Abstract: The use of heterogeneous devices is becoming increasingly widespread. Their main drawback is their low programmability due to the large amount of details that must be handled. Another important problem is the reduced code portability, as most of the tools to program them are vendor or device-specfic. The exception to this observation is OpenCL, which largely suffers from the reduced programmability problem mentioned, particularly in the host side. The Heterogeneous Programming Library (HPL) is a recent proposal to improve this situation, as it couples portability with good programmability. While the HPL kernels must be written in a language embedded in C++, users may prefer to use OpenCL kernels for several reasons such as their growing availability or a faster development from existing codes. In this paper we extend HPL to support the execution of native OpenCL kernels and we evaluate the resulting solution in terms of performance and programmability, achieving very good results.

Abstract: The particle-mesh spreading operation maps a value at an arbitrary particle position to con- tributions at regular positions on a mesh. This operation is often used when a calculation involving irregular positions is to be performed in Fourier space. We study several approaches for particle-mesh spreading on GPUs. A central concern is the use of atomic operations. We are also concerned with the case where spreading is performed multiple times using the same particle configuration, which opens the possibility of preprocessing to accelerate the overall com- putation time. Experimental tests show which algorithms are best under which circumstances.

Abstract: Computational science has benefited in the last years from emerging accelerators that increase the performance of scientific simulations, but using these devices hinders the programming task. This paper presents AMA: a set of optimization techniques to efficiently manage multi-accelerator systems. AMA maximizes the overlap of computation and communication in a blocking-free way. Then, we can use such spare time to do other work while waiting for device operations. Implemented on top of a task-based framework, the experimental evaluation of AMA on a quad-GPU node shows that we reach the performance of a hand-tuned native CUDA code, with the advantage of fully hiding the device management. In addition, we obtain up to more than 2x performance speed-up with respect to the original framework implementation.

Abstract: Commodity processors are comprised of several CPU cores and one integrated GPU. To fully exploit this type of architectures, one needs to automatically determine how to partition the workload between both devices. This is specially challenging for irregular workloads, where each iteration's work is data dependent and shows control and memory divergence. In this paper, we present a novel adaptive partitioning strategy specially designed for irregular applications running on heterogeneous CPU-GPU chips. The main novelty of this work is that the size of the workload assigned to the GPU and CPU adapts dynamically to maximize the GPU and CPU utilization while balancing the workload among the devices. Our experimental results on an Intel Haswell architecture using a set of irregular benchmarks show that our approach outperforms exhaustive static and adaptive state-of-the-art approaches in terms of performance and energy consumption.

Abstract: In the current work the authors present several approaches to the high performance simulation of human diseases propagation using hybrid two-component imitational models. The models under study were created by coupling compartmental and discrete-event submodels. The former is responsible for the simulation of the demographic processes in a population while the latter deals with a disease progression for a certain individual. The number and type of components used in a model may vary depending on the research aims and data availability. The introduced high performance approaches are based on batch random number generation, distribution of simulation runs and the calculations on graphical processor units. The emphasis was made on the possibility to use the approaches for various model types without considerable code refactoring for every particular model. The speedup gained was measured on simulation programs written in C++ and MATLAB for the models of HIV and tuberculosis spread and the models of tumor screening for the prevention of colorectal cancer. The benefits and drawbacks of the described approaches along with the future directions of their development are discussed.

Abstract: Climate change as a result of human activities is a problem of a paramount importance. The global temperature on Earth is gradually increasing and it may lead to substantially hotter summers in a moderate belt of Europe, which in turn is likely to influence the air conditioning penetration in this region. The current work is an attempt to predict air conditioning penetration in different residential areas in the UK between 2030-2090 using an integration of calibrated building models, future weather predictions and an agent-based model. Simulation results suggest that up to 12% of homes would install an air conditioner in 75 years’ time assuming an average purchasing ability of the households. The performed simulations provide more insight into the influence of overheating intensity along with households’ purchasing ability and social norms upon households’ decisions to purchase an air conditioner.

Abstract: Crowd evacuations involve thousands of persons in closed spaces. Having knowledge about where the problematic exits will be or where the disaster may occur can be crucial in emergency planning. We implemented a simulator using Agent Based Modelling able to model the behaviour of people in evacuation situations and a workflow able to run it in the cloud. The input is just a PNG image and the output are statistical results of the simulation executed on the cloud. This allows to provide the user with a system abstraction and only a map of the scenario is needed. Many events are held in main city squares, so to test our system we chose Siena and we fit about 28,000 individuals in the centre of the square. The software has special computational requirements because the results need to be statistically reliable. Because these needs we use distributed computing. In this paper we show how the simulator scales efficiently on the cloud.

Abstract: Evolutionary algorithms (EAs) are popular and powerful approaches for model calibration. This paper proposes an enhanced EA-based model calibration method, namely the differential evolution (DE) with sensitivity analysis and the Powell's method (DESAP). In contrast to traditional EA-based model calibration methods, the proposed DESAP owns three main features. First, an entropy-based sensitivity analysis operation is integrated so as to dynamically identify important parameters of the model as evolution progresses online. Second, the Powell's method is performed periodically to fine-tune the important parameters of the best individual in the population. Finally, in each generation, the DE operators are performed on a small number of better individuals rather than all individuals in the population. These new search mechanisms are integrated into the DE framework so as to reduce the computational cost and to improve the search efficiency. To validate its effectiveness, the proposed DESAP is applied to two crowd model calibration cases. The results demonstrate that the proposed DESAP outperforms several state-of-the-art model calibration methods in terms of accuracy and efficiency.

Abstract: Data partitioning is one of the main problems in parallel and distributed simulation. Distribution of data over the architecture directly influences the efficiency of the simulation. The partitioning strategy becomes a complex problem because it depends on several factors. In an Individual-oriented Model, for example, the partitioning is related to interactions between the individual and the environment. Therefore, parallel and distributed simulation should dynamically enable the interchange of the partitioning strategy in order to choose the most appropriate partitioning strategy for a specific context. In this paper, we propose a strip partitioning strategy to a spatially dependent problem in Individual-oriented Model applications. This strategy avoids sharing resources, and, as a result, it decreases communication volume among the processes. In addition, we develop an objective function that calculates the best partitioning for a specific configuration and gives the computing cost of each partition, allowing for a computing balance through a mapping policy. The results obtained are supported by statistical analysis and experimentation with an Ant Colony application. As a main contribution, we developed a solution where the partitioning strategy can be chosen dynamically and always returns the lowest total execution time.

Abstract: In recent years, thanks to the low cost of deploying, maintaining an Unmanned Aerial Vehicle (UAV) system and the possibility to operating them in areas inaccessible or dangerous for human pilots, UAVs have attracted much research attention both in the military field and civilian application. In order to deal with more sophisticated tasks, such as searching survival points, multiple target monitoring and tracking, the application of UAV swarms is forseen. This requires more complex control, communication and coordination mechanisms. However, these mechanisms are difficult to test and analyze under flight dynamic conditions. These multi- UAV scenarios are by their nature well suited to be modeled and simulated as multi-agent systems. The first step of modeling an multi-agent system is to construct the model of agent, namely accurate model to represent its behavior, constraints and uncertainties of UAVs. In this paper we introduce our approach to model an UAV as an agent in terms of multi-agent system principle. Construction of the model to satisfy the need for a simulation environment that researchers can use to evaluate and analyze swarm control mechanisms. Simulations results of a case study is provided to demonstrate one possible use of this approach.

Abstract: A procedure for low-cost multi-objective design optimization of antenna structures is discussed. The major stages of the optimization process include: (i) an initial reduction of the search space aimed at identifying its relevant subset containing the Pareto-optimal design space, (ii) construction—using sampled coarse-discretization electromagnetic (EM) simulation data—of the response surface approximation surrogate, (iii) surrogate optimization using a multi-objective evolutionary algorithm, and (iv) the Pareto front refinement. Our optimization procedure is demonstrated through the design of a planar quasi Yagi-Uda antenna. The final set of designs representing the best available trade-offs between conflicting objectives is obtained at a computational cost corresponding to about 172 evaluations of the high-fidelity EM antenna model.

Abstract: The paper describes methods for constructing the robust schedules using agent-based simulation. The measure of robustness represents the resistance of the schedule to random phenomena and we present the method for calculating robustness of the schedule. The procedure for creating the robust schedule combines standard solutions for planning and scheduling with computer simulation. It is described in detail and allows creation an executable robust schedule. Three different procedures for increasing the robustness (by changing the order of allocation of resources, by changing a plan and increasing time reserves) are short explained. The presented techniques were tested using real detailed simulation model of an existing container terminal.

Abstract: Trawl-doors have a large influence on the fuel consumption of fishing vessels. Design and optimization of trawl-doors using computational models are key factors in minimizing the fuel consumption. This paper presents an efficient optimization algorithm for the design of trawl-door shapes using computational fluid dynamic models. The approach is iterative and uses variable-fidelity models and space mapping. The algorithm is applied to the design of a multi-element trawl-door, involving four design variables controlling the angle of attack and the slat position and orientation. The results demonstrate that a satisfactory design can be obtained at a cost of a few iterations of the algorithm. Compared with direct optimization of the high-fidelity model and local response surface surrogate models, the proposed approach requires 79% less computational time while, at the same time, improving the design significantly (over 12% increase in the lift-to-drag ratio).

Abstract: Focal brachytherapy is a clinical procedure that can be used to treat low-risk prostate cancer with reduced side-effects compared to conventional brachytherapy. Current practice is to manually plan the placement of radioactive seeds inside the prostate to achieve a desired treatment dose. Problems with the current practice are that the manual planning is time-consuming and high doses to the urethra and rectum cause undesirable side-effects. To address this problem, we have designed an optimisation algorithm that constructs treatment plans which achieve the desired dose while minimizing dose to organs at risk. We also show that these seed plans are robust to post-operative movement of the seeds within the prostate.

Abstract: Statistically Similar Representative Volume Element (SSRVE) is used to simplify computational domain for microstructure representation of material in multiscale modelling. The procedure of SSRVE creation is based on optimization loop which allows to find the highest similarity between SSRVE and an original material microstructure. The objective function in this optimization is built upon computationally intensive numerical methods, including simulations of virtual material deformation, which is very time consuming. To avoid such long lasting calculations we propose to use the data farming approach to identification of SSRVE for Advanced High Strength Steels (AHSS) characterized by multiphase microstructure. The optimization method is based on a nature inspired approach which facilitates distribution and parallelization. The concept of SSRVE creation as well as the software architecture of the proposed solution is described in the paper in details. It is followed by examples of the results obtained for the identification of SSRVE parameters for DP steels which are widely exploited in modern automotive industry. Possible directions for further development and uses are described in the conclusions.

Abstract: In this work, we present a numerical treatment of the model of two-phase flow in porous media including specific interfacial area. For numerical discretization we use the cell-centered finite difference (CCFD) method based on the shifting-matrices method which could reduce the time-consuming operations. A new iterative implicit algorithm has been developed to solve the problem under consideration. All advection and advection-like terms that appear in saturation equation and interfacial area equation are treated using upwind schemes together with the CCFD and shifting-matrices techniques. Selected simulation results such as $p_c-S_w-a_{wn}$ surface have been introduced. The simulation results have a good agreement with those in the literature using either pore network modeling or Darcy scale modeling.

Abstract: The research extends and improves the parametric study of "Instabilities and transition of a sphere falling or ascending freely in a Newtonian fluid" of Jenny et al. (2004) with special focus on the onset of chaos and on chaotic states. The results show that the effect of density ratio responsible for two qualitatively different oblique oscillating states has a significant impact both on the onset of chaos and on the behavior of fully chaotic states. The observed difference between dense and light spheres is associated to the strength of coupling between fluid and solid degrees of freedom. While the low frequency mode of oblique oscillating state presents specific features due to a strong solid - fluid coupling, the dynamics of the high frequency mode is shown to be driven by the same vortex shedding as the wake of a fixed sphere. The different fluid-solid coupling also determines two different ways how chaos sets in. Two outstanding ordered regimes are evidenced and investigated in the chaotic domain. One of them, characteristic for its helical trajectories, might provide a link to the experimentally evidenced, but so far numerically unexplained, vibrating regime of ascension of light spheres. For fully chaotic states, it is shown that statistical averaging converges in a satisfactory manner. Several statistical characteristics are suggested and evaluated.

Abstract: Recently, we have developed several techniques in order to accelerate Monte Carlo (MC) molecular simulations. For that purpose, two strategies were followed. In the first, new algorithms were proposed as a set of early rejection schemes performing faster than the conventional algorithm while preserving the accuracy of the method. On the other hand, a reweighting and reconstruction scheme was introduced that is capable of retrieving primary quantities and second derivative properties at several thermodynamic conditions from a single MC Markov chain. The latter scheme, was first developed to extrapolate quantities in NVT ensemble for structureless Lennard-Jones particles. However, it is evident that for most real life applications the NpT ensemble is more convenient, as pressure and temperature are usually known. Therefore, in this paper we present an extension to the reweighting and reconstruction method to solve NpT problems utilizing the same Markov chains generated by the NVT ensemble simulations. Eventually, the new approach allows elegant switching between the two ensembles for several quantities at a wide range of neighboring thermodynamic conditions.

Abstract: The paper presents a mathematical model of a turbulent river flow based on unsteady shallow water equations and depth averaged turbulence model. The numerical model is based on upwind finite volume method on structured staggered grid. In order to get a stable numerical solution simple-based algorithm was used. Among well-developed models of the river flow proposed approach stands out with its computational efficiency and high quality in describing processes in a river stream. For the main cases of pollution transport in river flows it is essential to know whether the model is appropriate to predict turbulent characteristics of the flow in the open channel. Two computational cases have been carried out to investigating and to applying established model. The first case shows the impact of confluents into generation of turbulence in the river flow and shows that recirculation flows effects on the process of pollutant dispersion in water basins. Driven cavity test case have been carried out to investigate the accuracy of the established method and its applicability to the streams with a complex structure.

Abstract: In this paper, we proposed a nonparallel hyperplanes classier for multi-class classication, termed as NHCMC. This method inherits the idea of multiple birth support vector machine(MBSVM), that is the "max" decision criterion instead of the "min" one, but it has the incomparable advantages than MBSVM. First, the optimization problems in NHCMC can be solved eciently by sequential minimization optimization (SMO) without needing to compute the large inverses matrices before training as SVMs usually do; Second, kernel trick can be applied directly to NHCMC, which is superior to existing MBSVM. Experimental results on lots of data sets show the eciency of our method in multi-class classication accuracy.

Abstract: One-way coupling often occurs in multi-dimensional stochastic models in finance. In this paper, we develop a highly efficient Monte Carlo (MC) method for pricing European options under a N-dimensional one-way coupled model, where N is arbitrary. The method is based on a combination of (i) the powerful dimension and variance reduction technique, referred to as drMC, developed in Dang et. al (2014), that exploits this structure, and (ii) the highly effiective multilevel MC (mlMC) approach developed by Giles (2008). By first applying Step (i), the dimension of the problem is reduced from N to 1, and as a result, Step (ii) is essentially an application of mlMC on a 1-dimensional problem. Numerical results show that, through a careful construction of the ml-dr estimator, improved efficiency expected from the Milstein timestepping with first order strong convergence can be achieved. Moreover, our numerical results show that the proposed ml-drMC method is significantly more efficient than the mlMC methods currently available for multi-dimensional stochastic problems.

Abstract: This paper presents a Hierarchical Hidden Markov Model used to capture the USD/COP market sentiment dynamics choosing from uptrend or downtrend latent regimes based on observed feature vector realizations calculated from transaction prices and wavelet-transformed order book volume dynamics. The HHMM learned a natural switching buy/uptrend sell/downtrend trading strategy using a training-validation framework over one month of market data. The model was tested on the following two months, and its performance was reported and compared to results obtained from randomly classified market states and a feed-forward Neural Network. This paper also separately assessed the contribution to the model’s performance of the order book information and the wavelet transformation.

Abstract: There are plenty of researches dedicated to financial system stability, which takes significant place in prevention of financial crisis and its consequences. However banking system and external environment interaction and customers behaviour influence on the banking system stability are poorly studied. Current paper propose agent-based model of banking system and its external environment. We show how customers behaviour characteristics affect a banking system stability. Optimal interval for total environmental funds towards banking system wealthy is performed.

Abstract: he current trend in supercomputer architectures is leading scientific applications to use parallel programing models such Message Passing Interface (MPI) to use computing resources properly. Understanding how these applications behave it is not straightforward and its crucial for achieving good performance and good efficiency of their codes. Here we present a real case of study of a cutting­edge scientific application. NEMO is a state­of­the­art Ocean Global Circulation Model (OGCM) hundreds of users around the world. It is used for oceanographic research, operational oceanography, seasonal forecast and climate studies. In the framework of PRACE, the projects HiResClim and HiResClim II, related to High Resolution Climate projections, uses NEMO as a oceanic model and had conceded more than 38 million core hours in the 5th PRACE Regular Call for Proposals and 50 million core hours in the 7th PRACE Regular Call for Proposals, both in the Tier­0 machine Marenostrum. That huge amount of computation time justifies the effort to analyze and optimize the application’s performance. Using the performance tools developed at Barcelona Supercomputing Center (BSC) it is possible to analyse the behaviour of the application . We studied different executions of the NEMO model and performed different analysis of the computational phases, analyzing how cpu and memory behaves, and also communication patterns. We also did strong and weak scaling tests to find bottlenecks constraining the scalability of the application. With this analysis, we could confirm some of the envisaged problems in previous performance analysis of the application and further see other problems not identified before. Using Paraver is both possible to see with high detail the internal behaviour of the application (we can see for example when, who and to where every message is sent) or to compute metrics to extract useful information (such parallel efficiency, load balance or many more). Dimemas allows us to simulate the behaviour of the application under different conditions. It could be useful to analyze the sensibility to network parameters, and for example it could be useful to analyze if one application could run properly in cloud computing. Other tools being developed at BSC and used in this work are Clustering and Folding. The clustering tool uses a data mining technique to identify regions of code with similar performance trends. This make possible to group together and study different iterations, using the folding tool, in order to get instantaneous performance metrics inside the routines, finding areas of interest that have a poor hardware usage. To demonstrate the power of these tools we will show some success stories for NEMO using BSC tools, reporting how we identified specific bottlenecks, proposed some solutions and finally confirmed the impact of the changes.

Abstract: Using a three-level multiscale modelling scheme and several Petascale supercomputers, we have been able to model the dynamical process of polymer intercalation into clay tactoids and the ensuing aggregation of polymer-entangled tactoids into larger structures. In our approach, we use a quantum mechanical and atomistic descriptions to derive a coarse-grained yet chemically specific representation that can resolve processes on hitherto inaccessible length and time scales. We applied our approach to study collections of clay mineral tactoids interacting with two synthetic polymers, poly(ethylene glycol) and poly(vinyl alcohol). The controlled behavior of layered materials in a polymer matrix is centrally important for many engineering and manufacturing applications, and opens up a route to computing the properties of complex soft materials based on knowledge of their chemical composition, molecular structure, and processing conditions. In this talk I will present the work we have performed, as well as the techniques we used to enable the model coupling and the deployment on large infrastructures.

Abstract: TBD

Abstract: The remarkable developments and advances in High Performance Computing (HPC) and communications technology over the last decades made possible many achievements and benefits across a wide variety of academic and industrial branches. Thus, it is well-established that HPC is a key technology and enabler resource for science, industry and business activities, especially for large and complex problems where the scale of the problem being tackled creates challenges or the time of the solution is important. Envisioned to create a world-class competitive and persistent pan-European Research Infrastructure (RI) HPC Service, the Partnership for Advanced Computing in Europe (PRACE) was established in 2010, as a Belgian international not-for-profit association (aisbl) with its seat in Brussels, Belgium. Today, PRACE is one of the world’s leading providers of HPC to research and industry (in particular SME) communities. Out of 25 participating country members within and beyond Europe, 4 “Hosting Members” (France, Germany, Spain and Italy) are in-kind contributors, providing access to 6 leading edge supercomputers in all major architectural classes: JUQUEEN (GCS – FZJ, Germany), CURIE (GENCI – CEA, France), HORNET (GCS – HLRS, Germany), SuperMUC (GCS – LRZ, Germany), MareNostrum (BSC, Spain) and FERMI (CINECA, Italy), who committed a total funding of €400 million for the initial PRACE systems and operations. To keep pace with the dynamic needs of a variety of scientific and industry communities and numerous technical changes and developments, PRACE hosting members' systems are continuously updated and upgraded to make most advanced HPC technologies accessible to European scientists and industry. By pooling national computing resources, PRACE is able to award access to Hosting Members HPC resources, through a unified European open and fair Peer-Review process of proposals calls through a web-tool. Two types of calls for proposals are offered to cover the needs expressed by the research and industry communities and to enable the participating hosting members to synchronize access to the resources, namely the Preparatory Access Call (permanent open call) and the Regular Call for Project Access (twice a year calls). The Preparatory Access is intended for short-term access (2 or 6 months) to resources, for code-enabling and porting, required to prepare proposals for Project Access and to demonstrate the scalability of codes. Project Access is intended for large-scale projects of excellent scientific merit and for which clear European added-value and major impact at international level is expected; and can be used for 12, 24 or 36 months in the case of (Multi-Year Access) production runs. PRACE reserves a level of resources for Centres of Excellence (CoE), selected by the EC under the E-INFRA-5-2015 call for proposals. In 2013, the SME HPC Adoption Programme in Europe (SHAPE) is a pan-European programme to support greater HPC adoption by SMEs was initiated by PRACE. This partnership powers excellent science and engineering in academia and industry, addressing society’s grand challenges. Open to all disciplines of research, and industry for open R&D, the PRACE infrastructure is a vital catalyst in fostering European competitiveness. Up to the 10th PRACE Call for Project Access (February, 2015), PRACE has awarded 10.2 thousand million core hours to 394 R&D projects from 38 countries, to come to fruition and yield unprecedented results. The growing range of disciplines that now depend on HPC can also be observed in the upward trend and evolution of the number and quality of project applications received and resources requested via the PRACE Calls for Project Access. PRACE has supported 2 patents, 158 PhD these, 507 publications (some in the most notable scientific journals) and 719 scientific talks (up to the 5th PRACE Call for Project Access). PRACE is also engaged to provide top-class education and training for computational scientists through the PRACE Advanced Training Centres (PATC), the International HPC Summer School, and PRACE seasonal schools. Until December 2014, PRACE has provided over 200 training events with over 5000 trainees and 19686 person-days of training (attendance-based), with an upward attendance trend from both academia and industry communities. Since mid-2012, PRACE has supported 50 companies, after opening its Calls for Proposals to industrial applicants, in the role of principal investigator or research team member collaborating in an academia-led project. So far, PRACE has awarded 10 SHAPE projects from 6 different countries. PRACE has also published 16 Best Practice Guides and over 200 White Papers. Nowadays it is well-established that HPC is indispensable for Science and Technology advanced in a wide range of scientific disciplines, such as biosciences, climate and health. Success stories and R&D outcomes of PRACE-supported projects shows how joint action and European competitiveness can benefit from a cross-pollination between science and industry (including SMEs), aided by European HPC resources.

Abstract: Social norms theory has provided a foundation for public health interventions on critical issues such as alcohol and substance use, sexual violence, and risky sexual behavior. We assert that modern social norms interventions can be better informed with the use of network science methods. Social norms can be seen as a complex contagion on a social network, and the propagation of social norms as an information diffusion process. We observe instances where the recommendations of social norms theory match up to theoretical predictions from information diffusion models, but also places where the network science viewpoint highlights aspects of intervention design not addressed by the existing theory. Information about network structure and dynamics are often not used in existing social norms interventions; we argue that these factors may be contributing to the lack of efficacy of social norms interventions delivered via online social networks. Network models of intervention also offer opportunities for better evaluation and comparison across application domains.

Abstract: TBD

Abstract: TBD

Abstract: After a short introduction to the mathematical modelling of the elastic dynamic problem, which shows the similarity between the governing Partial Differential Equations (PDEs) in different applications, common blocks for Finite Element approximation are identified, and an Object Oriented Programming (OOP) methodology for linear and non-linear, stationary and dynamic problems is presented. Advantages of this approach are commented and some results are shown as examples of this methodology.

Abstract: Difficult black-box problems are required to be solved in many scientific and industrial areas. In this paper, efficient use of a hardware accelerator to implement dedicated solvers for such problems is discussed and studied based on an example of Golomb Ruler problem. The actual solution of the problem is shown based on evolutionary and memetic algorithms accelerated on GPGPU. The presented results prove the supremacy of GPGPU over optimized multicore CPU implementation.

Abstract: The problem of planning for groups of autonomous beings is gaining attention over the last few years. Real life tasks, like mobile robots coordination or urban traffic management, need robust and flexible solutions. In this paper a new approach to the problem of multi-variant planning in such systems is presented. It assumes use of simple reactive controllers by the beings, however the state observation is enriched by dynamically updated model, which contains planning results. The approach gives promising results in the considered use case, which is the Multi Robot Task Allocation problem.

Abstract: Graph transformations provide a well established method for the formal description of modifications of graph-based systems. On the other side such systems can be regarded as multi-agent ones providing a feasible mean for maintaining and manipulating large scale data. This paper deals with the problem of information exchange among agents maintaining different graph-based systems. Graph formalism applied for representing a knowledge maintained by agents is used at the same time to perform graph transformations modeling a knowledge exchange. The consistency of a knowledge represented by the set of agents is ensured by execution of some graph transformations rules by two agents in a parallel way. We suggest that complex operations (sequences of graph transformations) should be introduced instead of the formalism basing on simple unconditional operations. The approach presented in this paper is accompanied by examples concerning the problem of personal data distributed over different places (and maintained by different agents) and transmitted in such an environment\footnote{Financial support for this study was provided from resources of National Center for Research and Development, the grant number NCBiR 0021/R/ID2/2011/01. }.

Abstract: The paper contains the concept of agent-based search system and monitoring of Web pages. It is oriented at the exploration of limited problem area, covering a given sector of industry or economy. The proposal of agent-based (modular) structure of the system is due to the desire to ease the introduction of modifications or enrichment of its functionality. Commonly used search engines do not offer such a feature. The second part of the article presents a pilot version of the WEB mining system, representing a simplified implementation of the previously presented concept. Testing of the implemented application was executed by referring to the problem area of foundry industry.

Abstract: Computational modelling of chemical systems is most easily carried out in the vacuum for single molecules. Accounting for environmental effects accurately in quantum chemical calculations, however, is often necessary for computational predictions of chemical systems to have any relevance to experiments carried out in the condensed phases. I will discuss a quantum mechanics/molecular mechanics (QM/MM) based method to account for solid-state effects on geometries and molecular properties in molecular crystals. The method in its recent black-box implementation in Chemshell can satisfactorily describe the crystal packing effects on local geometries in a molecular crystals and account for the electrostatic effects that affects certain molecular properties such as transition metal NMR chemical shifts, electric field gradients, Mössbauer and other spectroscopic properties.

Abstract: In finite systems, such as nanoparticles and gas-phase molecules, calculations of minimum energy paths connecting initial and final states of transitions as well as searches for saddle points are complicated by the presence of external degrees of freedom, such as overall translation and rotation. A method based on quaternion algebra for removing the external degrees of freedom is presented and applied in calculations using two commonly used methods: the nudged elastic band (NEB) method for finding minimum energy paths and DIMER for minimum-mode following to find transition states. With the quaternion approach, fewer images in the NEB are needed to represent MEPs accurately. In both the NEB and DIMER calculations, the number of iterations required to reach convergence is significantly reduced.

Abstract: One of the methods to find the global minimum of a potential energy surface of a molecular system is simulated annealing. The main idea of simulated annealing is to start you system at a high temperature and then slowly cool it down so that there is a chance for the atoms in the system to explore the different degrees of freedom and ultimately find the global minimum. Simulated annealing is traditionally used in classical Monte Carlo or in classical molecular dynamics. One of the methods to find the global minimum of a potential energy surface of a molecular system is simulated annealing. The main idea of simulated annealing is to start you system at a high temperature and then slowly cool it down so that there is a chance for the atoms in the system to explore the different degrees of freedom and ultimately find the global minimum. Simulated annealing is traditionally used in classical Monte Carlo or in classical molecular dynamics. In molecular dynamics, one of the traditional methods was first implemented by Woodcock in 1971. In this method the velocities are scaled down after a given number of molecular dynamics steps, let the system explore the potential energy surface and scale down the velocities again until a minimum is found. In this work we propose to use a viscous friction term, similar to the one used in Langevin dynamics, to slowly bring down the temperature of the system in a natural way. We use drag terms that depend linearly or quadraticaly on the velocity of the particles. These drag terms will naturally bring the temperature the system down and when the system reaches equilibrium they will vanish. Thus, imposing a natural criterion to stop the simulation. We tested the method in Lenard-Jones clusters of up to 20 atoms. We started the system in different initial conditions and used different values for the temperature and the drag coefficients and found the global minima of every one of the clusters. This method demonstrated to be conceptually very simple, but very robust, in finding the global minima.

Abstract: Charged interfaces are physical phenomena found in various natural systems and artificial devices within the fields of biology, chemistry and physics. In electrochemistry, this is known as the electrochemical double layer, introduced by Helmholtz over 150 years ago. At this interface, between a solid surface and the electrolyte, chemical reactions can take place in a strong electric field. In this presentation, a new computational method is introduced for creating charged interfaces and to study charge transfer reactions on the basis of periodic DFT calculations. The electrochemical double layer is taken as an example, in particular the hydrogen electrode as well as the O2, N2 and CO2 reductions. With this method the mechanism of forming hydrogen gas, water, ammonia and methane/methanol is studied. The method is quite general and could be applied to a wide variety of atomic scale transitions at charged interfaces.

Abstract: Computational screening for catalysts that are stable, active and selective towards electrochemical reduction of nitrogen to ammonia at room temperature and ambient pressure is presented from a range of transition metal nitride surfaces. Density functional theory (DFT) calculations are used to study the thermochemistry of cathode reaction so as to construct the free energy profile and to predict the required onset potential via the Mars-van Krevelen mechanism. Stability of the surface vacancy as well as the poisoning possibility of these catalysts under operating conditions are also investigated towards catalyst engineering for sustainable ammonia formation. The most promising candidates turned out to be the (100) facets of rocksalt structure of VN, CrN, NbN and ZrN that should be able to form ammonia at -0.51 V, -0.76 V, -0.65 V and -0.76 V vs. SHE, respectively. Another interesting result of the current work is that for the introduced nitride candidates hydrogen evolution is no longer the competing reaction; thus, high formation yield of ammonia is expected at low onset potentials.

Abstract: The increasing design complexity of manycore architectures at the hardware and software levels imposes to have powerful tools capable of validating every functional and non-functional property of the architecture. At the design phase, the chip architect needs to explore several parameters from the design space, and iterate on different instances of the architecture, in order to meet the defined requirements. Each new architectural instance requires the configuration and the generation of a new hardware model/simulator, its runtime, and the applications that will run on the platform, which is a very long and error-prone task. In this context, the IP-XACT standard has become widely used in the semiconductor industry to package IPs and provide low level SW stack to ease their integration. In this work, we present a primer work on a methodology to automatically configuring and assembling an IP-XACT golden model and generating the corresponding manycore architecture HW model, low-level software runtime and applications. We use the STHORM manycore architecture and the HBDC application as a case study.

Abstract: The Open Community Runtime (OCR) is a recent effort in the search for a runtime for extreme scale parallel systems. OCR relies on the concept of a dynamically generated task graph to express the parallelism of a program. Rather than being directly used for application development, the main purpose of OCR is to become a low-level runtime for higher-level programming models and tools. Since manycore architectures like the Intel Xeon Phi are likely to play a major role in future high performance systems, we have implemented the OCR API for shared-memory machines, including the Xeon Phi. We have also implemented two benchmark applications and performed experiments to investigate the viability of the OCR as a runtime for manycores. Our experiments and a comparison with OpenMP indicate that OCR can be an efficient runtime system for current and emerging manycore systems.

Abstract: Prefetch engines working on distributed memory systems behave independently by analyzing the memory accesses that are addressed to the attached piece of cache. They potentially generate prefetching requests targeted at any other tile on the system that depends on the computed address. This distributed behavior involves several challenges that are not present when the cache is unified. In this paper, we identify, analyze, and quantify the effects of these challenges, thus paving the way to future research on how to implement prefetching mechanisms at all levels of this kind of system with shared distributed caches.

Abstract: Visual analytics (VA) provides an interactive way to explore vast amounts of data and find interesting patterns. This has already benefited the development of computational models, as the patterns found using VA can then become essential elements of the model. Similarly, recent advances in the use of VA for the data cleaning stage are relevant to computational modelling given the importance of having reliable data to populate and check models. In this paper, we demonstrate via case studies of medical models that VA can be very valuable at the conceptual stage, to both examine the fit of a conceptual model with the underlying data and assess possible gaps in the model. The case studies were realized using different modelling tools (e.g., system dynamics or network modelling), which emphasizes that the relevance of VA to medical modelling cuts across techniques. Finally, we discuss how the interdisciplinary nature of modelling for medical applications requires an increased support for collaboration, and we suggest several areas of research to improve the intake and experience of VA for collaborative modelling in medicine.

Abstract: The emergence of Ebola in West Africa is of worldwide public health concern. Successful mitigation of epidemics requires coordinated, well-planned intervention strategies that are specific to the pathogen, transmission modality, population, and available resources. Modeling and simulation in the field of computational epidemiology provides predictions of expected outcomes that are used by public policy planners in setting response strategies. Developing up to date models of population structures, daily activities, and movement has proven challenging for developing countries due to limited governmental resources. Recent collaborations (in 2012 and 2014) with telecom providers have given public health researchers access to Big Data needed to build high-fidelity models. Researchers now have access to billions of anonymized, detailed call data records (CDR) of mobile devices for several West African countries. In addition to official census records, these CDR datasets provide insights into the actual population locations, densities, movement, travel patterns, and migration in hard to reach areas. These datasets allow for the construction of population, activity, and movement models. For the first time, these models provide computational support of health related decision making in these developing areas (via simulation-based studies). New models, datasets, and simulation software were produced to assist in mitigating the continuing outbreak of Ebola. Existing models of disease characteristics, propagation, and progression were updated for the current circulating strain of Ebola. The simulation process required the interactions of multi-scale models, including viral loads (at the cellular level), disease progression (at the individual person level), disease propagation (at the workplace and family level), societal changes in migration and travel movements (at the population level), and mitigating interventions (at the abstract governmental policy level). The predictive results from this system were validated against results from the CDC's high-level predictions.

Abstract: High quality Electrocardiogram (ECG) data is very important because this signal is generally used for the analysis of heart diseases. Wearable sensors are widely adopted for physical activity monitoring and for the provision of healthcare services, but noise always degrades the quality of these signals. The paper describes a new algorithm for ECG signal denoising, applicable in the contest of the real-time health monitoring using mobile devices, where the signal processing efficiency is a strict requirement. The proposed algorithm is computationally cheap because it belongs to the class of Infinite Impulse Response (IIR) noise reduction algorithms. The main contribution of the proposed scheme is that removes the noise’s frequencies without the implementation of the Fast Fourier Transform that would require the use of special optimized libraries. It is composed by only few code lines and hence offers the possibility of implementation on mobile computing devices in an easy way. Moreover, the scheme allows the local denoising and hence a real time visualization of the denoised signal. Experiments on real datasets have been carried out in order to test the algorithm from accuracy and computational point of view.

Abstract: Syncytial tissues, such as the smooth muscle of the urinary bladder wall, are known to produce action potentials (spikes) with marked differences in their shapes and sizes. The need for this diversity is currently unknown, and neither is their origin understood. The small size of the cells, their syncytial arrangement, and the complex nature of innervation poses significant challenges for the experimental investigation of such tissues. To obtain better insight, we present here a three-dimensional electrical model of smooth muscle syncytium, developed using the compartmental modeling technique, with each cell possessing active channel mechanisms capable of producing an action potential. This enables investigation of the syncytial effect on action potential shapes and their propagation. We show how a single spike shape could undergo modulation, resulting in diverse shapes, owing to the syncytial nature of the tissue. Difference in the action potential features could impact their capacity to propagate through a syncytium. This is illustrated through comparison of two distinct action potential mechanisms. A better understanding of the origin of the various spike shapes would have significant implications in pathology, assisting in evaluating the underlying cause and directing their treatment.

Abstract: Epilepsy is one of the most common neurological diseases, affecting about 1% of the world population, of all ages, genders, origins. About one third of the epileptic patients cannot be treated by medication or surgery: they suffer from refractory epilepsy and must live with their seizures during all their lives. A seizure can happen anytime, anywhere, imposing severe constrains in the professional and social lives of these patients. The development of transportable and comfortable devices, able to capture a sufficient number of EEG scalp channels, to digitally process the signal, to extract appropriate features from the EEG raw signals, and give these features to machine learning classifiers, is an important objective that a large research community is pursuing worldwide. The classifiers must detect the pre-ictal time (some minutes before the seizure). In this presentation the problem is presented, solutions are proposed, results are discussed. The problem is formulated as a classification of high-dimensional datasets, with unbalanced four classes. Preprocessing of raw data, classification using Artificial Neural Networks and Support Vector Machines to the 275 patients of the European Epilepsy Database show that computer science, in this case machine learning, will have an important role in the problem. For about 30% of the patients we found results with clinical relevance. Real-time experiments made with some patients, in clinical environment and at home will be shown (including video) and discussed. The problem is still challenging the computer science community researching in medical applications. New research directions will be pointed out in the presentation.