centered finite difference over staggered grid. In this scheme, all the difference operations have been vectored thereby eliminating loops. This is particularly important when using programming languages that require interpretations, e.g., Matlab and Python. Using this scheme, the execution time becomes significantly smaller compared with non-vectored operations and also become comparable with those languages that require no repeated interpretations like FORTRAN, C, etc. This technique has also been applied to Navier-Stokes equations under laminar flow conditions.

Abstract: In the development of a numerical model for aeroacoustic problems, two main issues arise: which level of physical approximation to adopt and which numerical scheme is the most appropriate. It is possible to consider a hierarchy of physical aproximations, ranging from the wave equation, without or with convective effects, to the linearized Euler and Navier-Stokes equations, as well as a wide range of high-order numerical schemes, ranging from compact finite difference schemes to the discontinuous Galerkin method (DGM) for unstructured grids. For problems in complex geometries, significant hydrodynamic-acoustic interactions, coupling acoustic waves and vortical modes, may occur. For example in ducts with sudden changes of area where flow separation occurs in correspondence of sharp edges with a consequent generation of vorticity for viscous effects. To correctly model this coupling, the Navier-Stokes equations, linearized with respect to a representative mean flow, must be solved. The formulation based on Linearized Navier-Stokes (LNS) equations is suitable to deal with problems involving such hydrodynamic-acoustic interactions. The occurrence of geometrical complexities, such as sharp edges, where acoustic energy is transferred into the vortical modes for viscous effects, requires an highly accurate numerical scheme with non only reduced dispersive properties, to accurate model the wave propagation, but also providing a very low level of numerical dissipation on unstructured grids. The DGM is the most appropriate numerical scheme satisfying these requirements. The objective of the present work is to develop an efficient numerical solution of the LNS equations, based on a DGM on unstructured grids. To our knowledge, there is only one work dealing with the solution of the LNS for aeroacoustics where the equations are solved in the frequency domain. In this work we develop the method in the time domain. The non-dispersive and non-diffusive nature of acoustic waves propagating over long distances forces us to adopt highly accurate numerical methods. DGM is one of the most promising scheme due to its intrinsic stability and to its capability to treat unstructured grids. Both advantages make this method well suited for problems characterized by wave propagation phenomena in complex geometries. The main disadvantage of DGM is the high computational requirements because the discontinuous character of the method which adds extra nodes on the interfaces between cells respect to a standard continuous Galerkin Method (GM). Techniques of optimization of the DGM in the case of the Navier-Stokes equations, to reduce the computational effort, are currently object of intense research. At our knowledge, no similar effort is made in the context of the solution of the LNS equations. The LNS equations are derived and the DGM is presented. Preliminary results for the case of the scattering of plane waves traveling in a duct with a sudden area expansion and a comparison between LEE and LNS calculations of vortical modes, are presented.

Abstract: The organization of an ensemble of initial perturbations by a nonlinear dynamical system can produce highly non-Gaussian patterns, evidence of which is clearly observed in position-amplitude-scale features of coherent fluids. The true distribution of the ensemble is unknown, in part because models are in error and imperfect. A variety of distributions have been proposed in the context of Bayesian inference, including for example, mixture and kernel models. We contend that seeking posterior modes in non-Gaussian inference is fraught with heightened sensitivity to model error and demonstrate this fact by showing that a large component of the total variance remains unaccounted for as more modes emerge. Further, we show that in the presence of bias, this unaccounted variance slows convergence and produces distributions with lower information that require extensive auxiliary clean up procedures such as resampling. These procedures are difficult in large-scale problems where ensemble members may be generated through myriad schemes. We show that by treating the estimation problem entailed as a regression machine, multiple objectives can be incorporated in inference. The relative importance of these objectives can morph over time and can be dynamically adjusted by the data. In particular, we show that both variance reduction and nonlinear modes can be targeted using a stacked cascade generalization. We demonstrate this approach by constructing a new sequential filter called the Boosted Mixture Ensemble Filter and illustrating this on a lorenz system.

Abstract: This paper presents one approach to determining the hazard threat to a locale due to a large volcanic avalanche. The methodology employed includes large-scale numerical simulations, field data reporting the volume and runout of flow events, and a detailed statistical analysis of uncertainties in the modeling and data. The probability of a catastrophic event impacting a locale is calculated, together with a estimate of the uncertainty in that calculation. By a careful use of simulations, a hazard map for an entire region can be determined. The calculation can be turned around quickly, and the methodology can be applied to other hazard scenarios.

Abstract: This paper introduces a numerically-stable multiscale scheme to efficiently generate probabilistic hazard maps for volcanic ash transport using models of transport, dispersion and wind. The scheme relies on graph-based algorithms and low-rank approximations of the adjacency matrix of the graph. This procedure involves representing both the parameter space and physical space by a weighted graph. A combination of clustering and low rank approximation is then used to create a good approximation of the original graph. By performing a multiscale data sampling, a well-conditioned basis of a low rank Gaussian kernel matrix, is identified and used for out-of-sample extensions used in generating the hazard maps.

Abstract: The effects of forest fires cause a widespread devastation throughout the world every year. A good prediction of fire behavior can help on coordination and management of human and material resources in the extinction of these emergencies. Given the high uncertainty of fire behavior and the difficulty of extracting information required to generate accurate predictions, one system able to adapt to fire dynamics considering the uncertainty of the data is necessary. In this work two different systems based on Dynamic Data Driven Application are applied and a new probabilistic method based on the combination of both approaches is presented. This new method uses the computational power provided by high performance computing systems to adapt the chances in these kind of dynamic environments.

Abstract: Wildfires are critical for ecosystems in many geographical regions. However, our current urbanized existence in these environments is inducing this ecological balance to evolve into a different dynamic leading to the biggest fires in history. Wildfire wind speeds and directions change in an instant, and first responders can only be effective if they take action as quickly as the conditions change. What is lacking in disaster management today is a system integration of real-time sensor networks, satellite imagery, near-real time data management tools, wildfire simulation tools, and connectivity to emergency command centers before, during and after a wildfire. As a first time example of such an integrated system, the WIFIRE project is building an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction and visualization of wildfire behavior. This paper summarizes the approach and early results of the WIFIRE project to integrate networked observations, e.g., heterogeneous satellite data and real-time remote sensor data with computational techniques in signal processing, visualization, modeling and data assimilation to provide a scalable, technological, and educational solution to monitor weather patterns to predict a wildfire’s Rate of Spread.

Abstract: The role of Computational Science research methods teaching to science students at PG level is to enhance their research profile developing their abilities to investigate complex problems, analyse the resulting data and use adequately HPC environments and tools for computation and visualisation. The paper analyses the current state and proposes a program that encompass mathematical modelling, data science, advanced algorithms development, parallel programming and visualisation tools. It also gives examples of specific scientific domains with explicitly taught and embedded Computational Science subjects.

Abstract: In response to growing demands of society for experts trained in computational skills applied to various domains, the School of Computer Science at the University of Guelph is creating a new approach to doctoral studies called an Interdisciplinary PhD in Computational Sciences. The program is designed to appeal to candidates with strong backgrounds in either computer science or an application discipline who are not necessarily seeking a traditional academic career. Thesis based, it features minimal course requirements and short duration, with the student’s research directed by co-advisors from computer science and the application discipline. The degree program’s rationale and special characteristics are described. Related programs in Ontario and reception of this innovative proposal at the institutional level are discussed.

Abstract: While we are witnessing a transition from petascale to exascale computing, we experience, when teaching students and scientists to adopt distributed computing infrastructures for computational sciences, what Geoffrey A. Moore once coined the chasm between the visionaries in computational sciences and the early majority of scientific pragmatists. Using the EU-funded DRIHM project (Distributed Research Infrastructure for Hydro-Meteorology) as a case study, we see that innovative research infrastructures have difficulties to be accepted by the scientific pragmatists: The infrastructure services are not yet "mainstream". Excellence in workforces in computational sciences, however, can only be achieved if the tools are not only available but also used. In this paper we show for DRIHM how the chasm exhibits and how it can be crossed.

Abstract: Mathematical modelling is a difficult skill to acquire and transfer. In order to succeed in transferring the ability to model the observable world, the environment in which modelling is taught should resemble as much as possible the real environment in which students will leave and work. We devised a learning strategy based on modelling environmental variables in order to link weather conditions to weather emergencies by pollutants in the atmosphere of Monterrey, Mexico, metropolitan area. We structure course topics around a single comprehensive and integrative project. The objective of the project is to create a model that will predict behavior of existing phenomena using real data. In this case, we used data collected by weather stations. This data consists of weather information such as temperature, pressure, humidity, wind speed and the like. And, it also contains information about pollutants such as O3, CO2, CO, SO2, NOx, particles, etc. Students follow a procedure consisting for 4 stages. In the first stage they analyze the data; try to reduce dimensionality, link weather variables to contaminants and determine characteristic behaviours. In the second stage, students interpolate missing data and project component data to a 2D map of the metro area. In the third stage students create the mathematical model by carrying out curve fitting through least squares technique. In the third stage, students solve the model by finding roots, solving systems of equations, solving differential equations or integrating. The final deliverable is to determine under which weather conditions there can be an environmental contingency that put people’s health in danger. Class topics are taught in the order necessary to carry out the project. Any necessary knowledge required for the project not contemplated by course syllabus is carried out through team presentations with worked-out examples. Analysis of the strategy is presented as well as preliminary results.

Abstract: TBD

Abstract: TBD

Abstract: The aim of this workshop is to integrate results of different domains of computer science, computational science and mathematics. We invite papers oriented toward simulations, either hard simulations by means of finite element or finite difference methods, or soft simulations by means of evolutionary computations, particle swarm optimization and other. The workshop is most interested in simulations performed by using agent-oriented systems or by utilizing adaptive algorithms, but simulations performed by other kind of systems are also welcome. Agent-oriented system seems to be the attractive tool useful for numerous domains of applications. Adaptive algorithms allow significant decrease of the computational cost by utilizing computational resources on most important aspect of the problem. This year following the challenges of ICCS 2015 theme "Computational Science at the Gates of Nature" we invite submissions using techniques dealing with large simulations, e.g. agents based algorithms dealing with big data, model reduction techniques for large problems, fast solvers for large three dimensional simulations, etc. To give - rather flexible - guidance in the subject, the following, more detailed, topics are suggested. These of theoretical brand, like: (a) multi-agent systems in high-performance computing, (b) efficient adaptive algorithms for big problems, (c) low computational cost adaptive solvers, (d) agent-oriented approach to adaptive algorithms, (e) model reduction techniques for large problems, (f) mathematical modeling and asymptotic analysis of large problems, (g) finite element or finite difference methods for three dimensional or non-stationary problems, (h) mathematical modeling and asymptotic analysis. And those with stress on application sphere: (a) agents based algorithms dealing with big data, (b) application of adaptive algorithms in large simulation, (c) simulation and large multi-agent systems, (d) application of adaptive algorithms in three dimensional finite element and finite difference simulations, (e) application of multi-agent systems in computational modeling, (f) multi-agent systems in integration of different approaches.

Abstract: In this work, we present a novel isogeometric analysis discretization for the Navier-Stokes-Cahn-Hilliard equation, which uses divergence-conforming spaces. Basis functions generated with this method can have higher-order continuity, and allow to directly discretize the higher-order operators present in the equation. The discretization is implemented in PetIGA-MF, a high-performance framework for discrete differential forms. We present solutions in a two-dimensional annulus, and model spinodal decomposition under shear flow.

Abstract: Cylindrical Petri dishes embedded in a rectangular waveguide and exposed to a polarized electromagnetic wave are often used to grow cell cultures. To guarantee the success of these cultures, it is necessary to enforce that the specific absorption rate distribution is sufficiently high and uniform over the Petri dish. Accurate numerical simulations are needed to design such systems. These simulations constitute a challenge due to the strong discontinuity of electromagnetic parameters of the materials involved, the relative low value of field within the dish cultures compared with the rest of the domain, and the presence of the meniscus shape developed at the liquid/solid interface. The latter greatly increases the level of complexity of the model in terms of geometry and the intensity of the gradients/singularities of the field solution. In here, we employ a three-dimensional (3D) $hp$-adaptive finite element method using isoparametric elements to obtain highly accurate simulations. We analyse the impact of the geometrical modeling of the meniscus shape cell culture in the $hp$-adaptivity. Numerical results concerning the convergence history of the error indicate the numerical difficulties arisen due to the presence of a meniscus-shaped object. At the same time, the resulting energy distribution shows that to consider such meniscus shape is essential to guarantee the success of the cell culture from the biological point of view.

Abstract: Scientific workflows in clouds have been successfully used for automation of large-scale computations, but so far they were applied to the loosely-coupled problems, where most workflow tasks can be processed independently in parallel and do not require high volume of communication. The multi-frontal solver algorithm for finite element meshes can be represented as a workflow, but the fine granularity of resulting tasks and the large communication to computation ratio makes it hard to execute it efficiently in loosely-coupled environments such as the Infrastructure-as-a-Service clouds. In this paper, we hypothesize that there exists a class of meshes that can be effectively decomposed into a workflow and mapped onto a cloud infrastructure. To show that, we have developed a workflow-based multi-frontal solver using the HyperFlow workflow engine, which comprises workflow generation from the elimination tree, analysis of the workflow structure, task aggregation based on estimated computation costs, and distributed execution using a~dedicated worker service that can be deployed in clouds or clusters. The results of our experiments using the workflows of over 10,000 tasks indicate that after task aggregation the resulting workflows of over 100 tasks can be efficiently executed and the overheads are not prohibitive. These results lead us to conclusions that our approach is feasible and gives prospects for providing a generic workflow-based solution using clouds for problems typically considered as requiring HPC infrastructure.

Abstract: We present an application of Ant Colony Optimisation (ACO) to simulate socio-cognitive features of a population. We incorporated perspective taking ability to generate three different proportions of ant colonies: Control Sample, High Altercentricity Sample, and Low Altercentricity Sample. We simulated their performances on the Travelling Salesman Problem and compared them with the classic ACO. Results show that all three 'cognitively enabled' ant colonies require less time than the classic ACO. Also, though the best solution is found by the classic ACO, the Control Sample finds almost as good a solution but much faster. This study is offered as an example to illustrate an easy way of defining inter-individual interactions based on stigmergic features of the environment.

Abstract: The Perdew-Zunger self-interaction correction to DFT energy functionals can improve the accuracy of calculated results in many respects. The long range effective potential for the electrons then has the correct -1/r dependence so Rydberg excited states of molecules and clusters of molecules can be accurately treated [1,2]. Also, localized electronic states are brought down in energy so defects in semi-conductors and insulators with defect states in the band gap can be characterized [3,4]. The calculations are, however, more challenging since the energy functional is no longer unitary invariant and each step in the self-consistent procedure needs to include an inner loop where unitary optimization is carried out [5,6]. As a result, the calculations produce a set of optimal orbitals which are generally localized and correspond well to chemical intuition. It has become evident that the optimal orbital need to be complex valued functions [7]. If they are restricted to real valued functions, the energy of atoms and molecules is less accurate and structure of molecules can even be incorrect [8]. [1] 'Self-interaction corrected density functional calculations of Rydberg states of molecular clusters: N,N-dimethylisopropylamine', H. Gudmundsdóttir, Y. Zhang, P. M. Weber and H. Jónsson, J. Chem. Phys. 141, 234308 (2014). [2] 'Self-interaction corrected density functional calculations of molecular Rydberg states', H. Gudmundsdóttir, Y. Zhang, P. M. Weber and H. Jónsson, J. Chem. Phys. 139, 194102 (2013). [3] `Simulation of Surface Processes', H. Jónsson, Proceedings of the National Academy of Sciences 108, 944 (2011). [4] 'Solar hydrogen production with semiconductor metal oxides: New directions in experiment and theory', Á. Valdés et al., Phys. Chem. Chem. Phys. 14, 49 (2012). [5] 'Variational, self-consistent implementation of the Perdew–Zunger self-interaction correction with complex optimal orbitals', S. Lehtola and H. Jónsson, Journal of Chemical Theory and Computation 10, 5324 (2014). [6] 'Unitary Optimization of Localized Molecular Orbitals', S. Lehtola and H. Jónsson, Journal of Chemical Theory and Computation 9, 5365 (2013). [7] 'Importance of complex orbitals in calculating the self-interaction corrected ground state of atoms', S. Klüpfel, P. J. Klüpfel and H. Jónsson, Phys. Rev. A Rapid Communication 84, 050501 (2011). [8] 'The effect of the Perdew-Zunger self-interaction correction to density functionals on the energetics of small molecules', S. Klüpfel, P. Klüpfel and H. Jónsson, J. Chem. Phys. 137, 124102 (2012).

Abstract: too high atomization energy (overbinding of the molecules), the application of PZ-SIC gives a large overcorrection and leads to significant underestimation of the atomization energy. The exchange enhancement factor that is optimal for the generalized gradient approximation within the Kohn-Sham (KS) approach may not be optimal for the self-interaction corrected functional. The PBEsol functional, where the exchange enhancement factor was optimized for solids, gives poor results for molecules in KS but turns out to work better than PBE in PZ-SIC calculations. The exchange enhancement is weaker in PBEsol and the functional is closer to the local density approximation. Furthermore, the drop in the exchange enhancement factor for increasing reduced gradient in the PW91 functional gives more accurate results than the plateaued enhancement in the PBE functional. A step towards an optimal exchange enhancement factor for a gradient dependent functional of the PZ-SIC form is taken by constructing an exchange enhancement factor that mimics PBEsol for small values of the reduced gradient, and PW91 for large values. The average atomization energy is then in closer agreement with the high-level quantum chemistry calculations, but the variance is still large, the F2 molecule being a notable outlier.

Abstract: Metal nanoparticles of only ~100-200 atoms are synthesized using a dendrimer encapsulation technique to facilitate a direct comparison with density functional theory (DFT) calculations in terms of both structure and catalytic function. Structural characterization is done using electron microscopy, x-ray scattering, and electrochemical methods. Combining these tools with DFT calculations is found to improve the quality of the structural models. DFT is also successfully used to predict trends between structure and composition of the nanoparticles and their catalytic function for reactions including the reduction of oxygen and the oxidation of formic acid. This investigation demonstrates some remarkable properties of the nanoparticles, including facile structural rearrangements and nanoscale tuning parameters which can be used to optimize catalytic rates.

Abstract: Despite many decades of force field developments, and the proliferation of efficient first principles molecular dynamics simulation techniques, a universal microscopic model for water in its various phases has not yet been achieved. In recent years, progress in force field development has shifted from optimizing in ever greater detail the parameters of simple pair-wise additive empirical potentials to developing more advanced models that explicitly include many-body interactions through induced polarization and short-range exchange-repulsion interactions. Such models are often parametrized to reproduce as closely as possible the Born-Oppenheimer surface from highly accurate quantum chemistry calculations; the best models often outperform DFT in accuracy, yet are orders of magnitude more computationally efficient. The SCME model was recently suggested as a physically rigorous and transparent model where the dominant electrostatic interaction is described through a single-center multipole expansion up to the hexadecapole moment, and where many-body effects are treated by induced dipole and quadrupole moments. In this paper, recent improvements of SCME are presented along with selected applications. Monomer flexibility is included via an accurate potential energy surface, a dipole moment surface is used to describe the geometric component of the dipole polarizability, and several formulations of the anisotropic short-range exchange-repulsion interaction are compared. The performance of this second version of the model, SCME2, is demonstrated by comparing to experimental results and high-level quantum chemistry calculations. Future perspectives for applications and developments of SCME2 are presented, including an outline for how the model can be adapted to describe mixed systems of water with other small molecules and how it can be used as a polarizable solvent in QM/MM simulations.

Abstract: The present study aims to explore the quantum topological features of the electron density and its Laplacian of the understudied molecular bromine species involved in ozone depletion events. The characteristics of the C-Br and O-Br bonds have been analyzed via quantum theory of atom in molecules (QTAIM) analysis using the wave functions computed at the B3LYP/aug-cc-PVTZ level of theory. Quantum topology analysis reveal that the C-Br and O-Br bonds show depletion of charge density indicating the increased ionic character of these bonds. Contour plots and relief maps have been analyzed for regions of valence shell charge concentrations (VSCC) and depletions (VSCD) in the ground state

Abstract: Heterogeneous manycore parallel machines, mixing multicore CPUs with manycore accelerators provide an unprecedented amount of processing power per node. Dealing with such a large number of heterogeneous processing units -- providing a highly unbalanced computing power -- is one of the biggest challenge that developpers of HPC applications have to face. To Fully tap into the potential of these heterogeneous machines, pure offloading approaches, that consist in running an application on host cores while offloading part of the code on accelerators, are not sufficient. In this talk, I will go through the major software techniques that were specifically designed to harness heterogeneous architectures, focusing on runtime systems. I will discuss some of the most critical issues programmers have to consider to achieve portability of performance, and how programming languages may evolve to meet such as goal. Eventually, I will give some insights about the main challenges designers of programming environments will have to face in upcoming years.

Abstract: The increased availability of modern embedded many-core architectures supporting floating-point operations in hardware makes them interesting targets in traditional high performance computing areas as well. In this paper, the Lattice Boltzmann Method (LBM) from the domain of Computational Fluid Dynamics (CFD) is evaluated on Adapteva’s Epiphany many-core architecture. Although the LBM implementation shows very good scalability and high floating-point efficiency in the lattice computations, current Epiphany hardware does not provide adequate amounts of either local memory or external memory bandwidth to provide a good foundation for simulation of the large problems commonly encountered in real CFD applications.

Abstract: Many-core processors offer top computational power while keeping the energy consumption reasonable compared to complex processors. Today, they enter both high-performance computing systems, as well as embedded systems. However, these processors require dedicated programming models to efficiently benefit from their massively parallel architectures. The chemical programming paradigm has been introduced in the late eighties as an elegant way of formally describing distributed programs. Data are seen as molecules that can freely react thanks to operators to create new data. This paradigm has also been used within the context of grid computing and now seems to be relevant for many-core processors. Very few implementations of runtimes for chemical programming have been proposed, none of them giving serious elements on how it can be deployed onto a real architecture. In this paper, we propose to implement some parts of the chemical paradigm over the ΣC dataflow programming language, that is dedicated to many-core processors. We show how to represent molecules using agents and communication links, and to iteratively build the dataflow graph following the chemical reactions. A preliminary implementation of the chemical reaction mechanisms is provided using the τC dataflow compilation toolchain, a language close to ΣC, in order to demonstrate the relevance of the proposition.

Abstract: The Adapteva Epiphany RISC array processor offers high computational energy-efficiency and parallel scalability. However, extracting performance with a standard parallel programming model remains a great challenge. We present an effective programming model for the low-power Epiphany architecture based on the Message Passing Interface (MPI) standard. Using MPI exploits the similarities between the Epiphany architecture and a networked parallel distributed cluster. Furthermore, our approach enables codes written with MPI to execute on the RISC array processor with little modification. We present experimental results for the threaded MPI implementation of matrix-matrix multiplication and highlight the importance of fast inter-core data transfers. Our high-level programming methodology achieved an on-chip performance of 9.1 GFLOPS.

Abstract: This is the summary of the 8th Workshop on Biomedical and Bioinformatics Challenges for Computer Science - BBC2015

Abstract: A central issue in biological data analysis is that uncertainty, resulting from different factors of variabilities, may change the effect of the events being investigated. Therefore, robustness is a fundamental step to be considered. Robustness refers to the ability of a process to cope well with uncertainties, but the different ways to model both the processes and the uncertainties lead to many alternative conclusions in the robustness analysis. In this paper we apply a framework allowing to deal with such questions for mass spectrometry data. Specifically, we provide robust decisions when testing hypothesis over a case/control population of subject measurements (i.e. proteomic profiles). To this concern, we formulate (i) a reference model for the observed data (i.e., graphs), (ii) a reference method to provide decisions (i.e., test of hypotheses over graph properties) and (iii) a reference model of variability to employ sources of uncertainties (i.e., random graphs). We apply these models to a real-case study, analyzing the mass spectrometry pofiles of the most common type of Renal Cell Carcinoma; the Clear Cell variant.

Abstract: Exploration of tissue sections by imaging mass spectrometry reveals abundance of different biomolecular ions in different sample spots, allowing finding region specific features. In this paper we present computational and statistical methods for investigation of protein biomarkers i.e. biological features related to presence of different pathological states. Proposed complete processing pipeline includes data pre-processing, detection and quantification of peaks by using Gaussian mixture modeling and identification of specific features for different tissue regions by performing permutation tests. Application of created methodology provides detection of proteins/peptides with concentration levels specific for tumor area, normal epithelium, muscle or saliva gland regions with high confidence.

Abstract: NGS data processing in metagenomics studies has to deal with noisy data that can contain a large amount of reading errors which are difficult to detect and account for. This work introduces a fuzzy indicator of reliability technique to facilitate solutions to this problem. It includes modified Hamming and Levenshtein distance functions that are aimed to be used as drop-in replacements in NGS analysis procedures which rely on distances, such as phylogenetic tree construction. The distances utilise fuzzy sets of reliable bases or an equivalent fuzzy logic, potentially aggregating multiple sources of base reliability.

Abstract: Workflows are becoming the new paradigm in bioinformatics. In general, bioinformatics problems are solved by interconnecting several small software pieces to perform complex analyses. This demands a minimal expertise to create, enact and monitor such tools compositions. In addition bioinformatics is immersed in the big-data territory, facing huge problems to analyse such amount of data. We have addressed these problems by integrating a tools management platform (Galaxy) and a Cloud infrastructure, which prevents moving the big datasets between different locations and allows the dynamic scaling of the computing resources depending on the user needs. The result is a user-friendly platform that facilitates the work of the end-users while performing their experiments, installed in a Cloud environment that includes authentication, security and big-data transfer mechanisms. To demonstrate the suitability of our approach we have integrated in the infrastructure an existing pairwise and multiple genome comparison tool which comprises the management of huge datasets and high computational demands.

Abstract: In the medical imaging field, iterative methods have become a hot topic of research due to their capacity to resolve the reconstruction problem from a limited number of projections. This gives a good possibility to reduce radiation exposure on patients during the data acquisition. However, due to the complexity of the data, the reconstruction process is still time consuming, especially for 3D cases, even though implemented on modern computer architecture. Time of the reconstruction and high radiation dose imposed on patients are two major drawbacks in computed tomography. With the aim to resolve them effectively, we adapted Least Square QR method with soft threshold filtering technique for few-view image reconstruction and present its numerical validation. The method is implemented using CUDA programming mode and compared to standard SART algorithm. The numerical simulations and qualitative analysis of the reconstructed images show the reliability of the presented method.