Abstract: Large Linear Complexity (LC) is a fundamental requirement for a binary sequence to be used in secret key cryptography. In this paper, a method of computing all the nonlinear filtering functions applied to a shift register with a linear complexity $LC\geq \binom{L}{k} + \binom{L}{k-1}$, where L is the register's length and k the order of the filter, is proposed. Emphasis is on the simple algebraic operations (addition and shifting of functions) included in the calculations. The method formally completes the family of nonlinear functions whose filtered sequences satisfy the previous lower bound on LC. In cryptographic terms, it means an easy and useful way of designing sequence generators for cryptographic purposes.

Abstract: Cachaca is a type of distilled drink from sugarcane with great economic importance. Its classication includes three types: aged, premium and extra premium. These three classications are related to the aging time of the drink in wooden casks. Besides the aging time, it is important to know what the wood used in the barrel storage in order the properties of each drink are properly informed consumer. This paper shows a method for automatic recognition of the type of wood and the aging time using information from a computer vision system and chemical information. Two algorithms for pattern recognition are used: articial neural networks and k-NN (k-Nearest Neighbor). In the case study, 144 cachaca samples were used. The results showed 97% accuracy for the problem of the aging time classication and 100% for the problem of woods classication.

Abstract: Molecular dynamics (MD) simulations play an important role in materials design. However, the effective use of the most widely used MD simulators require significant expertise of the scientific domain and deep knowledge of the given software tool itself. In this paper, we present a tool that offers an intuitive, component-oriented approach to design complex molecular systems and set up initial conditions of the simulations. We integrate this tool into a web- and cloud-based software infrastructure, called MetaMDS, that lowers the barrier of entry into MD simulations for practitioners. The web interface makes it possible for experts to build a rich library of simulation components and for ordinary users to create full simulations by parameterizing and composing the components. A visual programming interface makes it possible to create optimization workflows where the simulators are invoked multiple times with various parameter configurations based on results of earlier simulation runs. Simulation configurations – including the various parameters, the version of tools utilized and the results – are stored in a database to support searching and browsing of existing simulation outputs and facilitating the reproducibility of scientific results.

Abstract: A spirometer is used for basic lung function test for preliminary diagnosis of respiratory diseases. There are significant amount of calculations and graphical analysis required to transform the raw spirometric data into meaningful parameters. This parameters and graphs help the physicians in preliminary patient diagnosis for respiratory disorders like asthma, chronic obstructive pulmonary disease, etc. This study was undertaken for the development of a software system which can be used with any spirometric instrument to automate the calculations and analysis of raw data. The clinician can feed the raw data from patient testing into the easy to use graphical user interface of the software which will be analyzed instantly and all the parameters, regression slopes, shape analysis plots and all the results will be displayed graphically. The estimation of the vital parameters and regression slopes are based on standard protocols and equations. This system will eliminate presently practiced time consuming manual calculations and graphical analysis; will have increased precision, be considerably faster and more versatile.

Abstract: The present paper approaches high-frequency trading from a computational science perspective, presenting a pattern recognition model to predict price changes of stock market assets. The technique is based on the feature-weighted Euclidean distance to the centroid of a training cluster. A set of micro technical indicators, traditionally employed by professional scalpers, is used in this setting. We describe procedures for removal of outliers, normalization of feature points, computation of weights of features, and classification of test points. The complexity of computation at each quote received is proportional to the number of features. In addition, processing of indicators is parallelizable and, therefore, suitable in high-frequency domains. Experiments are presented for different prediction time intervals and confidence thresholds. Predictions made 10 to 2000 milliseconds before a price change result in an accuracy, which ranges monotonically from 97% to 75%. Finally, we observed an empirical relation between Euclidean distance in the feature space and prediction accuracy.

Abstract: Foreign exchange (Forex) market is the largest trading market in the world. Predicting the trend of the market and performing automated trading are important for investors. Recently, machine learning techniques emerged as a powerful trend to predict foreign exchange (FX) rates. In this paper we propose a new classification method for identifying up, down, and sideways trends in Forex market foreign exchange rates. A multi-scale feature extraction approached is used for training multiple classifiers for each trend. Bayesian voting is used to find the ensemble of classifiers for each trend. Performance of the system is validated using different metrics. The results show superiority of ensemble classifier over individual ones.

Abstract: As the Pawsey Centre project continues, in 2013 iVEC was tasked with deciding which accelerator technology to use in the petascale supercomputer to be delivered in 2014. While accelerators provide impressive performance and efficiency, an important factor in this decision is the usability of the technologies. To assist in the assessment of technologies, iVEC conducted a code sprint where iVEC staff and advanced users were paired to make use of a range of tools to port their codes to two architectures. Results of the sprint indicate that certain subtasks could benefit from using the tools in the code-acceleration process; however, there will be many hurdles users will face in migrating to either of the platforms explored.

Abstract: In this paper, we present a new approach to detect global hot events and local hot events. Unlike previous event detection algorithms which do not distinguish between global events and local events, we believe it is important that we make that distinction as certain events can only be meaningful if they are placed in specific context while other events may arouse the interests of general users. The main contribution of this paper is that we've customized hot events detection by employing local community detection mechanisms and established a very clear concept for global hot events and local hot events. We present in this paper a multi-layer event detection algorithm which constructs a four-stage event detection procedure that produces a relatively comprehensive description of events relevant to the unique makeup and different interest of microblog users. Both the global hot events and local hot events we gathered are represented by a key tweet which contains sufficient information to depict a complete event. As a result of our algorithm's ability to precisely describe events which outperforms existing event detection algorithms, it is now possible for people to better understand public sentiment towards hot issues on microblogs. Experiments have shown that our multi-layer hot event detection algorithm can produce promising results in mining the interests of different communities, generating relevant event clusters and presenting meaningful events to community users. The most all-round evaluation indicator F-value, which takes both precision and recall rate into account, has demonstrated that our algorithm outperforms the other three traditional approaches in detecting hot events.

Abstract: Urgent or critical situations, such as natural disasters, often require that stakeholders make crucial decisions, by analyzing the information provided by a group of experts about the different elements of interest in these contexts, with the aid of decision support tools. This framework can be modeled as a Group Decision Making problem defined under uncertain environments, which are characterized by the imprecision and vagueness of information about the problem tackled. One of the main aspects to consider when solving Group Decision Making problems in urgent situations, is the fact that decisions should be taken under the highest level of possible agreement amongst all participating experts, in order to avoid serious mistakes or undesired responsibilities by some experts. Due to the complexity of urgent situations and the great amount of information about experts’ preferences that must be managed by stakeholders, it would be difficult to reach consensus in the group within a reasonable time period, since it requires an adequate analysis of experts’ preferences. Such an analysis might become a difficult task in these contexts, due to the inherent complexity, time pressure, etc. In order to help making consensual decisions in urgent situations and urgent computing environments, we propose a visual decision support tool for Group Decision Making problems defined under uncertainty. Such a tool provides stakeholders with a two-dimensional visual representation of experts’ preferences based on the similarities between them, and it enables the analysis of easily interpretable information about the state of the decision problem, as well as the detection of agreement/disagreement positions between experts. The tool is based on Self-Organizing Maps, an unsupervised learning technique widely utilized for data visualization purposes, which facilitates the visual projection of information related to preferences of experts into a low-dimensional space.

Abstract: This study presents a numerical method for solving a class of equations that partly consists of two derivatives of the unknown state, namely the first derivative at the moment and the second derivative at a previous certain time, as well as an integro-differential term containing a weakly singular kernel. These equations are types of integro-differential equation of the second kind and were originally obtained from an aeroelasticity problem. In an integrable condition, the precise solutions of this class of equations can be obtained by transforming the equations into equations similar to the Volterra integral equations of the second kind with delay. The main contribution of this study is to propose a semi-discretized numerical method that does not involve transforming the original equation into the corresponding Volterra equation, but still enables the numerical solution of the original equation to be determined. The feasibility of the proposed numerical method is demonstrated by applying examples in measuring the maximum errors with exact solutions and comparing graphs of the numerical and exact solutions.

Abstract: Exploring the multi-core architecture is an important issue to obtaining high performance in parallel and distributed discrete-event simulations. However, the simulation features must fit on parallel programming model in order to increase the performance. In this paper we show our experience developing a hybrid MPI+OpenMP version of our parallel and distributed discrete-event individual-oriented fish schooling simulator. In the hybrid approach developed, we fit our simulation features in the following manner: the communication between the Logical Processes happens via message passing whereas the computing of the individuals by OpenMP threads. In addition, we propose a new data structure for partitioning the fish clusters which avoid the critical section in OpenMP code. As a result, the hybrid version significantly improves the total execution time for huge quantity of individuals, because it decreases both the communication and management of processes overhead, whereas it increases the utilization of cores with sharing of resources.

Abstract: Synthetic observations of the suspended sediment concentration in an idealised macro-tidal estuary are assimilated into the 3d sediment transport model. The assimilation scheme relies on fast and cheap surrogates of the complex model (called emulators) to update the model’s state variables and its 2 parameters. A scenario with a hierarchically structured emulator is contrasted to the scenario with a more conventional non-hierarchical emulator. Numerical experiments indicate that for a given size of the ensemble an emulator which replicates a hierarchical structure of the model tends to provide a better approximation of that model. Improving the quality of the emulator translates into the improved quality of the assimilation products.

Abstract: In global scientific experiments with collaborative scenarios involving multinational teams there are big challenges related to data access, namely data movements are precluded to other regions or Clouds due to the constraints on latency costs, data privacy and data ownership. Furthermore, each site is processing local data sets using specialized algorithms and producing intermediate results that are helpful as inputs to applications running on remote sites. This paper shows how to model such collaborative scenarios as a scientific workflow implemented with AWARD (Autonomic Workflow Activities Reconfigurable and Dynamic), a decentralized framework offering a feasible solution to run the workflow activities on distributed data centers in different regions without the need of large data movements. The AWARD workflow activities are independently monitored and dynamically reconfigured and steering by different users, namely by hot-swapping the algorithms to enhance the computation results or by changing the workflow structure to support feedback dependencies where an activity receives feedback output from a successor activity. A real implementation of one practical scenario and its execution on multiple data centers of the Amazon Cloud is presented including experimental results with steering by multiple users.

Abstract: Metaheuristics is a class of approximate methods based on heuristics that can effectively handle real world (usually NP-hard) problems of high-dimensionality with multiple objectives. An existing multi-objective Tabu-Search (MOTS2) has been re-designed by and ported onto Compute Unified Device Architecture (CUDA) so as to effectively deal with a scalable multi-objective problem with a range of decision variables. The high computational cost due to the problem complexity is addressed by employing Graphics Processing Units (GPUs), which alleviate the computational intensity. The main challenges of the re-implementation are the effective communication with the GPU and the transparent integration with the optimization procedures. Finally, future work is proposed towards heterogeneous applications, where improved features are accelerated by the GPUs.

Abstract: Increasing numbers of genomic technologies lead to a huge amount of genomic data. More and more bioinformatics analysis tools are developed to assistant data analysis. Thus, the opportunities for the scientists to build their own bioinformatics pipelines increase. The fact that these analysis tools were developed in different software environments makes the integration of these diverse analysis tools difficult. Kepler provides an open source environment to integrate these diverse bioinformatics tools. Using Kepler, we integrated several external tools including Bioconductor packages, AltAnalyze a python-based open source tool and program in R to build an automated workflow to meta-analyze both online and local microarray data. The automated workflow connects the integrated tools seamlessly, delivers data flow between the tools smoothly, and hence improves efficiency and accuracy of complex data analyses. The workflow exemplifies the usage of Kepler as a scientific workflow platform for bioinformatics pipelines.

Abstract: Multi-tenant database is a new database solution which is significant for Software as a service (SaaS) and Big Data applications in the context of cloud computing paradigm. This multi-tenant database has significant design challenges to develop a solution that ensures a high level of data quality, accessibility, and manageability for the tenants using this database. In this paper, we propose a multi-tenant data management service called Elastic Extension Tables Schema Handler Service (EETSHS), which is based on a multi-tenant database schema called Elastic Extension Tables (EET). This data management service satisfies tenants’ different business requirements, by creating, managing, organizing, and administrating large volumes of structured, semi-structured, and unstructured data. Furthermore, it combines traditional relational data with virtual relational data in a single database schema and allows tenants to manage this data by calling functions from this service. We present algorithms for frequently used functions of this service, and perform several experiments to measure the feasibility and effectiveness of managing multi-tenant data using these functions. We report experimental results of query execution times for managing tenants’ virtual and traditional relational data showing that EET schema is a good candidate for the management of multi-tenant data for SaaS and Big Data applications.

Abstract: In the last few years, parallel processing systems have been studied very actively, and, given the huge number of computing nodes now involved in modern supercomputers, many topologies have been proposed to efficiently connect all these CPUs. A torus and its variants are such topologies popular as interconnection networks of massively parallel systems. Torus-connected cycles (TCC) have been introduced recently, building on the interesting properties or tori and enabling nodes clustering. In this paper, we present an algorithm that solves the container problem in a TCC. This problem consists in finding mutually node-disjoint paths between any pair of nodes. In a TCC(k, n), the proposed algorithm finds paths of lengths at most ⌊k/2⌋ n^2 + (⌊k/2⌋ + 4)n - 3 in O(n^3 + kn^2) time. Lastly, an empirical evaluation is conducted to inspect the practical behaviour of this algorithm.

Abstract: This paper considers practical constraints for the unconstrained knapsack problem in its two-dimensional version, using integer programming. We first present an integer formulation for this knapsack problem, so couple constraints related with load balance, vertical (cargo) stability and fragility of the items also called load bearing. Using C language and the CPLEX solver, good results are obtained in an acceptable runtime considering instances from the literature.

Abstract: In this paper an autonomous feature clustering framework has been proposed for performance and reliability evaluation of an environmental sensor network. Environmental time series were statistically preprocessed to extract multiple semantic features. A novel hybrid clustering framework was designed based on Principal Component Analysis (PCA), Guided Self-Organizing Map (G-SOM), and Fuzzy-CMeans (FCM) to cluster the historical multi-feature space into probabilistic state classes. Finally a dynamic performance annotation mechanism was developed based on Maximum (Bayesian) Probability Rule (MPR) to quantify the performance of an individual sensor node and network. Based on the results from this framework, a “data quality knowledge map” was visualized to demonstrate the effectiveness of this framework.

Abstract: Performance of OpenMP applications executed in multiple socket multicore processors can be limited by the memory interface. In a multisocket environment, each multicore processor can present a performance degradation with memory-bound parallel regions when sharing the same Last Level Cache (LLC). We propose a characterization of the performance on parallel regions to estimate cache misses and execution time. This model is used to select the number of threads and affinity distribution for each parallel region. The model is applied for SP and MG benchmarks from the NAS Parallel Benchmark Suite using different workloads on two different multicore, multisocket systems. The results shown that the estimation preserves the behavior shown in measured executions in the dimensional space of affinities. Estimated execution time is used to select a set of configurations in order to minimize the impact of memory contention, achieving significant improvements compared with a default configuration using all threads.

Abstract: A parallel algorithm to remove impulsive noise in digital images using heterogeneous CPU/GPU computing is proposed. The parallel denoising algorithm is based on the peer group concept and uses an Euclidean metric. In order to identify the amount of pixels to be allocated in multi-core and GPUs, a performance analysis using large images is presented. A comparison of the parallel implementation in multi-core, GPUs and a combination of both is performed. Performance has been evaluated in terms of execution time and Megapixels/second. We present several optimization strategies especially effective for the multi-core environment, and demonstrate significant performance improvements. The main advantage of the proposed noise removal methodology is its computational speed, which enables efficient filtering of color images in real-time applications.

Abstract: Matrix multiplication is a fundamental mathematical operation that has numerous applications across most scientific fields. Cannon's distributed algorithm to multiply two $n$-by-$n$ matrices on a two dimensional square mesh array with $n^{2}$ cells takes exactly $3n-2$ communication steps to complete. We show that it is possible to perform matrix multiplication in just $1.5n-1$ communication steps on a two dimensional square mesh array of the same size, thus halving the number of steps required.

Abstract: The present paper introduces a new communication and load-balancing scheme based on a clustering of the grid which we use for the efficient parallelization of simulations on dynamically adaptive grids. With a partitioning based on space-filling curves (SFCs), this yields several advantageous properties regarding the memory requirements and load balancing. However, for such an SFC-based partitioning, additional connectivity information has to be stored and updated for dynamically changing grids. In this work, we present our approach to keep this connectivity information run-length encoded (RLE) only for the interfaces shared between partitions. Using special properties of the underlying grid traversal and used communication scheme, we update this connectivity information implicitly for dynamically changing grids and can represent the connectivity information as a sparse communication graph: graph nodes (partitions) represent bulks of connected grid cells and each graph edge (RLE connectivity information) a unique relation between adjacent partitions. This directly leads to an efficient shared-memory parallelization with graph nodes assigned to computing cores and an efficient en bloc data exchange via graph edges. We further refer to such a partitioning approach with RLE meta information as \emph{a cluster-based domain decomposition} and to each partition as a \emph{cluster}. With the sparse communication graph in mind, we then extend the connectivity information represented by the graph edges with MPI ranks, yielding an en bloc communication for distributed-memory systems and a hybrid parallelization. For data migration, the stack-based intra-cluster communication allows a very low memory footprint for data migration and the RLE leads to efficient updates of connectivity information. Our benchmark is based on a shallow water simulation on a dynamically adaptive grid. We conducted performance studies for MPI-only and hybrid parallelizations, yielding an efficiency of over 90% on 256 cores. Furthermore, we demonstrate the applicability of cluster-based optimizations on distributed-memory systems.

Abstract: 21st Century Smart Manufacturing (SM) is manufacturing in which all information is available when it is needed, where it is needed, and in the form it is most useful [1,2] to drive optimal actions and responses. The 21st Century SM enterprise is data driven, knowledge enabled, and model rich with visibility across the enterprise (internal and external) such that all operating actions are determined and executed proactively by applying the best information and a wide range of performance metrics. SM also encompasses the sophisticated practice of generating and applying data-driven Manufacturing Intelligence throughout the lifecycle of design, engineering, planning and production. Workflow is foundational in orchestrating dynamic, adaptive, actionable decision-making through the contextualization and understanding of data. Pervasive deployment of architecturally consistent workflow applications creates the enterprise environment for manufacturing intelligence. Workflow as a Service (WfaaS) software allows task orchestration and facilitates workflow services and manage environment to integrate interrelated task components. Apps, and toolkits are required to assemble customized SM applications on a common, standards based workflow architecture and deploy on infrastructure that is accessible by small, medium, and large companies. Incorporating dynamic decision-making steps through contextualization of real-time data requires scientific workflow software such as Kepler. By combining workflow, private cloud computing and web services technologies, we built a prototype test bed to test a furnace temperature control model.

Abstract: Power consumption has became a critical concern in modern computing systems for various reasons including financial savings and environmental protection. With battery powered devices, we need to care about the available amount of energy since it is limited. For the case of supercomputers, as they imply a large aggregation of heavy CPU activities, we are exposed to a risk of overheating. As the design of current and future hardware is becoming more and more complex, energy prediction or estimation is as elusive as that of time performance. However, having a good prediction of power consumption is still an important request to the computer science community. Indeed, power consumption might become a common performance and cost metric in the near future. A good methodology for energy prediction could have a great impact on power-aware programming, compilation, or runtime monitoring. In this paper, we try to understand from measurements where and how power is consumed at the level of a computing node. We focus on a set of basic programming instructions, more precisely those related to CPU and memory. We propose an analytical prediction model based on the hypothesis that each basic instruction has an average energy cost that can be estimated on a given architecture through a series of micro-benchmarks. The considered energy cost per operation includes both the overhead of the embedding loop and associated (hardware/software) optimizations. Using these precalculated values, we derive an linear extrapolation model to predict the energy of a given algorithm expressed by means of atomic instructions. We then use three selected applications to check the accuracy of our prediction method by comparing our estimations with the corresponding measurements obtained using a multimeter. We show a 9.48\% energy prediction on sorting.

Abstract: The sparse grid combination technique provides a framework to solve high-dimensional numerical problems with standard solvers. To combine the component grid solutions of the combination technique either interpolation and sampling or a change of basis from the full grid basis to the hierarchical basis is required. We implement a memory efficient hierarchization algorithm for the component grids of the sparse grid combination technique performing this change of basis. By exploiting the structure of the component grids, this implementation comes within a factor of 1.5 of the runtime achievable for large grids by any hierarchization algorithm implementing the unidirectional principle. The implementation outperforms the currently fastest generic software StructuredSG by a factor between 5.8x and 41x for problems larger than 30MiB.

Abstract: Science gateways allow computational scientists to interact with a complex mix of mathematical models, software tools and techniques, and high performance computers. Accordingly, various groups have built high-level problem-solving environments that allow these to be mixed freely. In this paper, we introduce an interactive workflow-based science gateway, called WorkWays. WorkWays integrates different domain specific tools, and at the same time is flexible enough to support user input, so that users can monitor and steer simulations as they execute. A benchmark design experiment is used to demonstrate WorkWays.

Abstract: Scientific workflows integrate data and computing interfaces as configurable, semiautomatic graphs to solve a scientific problem. Kepler is such a software system for designing, executing, reusing, evolving, archiving and sharing scientific workflows. Electron tomography (ET) enables high-resolution views of complex cellular structures, such as cytoskeletons, organelles, viruses and chromosomes. This paper describes a workflow for Electron Tomography Programs in Kepler (EPiK). This EPiK workflow embeds the tracking process of IMOD, and realizes the main algorithms including filtered backprojection (FBP) from TxBR and iterative reconstruction methods. We have tested the three dimensional (3D) reconstruction process using EPiK on one set of ET data. EPiK workflow can provide a semi-automatic platform for obtaining the 3D structure of components ranging from molecules to cells by using multiple projection views of a tilted sample. EPiK can be a potential toolkit for biology researchers with the advantage of logical viewing, easy handling, and convenient sharing.

Abstract: In the last decade, Hadoop has become a de-facto standard framework for big data processing in the industry. Although Hadoop today is primarily applied to textual data, it can be also used to process binary data including images. A number of frameworks have been developed to increase productivity of developing Hadoop based solutions. This paper demonstrates how such a framework (Scalding) can be used to create a concise and efficient solution to a big data image-processing problem of creating photographic mosaics and compares it to a Hadoop API based implementation.

Abstract: This paper introduces a complex network model for collective behavior of floating drifters at sea. Direct simulation method for floating objects on irregular waves is used to express the network dynamics. The features of collective behavior (such as the network destruction) are considered. The model is applied to study of efficiency of maritime search and rescue operations at sea.

Abstract: Supercomputer architectures are being upgraded using different level of parallelism to improve computing performance. This makes it difficult for scientists to develop high performance code in a short time. From the viewpoint of productivity and software life cycle, a concise yet effective infrastructure is required to achieve parallel processing. In this paper, we propose a usable building block framework to build parallel applications on large-scale Cartesian data structures. The proposed framework is designed such that each process in a simulation cycle can easily access the generated data files with usable functions. This framework enables us to describe parallel applications with fewer lines of source code, and hence, it contributes to the productivity of the software. Further, this framework was considered for improving performance, and it was confirmed that the developed flow simulator based on this framework demonstrated considerably excellent weak scaling performance on the K computer.

Abstract: AMR technique can provide efficient numerical calculation by adapting fine cells to regions where higher numerical resolution is required. However, it is generally difficult for users to implement the AMR technique in their generic simulation programs which use uniform cells. For the purpose of carrying out numerical simulations including the AMR technique, we developed a framework for blocked-structured AMR simulation by which we can easily convert a generic uniform-cell simulation program to the one with the AMR treatment. In this paper we describe the developed framework and show the implementation of a simulation program into the framework by taking a two-dimensional advection simulation as an example.

Abstract: This paper presents a novel and efficient way of analysing big datasets used in social science research. We provide and demonstrate a way to deal with such datasets without the need for high performance distributed computational facilities. Using an Internet census dataset and with the help of freely available tools and programming libraries, we visualize global IP activity in a spatial and time dimension. We observe a considerable reduction in storage size of our dataset coupled with a faster processing time.

Abstract: The EC-Earth climate model is a seamless Earth System Model (ESM) used to carry out climate research in 24 academic institutions and meteorological services from 11 countries in Europe. This model couples several components and it is continuously under development. In this work we present a study regarding the impact of the I/O and data management when using EC-Earth in well-known supercomputing environments. Most large-scale and long-term climate simulators have been developed bearing in mind the paramount importance of its scalability. However, the computational capabilities of the High Performance Computing (HPC) environments increase at so great speed that it is almost impossible to re-implement the whole models so that they are able to exploit efficiently the new features. Therefore, it is necessary to design different strategies to take advantage of them. In this work we present an operational framework to run ensemble simulations in HPC platforms. A set of experiments are presented in order to validate the suitability of this technique. Moreover, the derived impact regarding the I/O and data management aspects is analyzed.

Abstract: With the growing scale of High Performance Computing applications comes an increase in the number of interruptions as a consequence of hardware failures. As the tendency is to scale parallel executions to hundred of thousands of processes, fault tolerance is becoming an important matter. Uncoordinated fault tolerance protocols, such as message logging, seem to be the best option since coordinated protocols might compromise applications scalability. Considering that most of the overhead during failure-free executions is caused by message logging approaches, in this paper we propose a Hybrid Message Logging protocol. It focuses on combining the fast recovery feature of pessimistic receiver-based message logging with the low protection overhead introduced by pessimistic sender-based message logging. The Hybrid Message Logging aims to reduce the overhead introduced by pessimistic receiver-based approaches by allowing applications to continue normally before a received message is properly saved. In order to guarantee that no message is lost, a pessimistic sender-based logging is used to temporarily save messages while the receiver fully saves its received messages. Experiments have shown that we can achieve up to 43% overhead reduction compared to a pessimistic receiver-based logging approach.

Abstract: In this paper, we consider our choice of a pseudorandom number generator (PRNG) in the context of running a simulation model for the growth of 3D tissues. This PRNG is the multiplicative linear congruential generator (MLCG) with carefully chosen parameters. We base our selection of this generator on three criteria. They are periodicity, randomness quality, and ease of implementation. In these regards, we review some of the pertinent theoretical properties of the employed LMCG and describe techniques used to obtain such sequences serially. Our investigation indicates that the LMCG, with properly selected parameters, can be a good, portable, user-specified, and user-controlled generator with acceptable quality. During the simulation of tissue growth, our various experiments have also shown that the ratio of the total number of random numbers consumed till confluence to the total number of computational sites in the cellular array never exceeds a certain number. This number can be used as a predictor for the period of a PRNG needed to run a particular experiment to simulate tissue growth and to estimate when a longer period may be required in order to deal with very large data sets.

Abstract: The expanding variety of observable real-world complex networks (CN) required development of mathematical models aimed to explain the nature of such constructions and to model their structure with certain precision. Existing models of CN seem to have lack of flexibility because of rigid modelling algorithm they are built upon. This might be inconvenient when there is a need to have an extended set of hypotheses about possible networks structures for some experiments. In present work we consider heuristic approach to the modelling of complex networks structures based on simulated annealing algorithm and apply it to the problem of modelling small-world networks with peculiar properties. It is shown that such approach helps to simulate realistic structures with properties unobtainable by traditional models of complex networks.

Abstract: The present work is an attempt to approach the modeling and visualization of individual opinion towards extremism in a society. We consider a scenario where two opposite concepts are on a dispute for exclusivity, via the effort of the individuals to convince each other of their own oppinion/decision. The power of convincement of each individual over the community depends on its influence, its communicability and on the enthusiasm (extremism) it defends its side. Here we propose a model taking these three factors into consideration. We analyze the system changes in real time by following the agent's extremism parameter of the society, representing its distribution in a graphical fashion and present realizations of the proposed model for a variety of initial configurations (input parameter sets). The results analysis suggest that the most important factor is influence more then the number of individuals defending a position. Moreover, communicability has small or no relevance. Visualization schemes for following individuals interactions are also included.

Abstract: In this paper we present the design and implementation of POSH, an Open-Source implementation of the OpenSHMEM standard. We present a model for its communications, and prove some properties on the memory model defined in the OpenSHMEM specification. We present some performance measurements of the communication library featured by POSH and compare them with an existing one-sided communication library.

Abstract: Developers of information systems have always utilized various visual formalisms during the design process, albeit in an informal manner. Architecture diagrams, finite state machines, and signal flow graphs are just a few examples. Model Integrated Computing (MIC) is an approach that considers these design artifacts as first class models and uses them to generate the system or subsystems automatically. Moreover, the same models can be used to analyze the system and generate test cases and documentation. MIC advocates the formal definition of these formalisms, called domain-specific modeling languages (DSML), via metamodeling and the automatic configuration of modeling tools from the metamodels. However, current MIC infrastructures are based on desktop applications that support a limited number of platforms, discourage concurrent design collaboration and are not scalable. This paper presents WebGME, a cloud- and web-based cyberinfrastructure to support the collaborative modeling, analysis, and synthesis of complex, large-scale scientific and engineering information systems. It facilitates interfacing with existing external software, such as simulators and analysis tools, it provides custom domain-specific visualization support and supports the creation of automatic code generators.

Abstract: Computational optimisation of clinically important electrocardiogram (ECG) signal features, within a single heart beat, using a Markov-chain Monte Carlo method is undertaken. A detailed, efficient data-driven software implementation of an MCMC algorithm has been shown. Initially software parallelisation is explored and has been shown that despite the large amount of model parameter inter-dependency that parallelisation is possible. Also, an initial reconfigurable hardware approach is explored for future applicability to real-time computation on a portable ECG device, under continuous extended use.

Abstract: Assortativity quantifies the tendency of nodes being connected to similar nodes in a complex network. Degree Assortativity can be quantified as a Pearson correlation. However, it is insufficient to explain assortative or disassortative tendencies of individual nodes or links, which may be contrary to the overall tendency of the network. A number of `local' assortativity measures have been proposed to address this. In this paper we define and analyse an alternative formulation for node assortativity, primarily for undirected networks. The alternative approach is justified by some inherent shortcomings of existing local measures of assortativity. Using this approach, we show that most real world scale-free networks have disassortative hubs, though we can synthesise model networks which have assortative hubs. Highlighting the relationship between assortativity of the hubs and network robustness, we show that real world networks do display assortative hubs in some instances, particularly when high robustness to targeted attacks is a necessity.

Abstract: This paper presents approach for mining and analysis of data from social media which is based on using Map Reduce model for processing big amounts of data and on using composite applications for performing more sophisticated analysis which are executed on environment for distributed computing-based cloud platform. We applied this system for creation characteristics of users who write about drugs and to estimate factors that can be used as part of model for prediction drug usage level in real world. We propose to use social media as an additional data source which complement official data sources for analysis and modeling illegal activities in society.

Abstract: This paper describes the approach to the design and implementation of a virtual learning laboratory (VLL) with the use of cloud computing technologies within the model of AaaS (Application as a Service). The formal model of composite application and a set of learning models using cloud-based VLL are proposed. The relation to learning objectives in accordance with the revised Bloom's taxonomy was identified for each model. The software tool to automate the creation and configuration VLL, based on the cloud computing platform CLAVIRE are presented. The paper ends with the description of case study implementation with the use of the offered approach.

Abstract: This paper presents a framework for the rapid development of problem solving environments in the field of Urban Science. This framework focuses on the use of shared cloud computing technologies for data processing and resource-intensive modeling, GIS-technologies for visualization of incoming data and computational results, and tools for the creation of a control graphical user interface and human-computer interaction.

Abstract: Due to the inherent discrete nature of sensing, interpolation is a key activity in sensing. Interpolation in urban computing faces an unprecedented inference issue that was not present in large scale traditional sensing environments. Obstacles such as buildings and walls are prevalent in urban sensing, and it is important to consider them in urban sensing interpolation. This paper introduces an obstacle handling interpolation in urban sensing where obstacles are present. Experimental results demonstrate that our proposed method outperforms traditional well-known interpolation methods, and test statistics verifies our method significantly improves performance at the expense of small amount of extra time.

Abstract: This paper presents a model of semantic ontologies integration into workflow composition design process via Virtual Simulation Objects (VSO) concept and technology. Domain knowledge distributed over open linked data sources may be usefully applied for new VSO-images design and used for organization computational-intensive simulation experiments. In this paper we describe the VSO-architecture extended with novel functionality regarding integration with linked open data sources. We also provide a computational-scientific example of domain-specific use-case offering a solution for some public-transportation domain problem.

Abstract: In this poster, we will present a weighted essentially non-oscillatory (WENO) scheme in space to approximate the viscosity solution of a first order Hamilton-Jacobi-Bellman (HJB) equations. The advantage of the WENO scheme is high-order accurate in space and essentially non-oscillatory. In addition to the accuracy, it is free of a CFL time step stability restriction and has small time truncation. We also know the WENO scheme is more robust than the ENO scheme. However, in general, HJB equations are not analytically solvable, and numerical approximations to the equation are normally sought in practice. For the time discretization, 3rd oder TVD Runge-Kutta or 4th order non-TVD Runge Kutta will be used.