### 7th Workshop on Biomedical and Bioinformatics Challenges for Computer Science (BBC) Session 1

#### Chair: Giuseppe A. Trunfio

 294 Mining Association Rules from Gene Ontology and Protein Networks: Promises and Challenges. [abstract]Abstract: The accumulation of raw experimental data about genes and proteins has been accompanied by the accumulation of functional information organized and stored into knowledge bases and ontologies. The assembly, organization and analysis of this data has given a considerable impulse to research. Usually biological knowledge is encoded by using annotation terms, i.e. terms describing for instance function or localization of genes and proteins. Such annotations are often organized into ontologies, that offer a formal framework to organize in a systematic way biological knowledge. For instance, Gene Ontology (GO) provides a set of annotations (namely GO Terms) of biological aspects. Consequently, for each biological concept, i.e. gene or protein a list of annotating terms is available. Each annotation may be derived using different methods, and an Evidence Code (EC) takes into account of this process. For instance electronically inferred annotations are distinguished from manual ones. Mining annotation data may thus extract biologically meaningful knowledge. For instance the analysis of these annotated data using association rules may evidence the co-occurrence of annotation helping for instance the classification of proteins starting from the annotation. Nevertheless, the use of frequent itemset mining is less popular with respect to other techniques, such as statistical based methods or semantic similarities. Here we give a short survey of these methods discussing possible future directions of research. We considered in particular the impact of the nature of annotation on association rule performances by discussing two case studies on protein complexes and protein families. As evidenced on this preliminary study the presence of electronic annotation has not a positive impact on the quality of association rules suggesting the possibility to introduce novel algorithm that are aware of evidence codes. Pietro Hiram Guzzi, Marianna Milano, Mario Cannataro 53 Automated Microalgae Image Classification [abstract]Abstract: In this paper we present a new method for automated recognition of 12 microalgae that are most commonly found in water resources of Thailand. In order to handle some difficulties encountered in our problem such as unclear algae boundary and noisy background, we proposed a new method for segmenting algae bodies from an image background and proposed a new method for computing texture descriptors from a blurry texture object. Feature combination approach is applied to handle a variation of algae shapes of the same genus. Sequential Minimal Optimization (SMO) is used as a classifier. An experimental result of 97.22% classification accuracy demonstrates an effectiveness of our proposed method. Sansoen Promdaen, Pakaket Wattuya, Nuttha Sanevas 192 A Clustering Based Method Accelerating Gene Regulatory Network Reconstruction [abstract]Abstract: One important direction of Systems Biology is to infer Gene Regulatory Networks and many methods have been developed recently, but they cannot be applied effectively in full scale data. In this work we propose a framework based on clustering to handle the large dimensionality of the data, aiming to improve accuracy of inferred network while reducing time complexity. We explored the efficiency of this framework employing the newly proposed metric Maximal Information Coefficient (MIC), which showed superior performance in comparison to other well established methods. Utilizing both benchmark and real life datasets, we showed that our method is able to deliver accurate results in fractions of time required by other state of the art methods. Our method provides as output interactions among groups of highly correlated genes, which in an application on an aging experiment were able to reveal aging related pathways. Georgios Dimitrakopoulos, Ioannis Maraziotis, Kyriakos Sgarbas, Anastasios Bezerianos 208 Large Scale Read Classification for Next Generation Sequencing [abstract]Abstract: Next Generation Sequencing (NGS) has revolutionised molecular biology, resulting in an explosion of data sets and a pressing need for rapid identification as a prelude to annotation and further analysis. NGS data consists of a substantial number of short sequence reads, given context through downstream assembly and annotation, a process requiring reads consistent with the assumed species or species group. Highly accurate results have been obtained for restricted sets using SVM classifiers, but such methods are difficult to parallelise and success depends on significant attention to feature selection. This work examines the problem at very large scale, using a mix of synthetic and real data with a view to determining the overall structure of the problem and the effectiveness of parallel ensembles of simpler classifiers (principally random forests) in addressing the challenges of large scale genomics. James Hogan, Timothy Peut

### Workshop on Cell Based and Individual Based modelling (CBIBM) Session 1

#### Chair: James Osborne

 395 The future of cell based modelling: connecting and coupling individual based models [abstract]Abstract: When investigating the development and function of multicellular biological systems it is not enough to only consider the behaviour of individual cells in isolation. For example when studying tissue development, how individual cells interact, both mechanically and biochemically, influences the resulting tissues form and function. Cell based modelling allows you to represent and track the interaction of individual cells in a developing tissue. Existing models including lattice based models (cellular automata and cellular Potts) and off-lattice based models (cell centre and vertex based representations) have given us insight into how tissues maintain homeostasis and how mutations spread. However, when tissues develop they interact biochemically and biomechanically with the environment and in order to capture these interactions, and the effect they have on development, the environment must be considered. We present a framework which allows multiple individual based models to be coupled together, in order to model both the tissue and the surrounding environment. The framework can use different modeling paradigms for each component, and subcellular behaviour (for example the cell cycle) can be considered. In this talk we present two examples of such a coupling, from the fields of developmental biology and vascular remodelling. James Osborne 206 Discrete-to-continuum modelling of nutrient-dependent cell growth [abstract]Abstract: Continuum partial differential equation models of the movement and growth of large numbers of cells generally involve constitutive assumptions about macro-scale cell population behaviour. It is difficult to know whether these assumptions accurately represent the mechanical and chemical processes that occur at the level of discrete cells. By deriving continuum models from individual-based models (IBMs) we can obtain PDE approximations to IBMs and conditions for their validity. We have developed a hybrid discrete-continuum model of nutrient-dependent growth of a line of discrete cells on a substrate in a nutrient bath. The cells are represented by linear springs connected in series, with resting lengths that evolve according to the local nutrient concentration. In turn, the continuous nutrient field changes as the cells grow due to the change in nutrient uptake with changes in cell density and the length of the cell line. Following Fozard et al. [Math. Med. and Biol., 27(1):39--74, 2010], we have derived a PDE continuum model from the discrete model ODEs for the motion of the cell vertices and cell growth by taking the large cell number limit. We have identified the conditions under which the continuum model accurately approximates the IBM by comparing numerical simulations of the two models. In addition to making the discrete and continuum frameworks more suitable for modelling cell growth by incorporating nutrient transport, our work provides conditions on the cell density to determine whether the IBM or continuum model should be used. This is an important step towards developing a hybrid model of tissue growth that uses both the IBM and its continuum limit in different regions. Lloyd Chapman, Rebecca Shipley, Jonathan Whiteley, Helen Byrne and Sarah Waters 434 Distinguishing mechanisms of cell aggregate formation using pair-correlation functions [abstract]Abstract:  Edward Green 432 Cell lineage tracing in invading cell populations: superstars revealed! [abstract]Abstract: Cell lineage tracing is a powerful tool for understanding how proliferation and differentiation of individual cells contribute to population behaviour. In the developing enteric nervous system (ENS), enteric neural crest (ENC) cells move and undergo massive population expansion by cell division within mesenchymal tissue that is itself growing. We use an agent-based model to simulate ENC colonisation and obtain agent lineage tracing data, which we analyse using econometric data analysis tools. Biological trials with clonally labelled ENS cells were also performed. In all realisations a small proportion of identical initial agents accounts for a substantial proportion of the total agent population. We term these individuals superstars. Their existence is consistent across individual realisations and is robust to changes in model parameters. However which individual agents will become a superstar is unpredictable. This inequality of outcome is amplified at elevated proliferation rate. Biological trials revealed identical and heretofore unexpected clonal behaviour. The experiments and model suggest that stochastic competition for resources is an important concept when understanding biological processes that feature high levels of cell proliferation. The results have implications for cell fate processes in the ENS and in other situations with invasive proliferative cells, such as invasive cancer. Kerry Landman, Bevan Cheeseman and Donald Newgreen 435 Agent-based modelling of the mechanism of immune control at the cellular level in HIV infection [abstract]Abstract: There are over 40 million people currently infected worldwide, and efforts to develop a vaccine would be improved greatly by a better understanding of how HIV survives and evolves. Recent studies discovered the ability of HIV target cells to present viral particles on the surface and trigger immune recognition and suppression by ÒkillerÓ cells of immune system. The effect of ÒkillersÓ remains to be poorly understood, however it plays a key role in control of HIV infection. While traditional vaccine approaches became unsuccessful, the vaccines against early expressed conservative viral parts are promising and would make possible managing the ability of the virus to mutate and avoid immune recognition. To discover the mechanism of ÒkillerÓ cells I developed an agent-based stochastic model of HIV dynamics at the cellular level. While the classic ODE approach is unable to simulate similar dynamics that I observed in the experimental data, the agent-based stochastic model is easily comprehensible and exposes similar kinetics. The complexity of the method increases greatly with the number of agents in the model and may be effectively resolved by using parallel computations on Graphics Processing Units (GPUs). I found that the simulated dynamics almost completely resembles the experimental data and provides answer on the addressed question. Also, the model may be applied in further developments on the design of experiments to distinguish mechanisms more precisely. Alexey Martyushev

### Solving Problems with Uncertainties (SPU) Session 1

#### Chair: Vassil Alexandrov

 37 Wind field uncertainty in forest fire propagation prediction [abstract]Abstract: Forest fires are a significant problem, especially in Mediterranean countries. To fight against these hazards, it is necessary to have an accurate prediction of its evolution beforehand. So, propagation models have been developed to determine the expected evolution of a forest fire. Such propagation models require input parameters to produce the predictions. Such parameters must be as accurate as possible in order to provide a prediction adjusted to the actual fire behavior. However, in many cases the information concerning the values of the input parameter is obtained by indirect measurements. Such indirect estimations imply an uncertainty degree concerning the values ​​of the parameters. This problem is very significant in the case of parameters that have a spatial distribution or variation, such as wind. The wind provided by a global weather forecast model or measured at a meteorological station in some particular point is modified by the topography of the terrain and has a different value at every point of the terrain. To estimate the wind speed and direction at each point of the terrain it is necessary to apply a wind field model that determines those values ​​at each point depending on the terrain topography. WindNinja is a wind field simulator that provides an estimate wind direction and wind speed at each point of the terrain given a meteorological wind. However, the calculation of the wind field takes some time when the map has a considerable size (30x30 Km) and the resolution is high (30x30meters). This time penalizes the prediction of forest fire spread and may eventually make impractical the effective prediction of fire spread with wind field. On the other hand, it must be considered that the data structures needed to calculate the wind field of a large map requires a large amount of memory that may not be available on a single node of a current system. To reduce the computation time of the wind field a data partition method has been applied. In this case the wind field is calculated in parallel on each part of the map and then the wind fields of the different parts are joined to form the global wind field. Furthermore, by partitioning the terrain map, the data structures necessary to resolve the wind field in each part are reduced significantly and can be stored in the memory of a single node in a current parallel system. Therefore, the existing nodes can perform computation in parallel with data that fit the capacity of the memory on each node. However, the calculation of the wind field is a complex problem which has certain border effects, so that the wind direction and speed in the points next to the border of each part may have some variability and differ from those they would have obtained if they were far from the border, for example if the wind field is calculated over a single complete map. To solve this problem, it is necessary to include a degree of overlap among the map parts. So, there is a margin from the beginning of the part and the part cells itself. The overall wind field aggregation is obtained by discarding the calculated margin fields overlap of each part. The inclusion of an overlap each part increases the execution time, but the variation in the wind field is reduced. The methodology has been tested with several terrain maps, and it was found that parts of 400x400 cells with an overlap of 50 cells per side provide a reasonable execution time (150 sec) with virtually no variation with respect to the wind field obtained with a global map. With this type of partitioning, each process solves an effective part of a map of 300x300 cells. Gemma Sanjuan, Carlos Brun, Tomas Margalef, Ana Cortes 307 A Framework for Evaluating Skyline Query over Uncertain Autonomous Databases [abstract]Abstract: The perception of skyline query is to find a set of objects that is much preferred in all dimensions. While this theory is easily applicable on certain and complete database, however, when it comes to data integration of databases where each has different representation of data in a same dimension, it would be difficult to determine the dominance relation between the underlying data. In this paper, we propose a framework, SkyQUD, to efficiently compute the skyline probability of datasets in uncertain dimensions. We explore the effects of having datasets with uncertain dimensions in relation to the dominance relation theory and propose a framework that is able to support skyline queries on this type of datasets. Nurul Husna Mohd Saad, Hamidah Ibrahim, Ali Amer Alwan, Fatimah Sidi, Razali Yaakob 253 Efficient Data Structures for Risk Modelling in Portfolios of Catastrophic Risk Using MapReduce [abstract]Abstract: The QuPARA Risk Analysis Framework~\cite{IEEEbigdata} is an analytical framework implemented using MapReduce and designed to answer a wide variety of complex risk analysis queries on massive portfolios of catastrophic risk contracts. In this paper, we present data structure improvements that greatly accelerate QuPARA's computation of Exceedance Probability (EP) curves with secondary uncertainty. Andrew Rau-Chaplin, Zhimin Yao, Norbert Zeh 40 Argumentation Approach and Learning Methods in Intelligent Decision Support Systems in the Presence of Inconsistent Data [abstract]Abstract: This paper contains a description of methods and algorithms for working with inconsistent data in intelligent decision support systems. An argumentation approach and application of rough sets for generalization problems are considered. The methods for finding the conflicts and the generalization algorithm based on rough sets are proposed. Noise models in the generalization algorithm are viewed. Experimental results are introduced. A decision of some problems that are not solvable in classical logics is given. Vadim N. Vagin, Marina Fomina, Oleg Morosin 365 Enhancing Monte Carlo Preconditioning Methods for Matrix Computations [abstract]Abstract: An enhanced version of a stochastic SParse Approximate Inverse (SPAI) preconditioner for general matrices is presented. This method is used in contrast to the standard deterministic preconditioners computed by the deterministic SPAI, and its further optimized parallel variant- Modified SParse Approximate Inverse Preconditioner (MSPAI). Thus we present a Monte Carlo preconditioner that relies on the use of Markov Chain Monte Carlo (MCMC) methods to compute a rough matrix inverse first, which is further optimized by an iterative filter process and a parallel refinement, to enhance the accuracy of the preconditioner. Monte Carlo methods quantify the uncertainties by enabling us to estimate the non-zero elements of the inverse matrix with a given precision and certain probability. The advantage of this approach is that we use sparse Monte Carlo matrix inversion whose complexity is linear of the size of the matrix. The behaviour of the proposed algorithm is studied, its performance measured and compared with MSPAI. Janko Strassburg, Vassil Alexandrov

### Main Track (MT) Session 2

#### Chair: Young Choon Lee

 152 An Empirical Study of Hadoop's Energy Efficiency on a HPC Cluster [abstract]Abstract: Map-Reduce programming model is commonly used for efficient scientific computations, as it executes tasks in parallel and distributed manner on large data volumes. The HPC infrastructure can effectively increase the parallelism of map-reduce tasks. However such an execution will incur high energy and data transmission costs. Here we empirically study how the energy efficiency of a map-reduce job varies with increase in parallelism and network bandwidth on a HPC cluster. We also investigate the effectiveness of power-aware systems in managing the energy consumption of different types of map-reduce jobs. We comprehend that for some jobs the energy efficiency degrades at high degree of parallelism, and for some it improves at low CPU frequency. Consequently we suggest strategies for configuring the degree of parallelism, network bandwidth and power management features in a HPC cluster for energy efficient execution of map-reduce jobs. Nidhi Tiwari, Santonu Sarkar, Umesh Bellur, Maria Indrawan-Santiago 167 Optimal Run Length for Discrete-Event Distributed Cluster-Based Simulations [abstract]Abstract: In scientific simulations the results generated usually come from a stochastic process. New solutions with the aim of improving these simulations have been proposed, but the problem is how to compare these solutions since the results are not deterministic. Consequently how to guarantee that the output results are statistically trusted. In this work we apply a statistical approach in order to define the transient and steady state in discrete event distributed simulation. We used linear regression and batch method to find the optimal simulation size. As contributions of our work we can enumerate: we have applied and adapted the simple statistical approach in order to define the optimal simulation length; we propose the approximate approach to normal distribution instead of generate replications sufficiently large; and the method can be used in other kind of non-terminating science simulations where the data either have a normal distribution or can be approximated by a normal distribution. Francisco Borges, Albert Gutierrez-Milla, Remo Suppi, Emilio Luque 173 A CUDA Based Solution to the Multidimensional Knapsack Problem Using the Ant Colony Optimization [abstract]Abstract: The Multidimensional Knapsack Problem (MKP) is a generalization of the basic Knapsack Problem, with two or more constraints. It is an important optimization problem with many real-life applications. It is an NP-hard problem and finding optimal solutions for MKP may be intractable. In this paper we use a metaheuristic algorithm based on ant colony optimization (ACO). Since several steps of the algorithm can be carried out concurrently, we propose a parallel implementation under the GPGPU paradigm (General Purpose Graphics Processing Units) using CUDA. To use the algorithm presented in this paper, it is necessary to balance the number of ants, number of rounds used, and whether local search is used or not, depending on the quality of the solution desired. In other words, there is a compromise between time and quality of solution. We obtained very promising experimental results and we compared our implementation with those in the literature. The results obtained show that ant colony optimization is a viable approach to solve MKP efficiently, even for large instances, with the parallel approach. Henrique Fingler, Edson Cáceres, Henrique Mongelli, Siang Song 174 Comparison of High Level FPGA Hardware Design for Solving Tri-Diagonal Linear Systems [abstract]Abstract: Reconfigurable computing devices can increase the performance of compute intensive algorithms by implementing application specific co-processor architectures. The power cost for this performance gain is often an order of magnitude less than that of modern CPUs and GPUs. Exploiting the potential of reconfigurable devices such as Field-Programmable Gate Arrays (FPGAs) is typically a complex and tedious hardware engineering task. Re- cently the major FPGA vendors (Altera, and Xilinx) have released their own high-level design tools, which have great potential for rapid development of FPGA based custom accelerators. In this paper, we will evaluate Altera’s OpenCL Software Development Kit, and Xilinx’s Vivado High Level Sythesis tool. These tools will be compared for their per- formance, logic utilisation, and ease of development for the test case of a tri-diagonal linear system solver. David Warne, Neil Kelson, Ross Hayward 181 Blood Flow Arterial Network Simulation with the Implicit Parallelism Library SkelGIS [abstract]Abstract: Implicit parallelism computing is an active research domain of computer science. Most implicit parallelism solutions to solve partial differential equations, and scientific simulations, are based on the specificity of numerical methods, where the user has to call specific functions which embed parallelism. This paper presents the implicit parallel library SkelGIS which allows the user to freely write its numerical method in a sequential programming style in C++. This library relies on four concepts which are applied, in this paper, to the specific case of network simulations. SkelGIS is evaluated on a blood flow simulation in arterial networks. Benchmarks are first performed to compare the performance and the coding difficulty of two implementations of the simulation, one using SkelGIS, and one using OpenMP. Finally, the scalability of the SkelGIS implementation, on a cluster, is studied up to 1024 cores. Hélène Coullon, Jose-Maria Fullana, Pierre-Yves Lagrée, Sébastien Limet, Xiaofei Wang

### Main Track (MT) Session 4

#### Chair: Y. Cui

 222 GPU Optimization of Pseudo Random Number Generators for Random Ordinary Differential Equations [abstract]Abstract: Solving differential equations with stochastic terms involves a massive use of pseudo random numbers. We present an application for the simulation of wireframe buildings under stochastic earthquake excitation. The inherent potential for vectorization of the application is used to its full extent on GPU accelerator hardware. A representative set of pseudo random number generators for uniformly and normally distributed pseudo random numbers has been implemented, optimized, and benchmarked. The resulting optimized variants outperform standard library implementations on GPUs. The techniques and improvements shown in this contribution using the Kanai-Tajimi model can be generalized to other random differential equations or stochastic models as well as other accelerators. Christoph Riesinger, Tobias Neckel, Florian Rupp, Alfredo Parra Hinojosa, Hans-Joachim Bungartz 229 Design and Implementation of Hybrid and Native Communication Devices for Java HPC [abstract]Abstract: MPJ Express is a messaging system that allows computational scientists to write and execute parallel Java applications on High Performance Computing (HPC) hardware. The software is capable of executing in two modes namely cluster and multicore modes. In the cluster mode, parallel applications execute in a typical cluster environment where multiple processing elements communicate with one another using a fast interconnect like Gigabit Ethernet or other proprietary networks like Myrinet and Infiniband. In this context, the MPJ Express library provides communication devices for Ethernet and Myrinet. In the multicore mode, the parallel Java application executes on a single system comprising of shared memory or multicore processors. In this paper, we extend the MPJ Express software to provide two new communication devices namely the native and hybrid device. The goal of the native communication device is to interface the MPJ Express software with native—typically written in C—MPI libraries. In this setting the bulk of messaging logic is offloaded to the underlying MPI library. This is attractive because MPJ Express can exploit latest features, like support for new interconnects and efficient collective communication algorithms of the native MPI library. The second device, called the hybrid device, is developed to allow efficient execution of parallel Java applications on clusters of shared memory or multicore processors. In this setting the MPJ Express runtime system runs a single multithreaded process on each node of the cluster—the number of threads in each process is equivalent to processing elements within a node. Our performance evaluation reveals that the native device allows MPJ Express to achieve comparable performance to native MPI libraries—for latency and bandwidth of point-to-point and collective communications—which is a significant gain in performance compared to existing communication devices. The hybrid communication device—without any modifications at application level—also helps parallel applications achieve better speedups and scalability. We witnessed comparative performance for various benchmarks—including NAS Parallel Benchmarks—with hybrid device as compared to the existing Ethernet communication device on a cluster of shared memory/multicore processors. Bibrak Qamar, Ansar Javed, Mohsan Jameel, Aamir Shafi, Bryan Carpenter 231 Deploying a Large Petascale System: the Blue Waters Experience [abstract]Abstract: Deployment of a large parallel system is typically a very complex process, involving several steps of preparation, delivery, installation, testing and acceptance. Despite the availability of various petascale machines currently, the steps and lessons from their deployment are rarely described in the literature. This paper presents the experiences observed during the deployment of Blue Waters, the largest supercomputer ever built by Cray and one of the most powerful machines currently available for open science. The presentation is focused on the final deployment steps, where the system was intensively tested and accepted by NCSA. After a brief introduction of the Blue Waters architecture, a detailed description of the set of acceptance tests employed is provided, including many of the obtained results. This is followed by the major lessons learned during the process. Those experiences and lessons should be useful to guide similarly complex deployments in the future. Celso Mendes, Brett Bode, Gregory Bauer, Jeremy Enos, Cristina Beldica, William Kramer 248 FPGA-based acceleration of detecting statistical epistasis in GWAS [abstract]Abstract: Genotype-by-genotype interactions (epistasis) are believed to be a significant source of unexplained genetic variation causing complex chronic diseases but have been ignored in genome-wide association studies (GWAS) due to the computational burden of analysis. In this work we show how to benefit from FPGA technology for highly parallel creation of contingency tables in a systolic chain with a subsequent statistical test. We present the implementation for the FPGA-based hardware platform RIVYERA S6-LX150 containing 128 Xilinx Spartan6-LX150 FPGAs. For performance evaluation we compare against the method iLOCi. iLOCi claims to outperform other available tools in terms of accuracy. However, analysis of a dataset from the Wellcome Trust Case Control Consortium (WTCCC) with about 500,000 SNPs and 5,000 samples still takes about 19 hours on a MacPro workstation with two Intel Xeon quad-core CPUs, while our FPGA-based implementation requires only 4 minutes. Lars Wienbrandt, Jan Christian Kässens, Jorge González-Domínguez, Bertil Schmidt, David Ellinghaus, Manfred Schimmler

### Main Track (MT) Session 7

#### Chair: Maria Indrawan-Santiago

 321 Evolving Agent-based Models using Complexification Approach [abstract]Abstract: This paper focuses on parameter search for multi-agent based models using evolutionary algorithms. Large numbers and variable dimensions of parameters require a search method which can efficiently handle a high dimensional search space. We are proposing the use of complexification as it emulates the natural way of evolution by starting with a small constrained search space and expanding it as the evolution progresses. We examined the effects of this method on an EA by evolving parameters for two multi-agent based models. Michael Wagner, Wentong Cai, Michael Harold Lees, Heiko Aydt 356 Discrete modeling and simulation of business processes using event logs. [abstract]Abstract: An approach to business process modelling for short term KPI prediction, based on event logs and values of environment variables, is proposed. Ready-for-simulation process model is built semi-automatically, expert only inputs desired environment variables, which are used as features during the learning process. Process workflow is extracted as a Petri Net model using a combination of process mining algorithms. Dependencies between features and process variables are formalized using decision and regression trees techniques. Experiments were conducted to predict KPIs of real companies. Ivan Khodyrev, Svetlana Popova 376 Modeling and Simulation Framework for Development of Interactive Virtual Environments [abstract]Abstract: The article presents a framework for interactive virtual environments’ development for simulation and modeling of complex systems. The framework uses system’s structural model as a core concept for composition and control of simulation-based scientific experiments not in terms of technological processes or workflows but in terms of domain-specific objects and their interconnection within the investigated system. The proposed framework enables integration and management of resources available within a cloud computing environment in order to support automatic simulation management and to provide the user with an interactive visual domain-specific interface to the system. Konstantin Knyazkov, Sergey Kovalchuk 34 Using interactive 3D game play to make complex medical knowledge more accessible [abstract]Abstract: This research outlines a new approach, that takes complex medical, nutritional & activity data and presents it to the diabetic patient in the form of a mobile app/game that uses interactive 3D computer graphics & game play to make this complex information more accessible. The pilot randomized control study results indicate that the Diabetes Visualizer’s use of interactive 3D game play increased the participants understanding of the condition, and its day-to-day management. More importantly the Diabetes Visualizer app stimulated participants interest in, and desire to engage in the task of diabetes management. Dale Patterson

### Main Track (MT) Session 8

#### Chair: Michela Taufer

 115 The influence of network topology on reverse-engineering of gene-regulatory networks [abstract]Abstract: Modeling and simulation of gene-regulatory networks (GRNs) has become an important aspect of modern computational biology investigations into gene regulation. A key challenge in this area is the automated inference (reverse-engineering) of dynamic, mechanistic GRN models from gene expression time-course data. Common mathematical formalisms used to represent such models capture both the relative weight or strength of a regulator gene and the type of the regulator (activator, repressor) with a single model parameter. The goal of this study is to quantify the role this parameter plays in terms of the computational performance of the reverse-engineering process and the predictive power of the inferred GRN models. We carried out three sets of computational experiments on a GRN system consisting of 22 genes. While more comprehensive studies of this kind are ultimately required, this computational study demonstrates that models with similar training (reverse-engineering) error that have been inferred under varying degrees of a priori known topology information, exhibit considerably different predictive performance. This study was performed with a newly developed multiscale modeling and simulation tool called MultiGrain/MAPPER. Alexandru Mizeranschi, Noel Kennedy, Paul Thompson, Huiru Zheng, Werner Dubitzky 188 Maximizing the Cumulative Influence through a Social Network when Repeat Activation Exists [abstract]Abstract: We study the problem of employing social networks for propagate influence when repeat activation is involved. While influence maximization has been extensively studied as the fundamental solution, it neglects the reality that a user may purchase a product/service repeatedly, incurring cumulative sales of the product/service. In this paper, we explore a new problem of cumulative influence maximization that brings the influence maximization a step closer to real-world viral marketing applications. In our problem setting, repeat activation exists and we aim to find a set of initial users, through which the maximal cumulative influence can be stimulated in a given time period. To describe the repeat activation behavior, we adopt the voter model to reflect the variation of activations over time. Under the voter model, we formulate the maximization problem and present an effective algorithm. We test and compare our method with heuristic algorithms on real-world data sets. Experimental results demonstrate the utility of the proposed method. Chuan Zhou, Peng Zhang, Wenyu Zang, Li Guo 320 Mining Large-scale Knowledge about Events from Web Text [abstract]Abstract: This paper addresses the problem of automatic acquisition of semantic relations between events. Since most of the previous researches rely on annotated corpus, the main challenge is the need for more generic methods to identify related event pairs and to extract event-arguments (particularly the predicate, subject and object). Motivated by this background, we develop a three-phased approach that acquires causality from the Web. Firstly, we use explicit connective markers (such as “because”) as linguistic cues to discover causal related events. Then, we extract the event-arguments based on local dependency parse trees of event expressions. In the final phase, we propose a statistical model to measure the potential causal relations. The present results of our empirical evaluation on a large-scale Chinese Web corpus have shown that (a) the use of local dependency tree extensively improves both the accuracy and recall of event-arguments extraction task; (b) our measure which is an improvement on PMI has a better performance. Yanan Cao, Peng Zhang, Jing Guo, Li Guo 200 Discovering Multiple Diffusion Source Nodes in Social Networks [abstract]Abstract: Social networks have greatly amplified spread of information across different communities. However, we recently observe that various malicious information, such as computer virus and rumors, are broadly spread via social networks. To restrict these malicious information, it is critical to develop effective method to discover the diffusion source nodes in social networks. Many pioneer works have explored the source node identification problem, but they all based on an ideal assumption that there is only a single source node, neglecting the fact that malicious information are often diffused from multiple sources to intentionally avoid network audit. In this paper, we present a multi-source locating method based on a given snapshot of partially and sparsely observed infected nodes in the network. Specifically, we first present a reverse propagation method to detect recovered and unobserved infected nodes in the network, and then we use community cluster algorithms to change the multi-source locating problem into a bunch of single source locating problems. At the last step, we identify the nodes having the largest likelihood estimations as the source node on the infected clusters. Experiments on three different types of complex networks show the performance of the proposed method. Wenyu Zang, Peng Zhang, Chuan Zhou, Li Guo 293 The Origin of Control in Complex Networks [abstract]Abstract: Recent work at the borders of network science and control theory have begun to investigate the control of complex systems by studying their underlying network representations. A majority of the work in this nascent field has looked at the number of controls required in order to fully control a network. In this talk I will present research that provides a ready breakdown of this number into categories that are both easy to observe in real world networks as well as instructive in understanding the underlying functional reasons for why the controls must exist. This breakdown is able to shed light on several observations made in the previous literature regarding controllability of networks. This decomposition produces a mechanism to cluster networks into classes that are consistent with their large scale architecture and purpose. Finally, we observe that synthetic models of formation generate networks with control breakdowns substantially different from what is observed in real world networks. Justin Ruths

### Main Track (MT) Session 11

#### Chair: Dieter Kranzlmuller

 360 Workflow as a Service in the Cloud: Architecture and Scheduling Algorithms [abstract]Abstract: With more and more workflow systems adopting cloud as their execution environment, it becomes increasingly challenging on how to efficiently manage various workflows, virtual machines (VMs) and workflow execution on VM instances. To make the system scalable and easy-to-extend, we design a Workflow as a Service (WFaaS) architecture with independent services. A core part of the architecture is how to efficiently respond continuous workflow requests from users and schedule their executions in the cloud. Based on different targets, we propose four heuristic workflow scheduling algorithms for the WFaaS architecture, and analyze the differences and best usages of the algorithms in terms of performance, cost and the price/performance ratio via experimental studies. Jianwu Wang, Prakashan Korambath, Ilkay Altintas, Jim Davis, Daniel Crawl 36 Large Eddy Simulation of Flow in Realistic Human Upper Airways with Obstructive Sleep Apnea [abstract]Abstract: Obstructive sleep apnea (OSA) is a common type of sleep disorder characterized by abnormal repetitive cessation in breathing during sleep caused by partial or complete narrowing of pharynx in the upper airway. The upper airway surgery is commonly performed for this disorder, however the success rate is limited because the lack of the thorough understanding of the primary mechanism associated with OSA. The computational fluid dynamics (CFD) simulation with Large Eddy Simulation approach is conducted to investigate a patient-specific upper airway flow with severe OSA. Both pre and post-surgical upper airway models are simulated to reveal the effect of the surgical treatment. Only the inhaled breathing is conducted with six periods (about 15 second) unsteady flow. Compared with the results before and after treatment, it is illustrated that there exists a significant pressure and shear stress dropping region near the soft palate before treatment; and after the treatment the flow resistance in the upper airway is decreased and the wall shear stress value is significantly reduced. Mingzhen Lu, Yang Liu, Jingying Ye, Haiyan Luo 86 Experiments on a Parallel Nonlinear Jacobi-Davidson Algorithm [abstract]Abstract: The Jacobi-Davidson (JD) algorithm is very well suited for the computation of a few eigenpairs of large sparse complex symmetric nonlinear eigenvalue problems. The performance of JD crucially depends on the treatment of the so-called correction equation, in particular the preconditioner, and the initial vector. Depending on the choice of the spectral shift and the accuracy of the solution, the convergence of JD can vary from linear to cubic. We investigate parallel preconditioners for the Krylov space method used to solve the correction equation. We apply our nonlinear Jacobi-Davidson (NLJD) method to quadratic eigenvalue problems that originate from the time-harmonic Maxwell equation for the modeling and simulation of resonating electromagnetic structures. Yoichi Matsuo, Hua Guo, Peter Arbenz 184 Improving Collaborative Recommendation via Location-based User-Item Subgroup [abstract]Abstract: Collaborative filter has been widely and successfully applied in recommendation system. It typically associates a user with a group of like-minded users based on their preferences over all the items, and recommends to the user those items enjoyed by others in the group. Some previous studies have explored that there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items and subgroup analysis can get better accuracy. While, we find that geographical information of user have impacts on user group preference for items. Hence, In this paper, we propose a Bayesian generative model to describe the generative process of user-item subgroup preference under considering users' geographical information. Experimental results show the superiority of the proposed model. Zhi Qiao, Peng Zhang, Yanan Cao, Chuan Zhou, Li Guo 90 Optimizing Shared-Memory Hyperheuristics on top of Parameterized Metaheuristics [abstract]Abstract: This paper studies the auto-tuning of shared-memory hyperheuristics developed on top of a unified shared-memory metaheuristic scheme. A theoretical model of the execution time of the unified scheme is empirically adapted for particular metaheuristics and hyperheuristics through experimentation. The model is used to decide at running time the number of threads to obtain a reduced execution time. The number of threads is different for the different basic functions in the scheme, and depends on the problem to be solved, the metaheuristic scheme, the implementation of the basic functions and the computational system where the problem is solved. The applicability of the proposal is shown with a problem of minimization of electricity consumption in exploitation of wells. Experimental results show that satisfactory execution times can be achieved with auto-tuning techniques based on theoretical-empirical models of the execution time. José Matías Cutillas Lozano, Domingo Gimenez

### Main Track (MT) Session 12

#### Chair: Luiz DeRose

 187 The K computer Operations: Experiences and Statistics [abstract]Abstract: The K computer, released on September 29, 2012, is a large-scale parallel supercomputer system consisting of 82,944 compute nodes. We have been able to resolve a significant number of operation issues since its release. Some system software components have been fixed and improved to obtain higher stability and utilization. We achieved 94% service availability because of a low hardware failure rate and approximately 80% node utilization by careful adjustment of operation parameters. We found that the K computer is an extremely stable and high utilization system. Keiji Yamamoto, Atsuya Uno, Hitoshi Murai, Toshiyuki Tsukamoto, Fumiyoshi Shoji, Shuji Matsui, Ryuichi Sekizawa, Fumichika Sueyasu, Hiroshi Uchiyama, Mitsuo Okamoto, Nobuo Ohgushi, Katsutoshi Takashina, Daisuke Wakabayashi, Yuki Taguchi, Mitsuo Yokokawa 195 Quantum mechanics study of hexane isomers through gamma-ray and graph theory combined with C1s binding energy and nuclear magnetic spectra (NMR) [abstract]Abstract: Quantum mechanically calculated positron-electron annihilation gamma-ray spectra, C1s binding energy spectra and NMR spectra are employed to study the electronic structures of hexane and its isomers, which is assisted using graph theory. Our recent positron-electron annihilation gamma-ray spectral study of n-hexane in gas phase and core ionization (IPs) spectral studies for small alkanes and their isomers, have paved the path for the present correlation study where quantum mechanics is combined with graph theory, C1s ionization spectroscopy and nuclear magnetic resonance (NMR), to further understand the electronic structure and topology for the hexane isomers. The low-energy plane wave positron (LEPWP) model indicated that the positrophilic electrons of a molecule are dominated by the electrons in the lowest occupied valence orbital (LOVO). The most recent results using NOMO indicated that the electronic wave functions dominate the electron-positron wave functions for molecular systems. In addition to quantum mechanics, chemical graphs are also studied and are presented in the present study. Subhojyoti Chatterjee and Feng Wang 257 Dendrogram Based Algorithm for Dominated Graph Flooding [abstract]Abstract: In this paper, we are concerned with the problem of flooding undirected weighted graphs under ceiling constraints. We provide a new algorithm based on a hierarchical structure called {\em dendrogram}, which offers the significant advantage that it can be used for multiple flooding with various scenarios of the ceiling values. In addition, when exploring the graph through its dendrogram structure in order to calculate the flooding levels, independent sub-dendrograms are generated, thus offering a natural way for parallel processing. We provide an efficient implementation of our algorithm through suitable data structures and optimal organisation of the computations. Experimental results show that our algorithm outperforms well established classical algorithms, and reveal that the cost of building the dendrogram highly predominates over the total running time, thus validating both the efficiency and the hallmark of our method. Moreover, we exploit the potential parallelism exposed by the flooding procedure to design a multi-thread implementation. As the underlying parallelism is created on the fly, we use a queue to store the list of the sub-dendrograms to be explored, and then use a dynamic round-robin scheduling to assign them to the participating threads. This yields a load balanced and scalable process as shown by additional benchmark results. Our program runs in few seconds on an ordinary computer to flood graphs with more that $20$ millions of nodes. Claude Tadonki 278 HP-DAEMON: High Performance Distributed Adaptive Energy-efficient Matrix-multiplicatiON [abstract]Abstract: The demands of improving energy efficiency for high performance scientific applications arise crucially nowadays. Software-controlled hardware solutions directed by Dynamic Voltage and Frequency Scaling (DVFS) have shown their effectiveness extensively. Although DVFS is beneficial to green computing, introducing DVFS itself can incur non-negligible overhead, if there exist a large number of frequency switches issued by DVFS. In this paper, we propose a strategy to achieve the optimal energy savings for distributed matrix multiplication via algorithmically trading more computation and communication at a time adaptively with user-specified memory costs for less DVFS switches, which saves 7.5% more energy on average than a classic strategy. Moreover, we leverage a high performance communication scheme for fully exploiting network bandwidth via pipeline broadcast. Overall, the integrated approach achieves substantial energy savings (up to 51.4%) and performance gain (28.6% on average) compared to ScaLAPACK pdgemm() on a cluster with an Ethernet switch, and outperforms ScaLAPACK and DPLASMA pdgemm() respectively by 33.3% and 32.7% on average on a cluster with an Infiniband switch. Li Tan, Longxiang Chen, Zizhong Chen, Ziliang Zong, Rong Ge, Dong Li 279 Evaluating the Performance of Multi-tenant Elastic Extension Tables [abstract]Abstract: An important challenge in the design of databases that support multi-tenant applications is to provide a platform to manage large volumes of data collected from different businesses, social media networks, emails, news, online texts, documents, and other data sources. To overcome this challenge we proposed in our previous work a multi-tenant database schema called Elastic Extension Tables (EET) that combines multi-tenant relational tables and virtual relational tables in a single database schema. Using this approach, the tenants’ tables can be extended to support the requirements of individual tenants. In this paper, we discuss the potentials of using EET multi-tenant database schema, and show how it can be used for managing physical and virtual relational data. We perform several experiments to measure the feasibility and effectiveness of EET by comparing it with a commercially available multi-tenant schema mapping technique used by SalesForce.com. We report significant performance improvements obtained using EET when compared to Universal Table Schema Mapping (UTSM), making the EET schema a good candidate for the management of multi-tenant data in Software as a Service (SaaS) and Big Data applications. Haitham Yaish, Madhu Goyal, George Feuerlicht

### Main Track (MT) Session 14

#### Chair: Jin Chao Jin

 207 Visual Analytics of Topological Higher Order Information for Emergency Management based on Tourism Trajectory Datasets [abstract]Abstract: Trajectory datasets have presented new opportunities for spatial computing applications and geo-informatics technologies with regard to emergency management. Existing research of trajectory analysis and data mining mainly employs algorithmic approaches and analyzing geometric information of trajectories. This study presents an efficient analytics tool based on visualization approaches for analyzing large volume of trajectory datasets. This approach is particular useful for emergency management when critical decisions based on semantic information are needed. Tourism trajectory datasets are used to demonstrate the proposed approach. Ye Wang, Kyungmi Lee, Ickjai Lee 238 Modulight : A Framework for Efficient Dynamic Interactive Scientific Visualization [abstract]Abstract: The interactive scientific visualization applications are based on heterogeneous codes to implement simulation or data processing, visualization and interaction parts. These different parts need to be precisely assemble to construct an efficient application running in interactive time. Component-based approach is a good paradigm to express this kind of applications. The interactive scientific visualization domain is now classically extended with visual analysis applications. In this case, some parts of the application need to be added or removed dynamically during its execution. In this paper, we describe a component-based approach dedicated to dynamic interac- tive scientific visualization applications. We propose a framework called Modulight which implements our approach using the MPI2 library and the optimized socket library ØMQ. The performance of this framework is also analyzed from a real-life application of molecular dynamics. Sébastien Limet, Millian Poquet, Sophie Robert 289 Visualization of long-duration acoustic recordings of the environment [abstract]Abstract: Acoustic recordings of the environment are an important aid to ecologists monitoring biodiversity and environmental health. However, rapid advances in recording technology, storage and computing make it possible to accumulate thousands of hours of recordings, of which, ecologists can only listen to a small fraction. The big-data challenge addressed in this paper is to visualize the content of long-duration audio recordings on multiple scales, from hours, days, months to years. The visualization should facilitate navigation and yield ecologically meaningful information. Our approach is to extract (at one minute resolution) acoustic indices which reflect content of ecological interest. An acoustic index is a statistic that summarizes some aspect of the distribution of acoustic energy in a recording. We combine indices to produce false-color images that reveal acoustic content and facilitate navigation through recordings that are months or even years in duration. Michael Towsey, Liang Zhang, Mark Cottman-Fields, Jason Wimmer, Jinglan Zhang, Paul Roe 362 A computational science agenda for programming language research [abstract]Abstract: Scientific models are often expressed as large and complicated programs. These programs embody numerous assumptions made by the developer (e.g. for differential equations, the discretization strategy and resolution). The complexity and pervasiveness of these assumptions means that often the only true description of the model is the software itself. This has led various researchers to call for scientists to publish their source code along with their papers. We argue that this is unlikely to be beneficial since it is almost impossible to separate implementation assumptions from the original scientific intent. Instead we advocate higher-level abstractions in programming languages, coupled with lightweight verification techniques such as specification and type systems. In this position paper, we suggest several novel techniques and outline an evolutionary approach to applying these to existing and future models. One-dimensional heat flow is used as an example throughout. Dominic Orchard, Andrew Rice

### Agent Based Simulations, Adaptive Algorithms and Solvers (ABS-AA-S) Session 1

#### Chair: Maciej Paszynski

 130 PETIGA: HIGH-PERFORMANCE ISOGEOMETRIC ANALYSIS OF PHASE-FIELD MODELS [abstract]Abstract: \begin{document} We have developed fast implementations of B-spline/NURBS based finite element solvers, written using PETSc. PETSc is frequently used in software packages to leverage its optimized and parallel implementation of solvers, however we also are using PETSc data structures to assemble the linear systems. These structures in PETSC (called DA‚Äôs) were originally intended for the parallel assembly of linear systems resulting from finite differences. We have reworked this structure for linear systems resulting from isogeometric analysis based on tensor product spline spaces. The result of which is the PetIGA framework for solving problems using isogeometric analysis which is scalable and greatly simplified over previous solvers. Our infrastructure has also allowed us to develop scalable solvers for a variety of problems. We have chosen to pursue nonlinear time dependent problems~\cite{PetIGAp, PetIGAc}, such as: \begin{itemize} \item Cahn-Hilliard \item Navier-Stokes-Korteweg \item Variational Multiscale for Navier-Stokes \item Diffusive Wave Approximation to Shallow Water Equations \item Phase-Field Crystal (PFC) equation and its time integration \item Divergence-conforming B-spline modeling of nanoparticle suspensions \end{itemize} We also have solvers for an assortment of linear problems: Poisson, elasticity, Helmholtz, thin shells, advection-diffusion, and diffusion-reaction. All solvers are written to be inherently parallel and run on anything from a laptop to a supercomputer such as Shaheen, KAUST‚Äôs IBM-BlueGeneP supercomputer. In this presentation we will focus on new time integration techniques for phase-field modeling which are energy stable and allow for stable linearizations of the underlying non-linear model~\cite{PFC}. \begin{thebibliography}{99} \setlength{\parskip}{0pt} \bibitem{PetIGAp} N. Collier, L. Dalcin, and V.M. Calo, PetIGA: High-Performance Isogeometric Analysis,'' submitted, 2013. \bibitem{PetIGAc} L. Dalcin and N. Collier, PetIGA: A framework for high performance Isogeometric Analysis,'' https://bitbucket.org/dalcinl/petiga/, 2013 \bibitem{PFC} P. Vignal, L. Dalcin, D.L. Brown, N. Collier, and V.M. Calo, Energy-stable time-discretizations for the phase-Ô¨Åeld crystal equation,'' in preparation, 2014. \end{thebibliography} Victor Calo, Nathan Collier, Lisandro Dalcin and Philippe Vignal 44 Graph grammar based multi-thread multi-frontal direct solver with Galois scheduler [abstract]Abstract: In this paper, we present a multi-frontal solver algorithm for the adaptive finite element method expressed by graph grammar productions. The graph grammar productions construct first the binary elimination tree, and then process frontal matrices stored in distributed manner in nodes of the elimination tree. The solver is specialized for a class of one, two and three dimensional h refined meshes whose elimination tree has a regular structure. In particular, this class contains all one dimensional grids, two and three dimensional grids refined towards point singularities, two dimensional grids refined in an anisotropic way towards edge singularity as well as three dimensional grids refined in an anisotropic way towards edge or face singularities. In all these cases, the structure of the elimination tree and the structure of the frontal matrices are similar. The solver is implemented within the Galois environment, which allows parallel execution of graph grammar productions. We also compare the performance of the Galois implementation of our graph grammar based solver with the MUMPS solver Damian Goik, Konrad Jopek, Maciej Paszynski, Andrew Lenharth, Donald Nguyen, Keshav Pingali 154 Automatically Adapted Perfectly Matched Layers for Problems with High Contrast Materials Properties [abstract]Abstract: For the simulation of wave propagation problems, it is necessary to truncate the computational domain. Perfectly Matched Layers are often employed for that purpose, especially in high contrast layered materials where absorbing boundary conditions are difficult to design. In here, we define a Perfectly Matched Layer that automatically adjusts its parameters without any user interaction. The user only has to indicate the desired decay in the surrounding layer. With this Perfectly Matched Layer, we show that even in the most complex scenarios where the material contrast properties are as high as sixteen orders of magnitude, we do not introduce numerical reflections when truncating the domain, thus, obtaining accurate solutions. Julen Alvarez-Aramberri, David Pardo, Helene Barucq, Elisabete Alberdi Celaya 127 A Linear Complexity Direct Solver for H-adaptive Grids With Point Singularities [abstract]Abstract: In this paper we present a theoretical proof of linear computational cost and complexity for a recently developed direct solver driven by hypergraph grammar productions. The solver is specialized for computational meshes with point singularities in two and three dimensions. Linear complexity is achieved due to utilizing the special structure of such grids. We describe the algorithm and estimate the exact computational cost on an example of a two-dimensional mesh containing a point singularity. We extend this reasoning to the three dimensional meshes. Numerical results fully support our theoretical estimates. Piotr Gurgul 436 Towards a new software tool for conservation planning [abstract]Abstract: In a dynamic world, the process of prioritizing where to invest limited conservation resources is extremely complex. It needs to incorporate information on features (species, or landforms), planning units, ongoing or predicted future threats, and the costs and effectiveness of potential conservation actions. Extended research has been conducted on the spatial and temporal conservation prioritization using software tools such as Marxan, C-Plan, and Zonation to aid managers in their decision-making process. However, these tools are limited in various ways in addressing the full complexity of day-to-day management decisions. Some tools fail to consider variation in: land values in space and time; multiple threats and their spatio-temporal variations; multiple conservation actions applied to individual areas; the feasibility, effectiveness, and varying costs of actions; and the dynamic nature of biodiversity responses in space and time. Optimizing such a multi-dimensional system is a large challenge in complexity mathematics. What is needed is a new software tool that builds on current approaches, but allows for more realistic scenarios as described above, developed and parameterised in close collaboration with managers. This includes the modification of existing tools and the creation of new algorithms. The new software will be trialled in conservation planning exercises for islands in north-western Western Australia and the Great Barrier Reef. The current approaches mostly exploit simulated annealing as it was proven the fastest and sufficiently efficient for problems which do not need the best solution. The new software, however, intends to include sub-models on threats, costs, and contribution of action on individual islands. We are examining the option of use constraint programming to incorporate these sub-models into the decision process, with desirable time resolution. Jana Brotankova, Bob Pressey, Ian Craigie, Steve Hall, Amelia Wenger

### Agent Based Simulations, Adaptive Algorithms and Solvers (ABS-AA-S) Session 3

#### Chair: Aleksander Byrski

 325 Agent-based Evolutionary Computing for Diﬃcult Discrete Problems [abstract]Abstract: Hybridizing agent-based paradigm with evolutionary computation can enhance the field of meta-heuristics in a significant way, giving to usually passive individuals autonomy and capabilities of perception and interaction with other ones, treating them as agents. In the paper as a follow-up to the previous research, an evolutionary multi-agent system (EMAS) is examined in difficult discrete benchmark problems. As a means for comparison, classical evolutionary algorithm (constructed along with Michalewicz model) implemented in island-model is used. The results encourage for further research regarding application of EMAS in discrete problem domain. Michal Kowol, Aleksander Byrski, Marek Kisiel-Dorohinicki 225 Translation of graph-based knowledge representation in multi-agent system [abstract]Abstract: Agents provide a feasible mean for maintaining and manipulating large scale data. This paper deals with the problem of information exchange between different agents. It uses graph based formalism for the representation of knowledge maintained by an agent and graph transformations as a mean of knowledge exchange. Such a rigorous formalism ensures the cohesion of graph-based knowledge held by agents after each modification and exchange action. The approach presented in this paper is illustrated by a case study dealing with the problem of personal data held in different places (maintained by different agents) and the process of transmitting such information Leszek Kotulski, Adam Sedziwy, Barbara Strug 239 Agent-based Adaptation System for Service-Oriented Architectures Using Supervised Learning [abstract]Abstract: In this paper we propose an agent-based system for Service-Oriented Architecture self-adaptation. Services are supervised by autonomous agents which are responsible for deciding which service should be chosen for interoperation. Agents learn the choice strategy autonomously using supervised learning. In experiments we show that supervised learning (Naive Bayes, C4.5 and Ripper) allows to achieve much better efficiency than simple strategies such as random choice or round robin. What is also important, supervised learning generates a knowledge in a readable form, which may be analyzed by experts. Bartlomiej Sniezynski 324 Generation-free Agent-based Evolutionary Computing [abstract]Abstract: Metaheuristics resulting from the hybridization of multi-agent systems with evolutionary computing are efficient in many optimization problems. Evolutionary multi-agent systems (EMAS) are more similar to biological evolution than classical evolutionary algorithms. However, technological limitations prevented the use of fully asynchronous agents in previous EMAS implementations. In this paper we present a new algorithm for agent-based evolutionary computations. The individuals are represented as fully autonomous and asynchronous agents. Evolutionary operations are performed continuously and no artificial generations need to be distinguished. Our results show that such asynchronous evolutionary operators and the resulting absence of explicit generations lead to significantly better results. An efficient implementation of this algorithm was possible through the use of Erlang technology, which natively supports lightweight processes and asynchronous communication. Daniel Krzywicki, Jan Stypka, Piotr Anielski, Lukasz Faber, Wojciech Turek, Aleksander Byrski, Marek Kisiel-Dorohinicki 27 Hypergraph grammar based linear computational cost solver for three dimensional grids with point singularities [abstract]Abstract: In this paper we present a hypergraph grammar based multi-frontal solver for three dimensional grids with point singularities. We show experimentally that the computational cost of the resulting solver algorithm is linear with respect to the number of degrees of freedom. We also propose a reutilization algorithm that enables to reuse LU factorizations over unrefined parts of the mesh when new local refinements are executed by the hypergraph grammar productions. Piotr Gurgul, Anna Paszynska, Maciej Paszynski

### Urgent Computing: Computations for Decision Support in Critical Situations (UC) Session 1

#### Chair: Alexander Boukhanovsky

 429 High Performance Computations for Decision Support in Critical Situations: Introduction to the Third Workshop on Urgent Computing [abstract]Abstract: This paper is the preface to the Third Workshop on Urgent Computing. The Urgent Computing workshops have been traditionally embedded in frame of International Conference of Computational Science (ICCS) since 2012. They are aimed to develop a dialogue on the present and future of research and applications associated with the large-scale computations for decision support in critical situations. The key workshop topics in 2014 are: methods and principles of urgent computing, middleware, platforms and infrastructures, simulation-based decision support for complex systems control, interactive visualization and virtual reality for decision support in emergency situations, domain-area applications to emergency situations, including natural and man-made disasters, e.g. transportation problems, epidemics, criminal acts, etc. Alexander Boukhanovsky, Marian Bubak 342 Personal decision support mobile service for extreme situations [abstract]Abstract: This article discusses aspects of implementation of a massive personal decision support mobile service for evacuation process in extreme situations, based on second-generation cloud computation platform CLAVIRE and a virtual society model. The virtual society model was constructed using an agent-based approach. To increase credibility the individual motivation methods (personal decision support and user training) were used. Vladislav A. Karbovskii, Daniil V. Voloshin, Kseniia A. Puzyreva, Aleksandr S. Zagarskikh 357 Evaluation of in-vehicle decision support system for emergency evacuation [abstract]Abstract: One of the most important issues in Decision Support Systems (DSS) technology is in ensuring their effectiveness and efficiency for future implementations and use. DSS is prominent tool in disaster information system, which allows the authority to provide life safety information directly to the mobile devices of anyone physically located in the evacuation area. After that a personal DSS guides users to a safe point. Due to the large uncertainty in initial conditions and assumptions on underlying process such DSS is extremely hard for implementation and evaluation, particularly in real environment. We propose a simulation methodology for the evaluation of in-vehicle DSS for emergency evacuation based on transport system and human decision-making modeling. Sergei Ivanov, Konstantin Knyazkov 358 Problem solving environment for development and maintenance of St. Petersburg’s Flood Warning System [abstract]Abstract: Saint-Petersburg Flood Warning System (FWS) is a life-critical system that requires permanent maintenance and development. Tasks that arise during these processes could be much more resource-intensive than an operational loop of the system and may involve complex problems for research. Thereby it is essential to have a special software tool to handle a collection of different models, data sources and auxiliary software that they could be combined in different ways according to a particular research problem to be solved. This paper aims to share the idea of Saint-Petersburg FWS evolution with help of problem-solving environment based on the cloud platform CLAVIRE. Sergey Kosukhin, Anna Kalyuzhnaya, Denis Nasonov

### Urgent Computing: Computations for Decision Support in Critical Situations (UC) Session 2

#### Chair: Alexander Boukhanovsky

 366 Hybrid scheduling algorithm in early warning [abstract]Abstract: Investigations in development of efficient early warning systems (EWS) are essentially for prediction and warning of upcoming natural hazards. Besides providing of communication and computationally intensive infrastructure, the high resource reliability and hard deadline option are required for EWS scenarios processing in order to get guaranteed information in time-limited conditions. In this paper planning of EWS scenarios execution is investigated and the efficient hybrid algorithm for urgent workflows scheduling is developed based on traditional heuristic and meta-heuristic approaches within state-of-art cloud computing principles. Denis Nasonov, Nikolay Butakov 400 On-board Decision Support System for Ship Flooding Emergency Response [abstract]Abstract: The paper describes a real-time software system to support emergency planning decisions when ship flooding occurs. The events of grounding and collision are considered, where the risk of subsequent flooding of hull compartments is very high, and must be avoided or at least minimized. The system is based on a highly optimized algorithm that estimates, ahead in time, the progressive flooding of the compartments according to the current ship status and existent damages. Flooding times and stability parameters are measured, allowing for the crew to take the adequate measures, such as isolate or counter-flood compartments, before the flooding takes incontrollable proportions. The simulation is visualized in a Virtual Environment in real-time, which provides all the functionalities to evaluate the seriousness and consequences of the situation, as well as to test, monitor and carry out emergency actions. Being a complex physical phenomena that occurs in an equally complex structure such as a ship, the real-time flooding simulation combined with the Virtual Environment requires large computational power to ensure the reliability of the simulation results. Moreover, the distress normally experienced by the crew in such situations, and the urgent (and hopefully appropriate) required counter-measures, leave no room for inaccuracies or misinterpretations, caused by the lack of computational power, to become acceptable. For the events considered, the system is primarily used as a decision support tool to take urgent actions in order to avoid or at least minimize disastrous consequences such as oil spilling, sinking, or even loss of human lives. Jose Varela, Jose Rodrigues, Carlos Guedes Soares

### Bridging the HPC Tallent Gap with Computational Science Research Methods (BRIDGE) Session 1

#### Chair: Vassil Alexandrov

 153 In Need of Partnerships – An Essay about the Collaboration between Computational Sciences and IT Services [abstract]Abstract: Computational Sciences (CS) are challenging in many aspects, not only from the scientific domain they address, but especially also from its needs of the most sophisticated IT infrastructures to perform their research. Often, the latest and most powerful supercomputers, high-performance networks and high-capacity data storages are utilized for CS, while being offered, developed and operated by experts outside CS. This standard service approach has certainly been useful for many domains, but more and more often it represents a limitation to the needs of CS and the restrictions of the IT services. The partnership initiative πCS established at the Leibniz Supercomputing Centre (LRZ) moves the collaboration between Computational Scientists and IT service providers to a new level, moving from a service-centered approach to an integrated partnership. The interface between them is a gateway to an improved collaboration between equal partners, such that future IT services address the requirements of CS in a better, optimized, and more efficient way. In addition, it sheds some light on future professional development. Anton Frank, Ferdinand Jamitzky, Helmut Satzger, Dieter Kranzlmüller 281 Development of Multiplatform Adaptive Rendering Tools to Visualize Scientific Experiments [abstract]Abstract: In this paper, we propose methods and tools for multiplatform adaptive visualization system development adequate to the specific visualization goals of the experiments in the different fields of science. Approach proposed was implemented and we present a client-server rendering system SciVi (Scientific Visualizer) which provides multiplatform portability and automated integration with different solvers based on ontology engineering methods. SciVi is developed in Perm State University to help scientists and researchers acquire the multidisciplinary skills and to solve real scientific problems. Konstantin Ryabinin, Svetlana Chuprina 296 Education 2.0: Student Generated Learning Materials through Collaborative Work [abstract]Abstract: In order to comply with the Integrated Learning Processes model a course on operating systems was redesigned in such a way that students would generate most of their learning materials as well a significant part of their evaluation exams. This new approach resulted in a statistical significant improvement of student’s grade as measured by a standardized exam compared with a previous student intake. Raul Ramirez-Velarde, Raul Perez-Cazares, Nia Alexandrov, Jose Jesus Garcia-Rueda 413 Challenges of Big Data and the Skills Gap [abstract]Abstract: At present, Big Data becomes reality that no one can ignore. Big Data is our environment whenever we need to make a decision. Big Data is a buzz word that makes everyone understands how important it is. Big Data shows a big opportunity for academia, industry and government. Big Data then is a big challenge for all parties. This talk will discuss some fundamental issues of Big Data problems, such as data heterogeneity vs. decision heterogeneity, data stream research and data-driven decision management. Furthermore, this talk will provide a number of real-life Bid Data Applications and will outline the challenges in bridging the skills gap in while focusing on Big Data. Yong Shi and Yingjie Tian

### Bridging the HPC Tallent Gap with Computational Science Research Methods (BRIDGE) Session 2

#### Chair: Vassil Alexandrov

 412 The HPC Talent Gap: an Australian Perspective [abstract]Abstract: The recent Super Science initiative by the Australian government has provided funding for two petascale supercomputers to support research nationally, along with cloud, storage and network infrastructure. While some research areas are well-established in the use of HPC, much of the potential user base is still working with desktop computing. To be able to make use of the new infrastructure, these users will need training, support and associated funding. It is important to not only increase uptake in computational science, but also to nurture the workforce based on identified roles and ongoing support for careers and career pathways. This paper will present a survey of a range of efforts made in Australia to increase uptake and skills in HPC, and reflect on successes and the challenges ahead. Valerie Maxville 418 Measuring Business Value of Learning Technology Implementation in Higher Education Setting [abstract]Abstract: This paper introduces the concept of Business Value of Learning Technology and presents an approach how to measure the Business Value of Learning Technology in Higher Education setting based on a case study in Computational Science and cognate areas. Computational Science subject area is used as a pilot for the studies described in this paper since it is a multidisciplinary area, attracting students from diverse backgrounds and Computational Science is both the natural environment to promote collaborative teaching methods and collaborative provision of courses and as such requires more streamlined management processes. The paper, based on the above case study, presents the motivators and hygiene factors for Learning Technology Implementation in Higher Education setting. Finally, the Intersecting Influences Model presents the influences of pedagogy, technology and management over the motivation and hygiene factors, together with the corresponding generalization for PG level HE setting. Nia Alexandrov

### Modeling and Simulation of Large-scale Complex Urban Systems (MASCUS) Session 1

#### Chair: Heiko Aydt

 111 Analysing the Effectiveness of Wearable Wireless Sensors in Controlling Crowd Disasters [abstract]Abstract: The Love Parade disaster in Duisberg, Germany lead to several deaths and injuries. Disasters like this occur due to the existence of high densities in a limited area. We propose a wearable electronic device that helps reduce such disasters by directing people and thus controlling the density of the crowd. We investigate the design and effectiveness of such a device through an agent based simulation using social force. We also investigate the effect of device failure and participants not paying attention in order to determine the critical number of devices and attentive participants required for the device to be effective. Teo Yu Hui Angela, Vaisagh Viswanathan, Michael Lees, Wentong Cai 204 Individual-Oriented Model Crowd Evacuations Distributed Simulation [abstract]Abstract: Emergency plan design is an important problem in building design to evacuate people as fast as possible. Evacuation simulation exercises as fire drills are not a realistic situation to understand the behaviour of people. In the case of crowd evacuations the complexity and uncertainty of the systems increases. Computer simulation allows us to run crowd dynamics models and extract information from emergency situations. Several models solve the emergency evacuation problem. Individual oriented modelling allows to describe rules for individual and simulate interactions between them. Because the variation on the emergency situations results have to be statistically reliable. This reliability increases the computing demand. Distributed and parallel paradigms solve the performance problem. In the present work we developed a model to simulate crowd evacuations. We implemented two versions of the model. One using Netlogo and another using C with MPI. We chose a real environment to test the simulator: building 2 of Fira de Barcelona building, able to hold thousands of persons. The distributed simulator was tested with 62,820 runs in a distributed environment with 15,000 individuals. In this work we show how the simulator has a linear speedup and scales efficiently. Albert Gutierrez-Milla, Francisco Borges, Remo Suppi, Emilio Luque 133 Simulating Congestion Dynamics of Train Rapid Transit using Smart Card Data [abstract]Abstract: Investigating congestion in train rapid transit systems (RTS) in today's urban cities is a challenge compounded by limited data availability and difficulties in model validation. Here, we integrate information from travel smart card data, a mathematical model of route choice, and a full-scale agent-based model of the Singapore RTS to provide a more comprehensive understanding of the congestion dynamics than can be obtained through analytical modelling alone. Our model is empirically validated, and allows for close inspection of the dynamics including station crowdedness, average travel duration, and frequency of missed trains---all highly pertinent factors in service quality. Using current data, the crowdedness in all 121 stations appears to be distributed log-normally. In our preliminary scenarios, we investigate the effect of population growth on service quality. We find that the current population (2 million) lies below a critical point; and increasing it beyond a factor of approximately 10% leads to an exponential deterioration in service quality. We also predict that incentivizing commuters to avoid the most congested hours can bring modest improvements to the service quality provided the population remains under the critical point. Finally, our model can be used to generate simulated data for statistical analysis when such data are not empirically available, as is often the case. Nasri Othman, Erika Fille Legara, Vicknesh Selvam, Christopher Monterola 177 A method to ascertain rapid transit systems' throughput distribution using network analysis [abstract]Abstract: We present a method of predicting the distribution of passenger throughput across stations and lines of a city rapid transit system by calculating the normalized betweenness centrality of the nodes (stations) and edges of the rail network. The method is evaluated by correlating the distribution of betweenness centrality against throughput distribution which is calculated using actual passenger ridership data. Our ticketing data is from the rail transport system of Singapore that comprises more than 14 million journeys over a span of one week. We demonstrate that removal of outliers representing about 10\% of the stations produces a statistically significant correlation above 0.7. Interestingly, these outliers coincide with stations that opened six months before the time the ridership data was collected, hinting that travel routines along these stations have not yet settled to its equilibrium. The correlation is improved significantly when the data points are split according to their separate lines, illustrating differences in the intrinsic characteristics of each line. The simple procedure established here shows that static network analysis of the structure of a transport network can allow transport planners to predict with sufficient accuracy the passenger ridership, without requiring dynamic and complex simulation methods. Muhamad Azfar Ramli, Christopher Monterola, Gary Kee Khoon Lee, Terence Gih Guang Hung 236 Fast and Accurate Optimization of a GPU-accelerated CA Urban Model through Cooperative Coevolutionary Particle Swarms [abstract]Abstract: The calibration of Cellular Automata (CA) models for simulating land-use dynamics requires the use of formal, well-structured and automated optimization procedures. A typical approach used in the literature to tackle the calibration problem, consists of using general optimization metaheuristics. However, the latter often require thousands of runs of the model to provide reliable results, thus involving remarkable computational costs. Moreover, all optimization metaheuristics are plagued by the so called curse of dimensionality, that is a rapid deterioration of eciency as the dimensionality of the search space increases. Therefore, in case of models depending on a large number of parameters, the calibration problem requires the use of advanced computational techniques. In this paper, we investigate the eectiveness of combining two computational strategies. On the one hand, we greatly speed up CA simulations by using general-purpose computing on graphics processing units. On the other hand, we use a specifically designed cooperative coevolutionary Particle Swarm Optimization algorithm, which is known for its ability to operate eectively in search spaces with a high number of dimensions. Ivan Blecic, Arnaldo Cecchini, Giuseppe A. Trunfio

### Workshop on Data Mining in Earth System Science (DMESS) Session 1

#### Chair: Jay Larson

 375 Stochastic Parameterization to Represent Variability and Extremes in Climate Modeling [abstract]Abstract: Unresolved sub-grid processes, those which are too small or dissipate too quickly to be captured within a model's spatial resolution, are not adequately parameterized by conventional numerical climate models. Sub-grid heterogeneity is lost in parameterizations that quantify only the bulk effect' of sub-grid dynamics on the resolved scales. A unique solution, one unreliant on increased grid resolution, is the employment of stochastic parameterization of the sub-grid to reintroduce variability. We administer this approach in a coupled land-atmosphere model, one that combines the single-column Community Atmosphere Model and the single-point Community Land Model, by incorporating a stochastic representation of sub-grid latent heat flux to force the distribution of precipitation. Sub-grid differences in surface latent heat flux arise from the mosaic of Plant Functional Types (PFT's) that describe terrestrial land cover. With the introduction of a stochastic parameterization framework to affect the distribution of sub-grid PFT's, we alter the distribution of convective precipitation over regions with high PFT variability. The stochastically forced precipitation probability density functions show lengthened tails demonstrating the retrieval of rare events. Through model data analysis we show that the stochastic model increases both the frequency and intensity of rare events in comparison to conventional deterministic parameterization. Roisin Langan, Richard Archibald, Matthew Plumlee, Salil Mahajan, Daniel Ricciuto, Cheng-En Yang, Rui Mei, Jiafu Mao, Xiaoying Shi, Joshua Fu 426 Understanding Global Climate Variability, Change and Stability through Densities, Distributions, and Informatics [abstract]Abstract: Climate modelling as it is generally practised is the act of generating large volumes of simu- lated weather through integration of primitive-equation/general circulation model-based Earth system models (ESMs) and subsequent statistical analysis of these large volumes of model-generated history files. This ap- proach, though highly successful, entails explosively growing data volumes, and may not be practicable on exascale computers. This situation begs the question: Can we model climate’s governing dynamics directly? If we pursue this tactic, there are two clear avenues to pursue: i) analysis of the combined primitive equations and subgridscale parameterisations to formulate an “envelope theory” applicable to the system’s larger spa- tiotemporal scales; and ii) a search for governing dynamics through analysis of the existing corpus of climate observation assimilated and simulated data. Our work focuses on strategy ii). Climate data analysis concentrates primarily on statistical moments, quantiles, and extremes, but rarely on the most complete statistical descriptor—the probability density function (PDF). Long-term climate variabil- ity motivates a moving-window-sampled PDF, which we call a time-dependent PDF (TDPDF). The TDPDF resides within a PDF/information-theoretic framework that provides answers to several key questions of cli- mate variability, stability, and change, including: How does the climate evolve in time? How representative is any given sampling interval of the whole record? How rapidly is the climate changing? In this study, we pursue probability density estimation globally sampled climate data using two techniques that are readily applicable to spatially weighted data and yield closed-form PDFs: the Edgworth expansion and kernel smoothing. We explore our concerns regarding serial correlation in the data and effective sample size due to spatiotemporal correlations. We introduce these concepts for a simple dataset: the Central England Temperature Record. We then apply these techniques to larger, spatially-weghted climate data sets, including the USA National Center for Environmental Predictions NCEP-1 Reanalysis, the Australian Water Availability Project (AWAP) dataset, and the Australian Water and Carbon Observatory dataset. Jay Larson and Padarn Wilson 52 Integration of artificial neural networks into operational ocean wave prediction models for fast and accurate emulation of exact nonlinear interactions [abstract]Abstract: In this paper, an implementation study was undertaken to employ Artificial Neural Networks (ANN) in third-generation ocean wave models for direct mapping of wind-wave spectra into exact nonlinear interactions. While the investigation expands on previously reported feasibility studies of Neural Network Interaction Approximations (NNIA), it focuses on a new robust neural network that is implemented in Wavewatch III (WW3) model. Several idealistic and real test scenarios were carried out. The obtained results confirm the feasibility of NNIA in terms of speeding-up model calculations and is fully capable of providing operationally acceptable model integrations. The ANN is able to emulate the exact nonlinear interaction for single- and multi-modal wave spectra with a much higher accuracy then Discrete Interaction Approximation (DIA). NNIA performs at least twice as fast as DIA and at least two hundred times faster than exact method (Web-Resio-Tracy, WRT) for a well trained dataset. The accuracy of NNIA is network configuration dependent. For most optimal network configurations, the NNIA results and scatter statistics show good agreement with exact results by means of growth curves and integral parameters. Practical possibilities for further improvements in achieving fast and highly accurate emulations using ANN for emulating time consuming exact nonlinear interactions are also suggested and discussed. Ruslan Puscasu

### Large Scale Computationl Physics (LSCP) Session 1

#### Chair: Fukuko YUASA

 404 Development of lattice QCD simulation code set Bridge++'' on accelerators [abstract]Abstract: We are developing a new code set Bridge++'' for lattice QCD (Quantum Chromodynamics) simulations. It aims at an extensible, readable, and portable workbench, while achieving high performance. Bridge++ covers popular lattice actions and numerical algorithms. The code set is constructed in C++ with an object oriented programming. In this paper, we describe our code design focusing on the use of accelerators such as GPGPUs. For portability our implementation employs OpenCL to control the devices while encapsulates the details of manipulation by providing generalized interfaces. The code is successfully applied to several recent accelerators. Shinji Motoki, Shinya Aoki, Tatsumi Aoyama, Kazuyuki Kanaya, Hideo Matsufuru, Yusuke Namekawa, Hidekatsu Nemura, Yusuke Taniguchi, Satoru Ueda, Naoya Ukita 406 GPGPU Application to the Computation of Hamiltonian Matrix Elements between Non-orthogonal Slater Determinants in the Monte Carlo Shell Model [abstract]Abstract: We apply the computation with a GPU accelerator to calculate Hamiltonian matrix elements between non-orthogonal Slater determinants utilized in the Monte Carlo shell model. The bottleneck of this calculation is the two-body part in the computation of Hamiltonian matrix elements. We explain an efficient computational method to overcome this bottleneck. For General-Purpose computing on the GPU (GPGPU) of this method, we propose a computational procedure to avoid the unnecessary costs of data transfer into a GPU device and aim for efficient computation with the cuBLAS interface and the OpenACC directive. As a result, we achieve about 40 times better performance in FLOPS as compared with a single-threaded process of CPU for the two-body part in the computation of Hamiltonian matrix elements. Tomoaki Togashi, Noritaka Shimizu, Yutaka Utsuno, Takashi Abe, Takaharu Otsuka

### Dynamic Data Driven Application Systems (DDDAS) Session 2

#### Chair: Frederica Darema

 43 Towards a Dynamic Data Driven Wildfire Behavior Prediction System at European Level [abstract]Abstract: Southern European countries are severely affected by forest fires every year, which lead to very large environmental damages and great economic investments to recover affected areas. All affected countries invest lots of resources to minimize fire damages. Emerging technologies are used to help wildfire analysts determine fire behavior and spread aiming at a more efficient use of resources in fire fighting. In the case of trans-boundary fires, the European Forest Fire Information System (EFFIS) works as a complementary system to national and regional systems in the countries, providing information required for international collaboration on forest fire prevention and fighting. In this work, we describe a way of exploiting all the available information in the system to feed a dynamic data driven wildfire behavior prediction model that can deliver results to support operational decisions. The model is able to calibrate the unknown parameters based on the real observed data, such as wind condition and fuel moistures, using a steering loop. Since this process is computational intensive, we exploit multi-core platforms using a hybrid MPI-OpenMP programming paradigm. Tomàs Artés, Andrés Cencerrado, Ana Cortes, Tomas Margalef, Darío Rodríguez, Thomas Petroliagkis, Jesus San Miguel 91 Fast Construction of Surrogates for UQ Central to DDDAS -- Application to Volcanic Ash Transport [abstract]Abstract: In this paper we present new ideas to greatly enhance the quality of uncertainty quantification in the DDDAS framework. We build on ongoing work in large scale transport of geophysical mass of volcanic origin -- a danger to both land based installations and airborne vehicles. A. K. Patra, E. R. Stefanescu, R. M. Madankan, M. I Bursik, E. B. Pitman, P. Singla, T. Singh, P. Webley 306 A Dynamic Data-driven Decision Support for Aquaculture Farm Closure [abstract]Abstract: We present a dynamic data-driven decision support for aquaculture farm closure. In decision support, we use machine learning techniques in predicting closures of a shellfish farm. As environmental time series are used in closure, we propose two approaches using time series and machine learning for closure prediction. In one approach, we consider time series prediction and then using expert rules to predict closure. In other approach, we use time series classification for closure prediction. Both approaches exploit a dynamic data-driven technique where prediction models are updated with the update of new data to predict closure decisions. Experimental results at a case study shellfish farm validate the applicability of the proposed method in aquaculture decision support. Md. Sumon Shahriar, John McCulloch 76 An Open Framework for Dynamic Big-Data-Driven Application Systems (DBDDAS) Development [abstract]Abstract: In this paper, we outline key features that dynamic data-driven application systems (DDDAS) have. The term Big Data (BD) has come into being in recent years that is highly applicable to most DDDAS since most applications use networks of sensors that generate an overwhelming amount of data in the lifespan of the application runs. We describe what a dynamic big-data-driven application system (DBDDAS) toolkit must have in order to provide all of the essential building blocks that are necessary to easily create new DDDAS without re-inventing the building blocks. Craig C. Douglas

### Dynamic Data Driven Application Systems (DDDAS) Session 4

#### Chair: Ana Cortes

 74 Dynamic Data Driven Crowd Sensing Task Assignment [abstract]Abstract: To realize the full potential of mobile crowd sensing, techniques are needed to deal with uncertainty in participant locations and trajectories. We propose a novel model for spatial task assignment in mobile crowd sensing that uses a dynamic and adaptive data driven scheme to assign moving participants with uncertain trajectories to sensing tasks, in a near-optimal manner. Our scheme is based on building a mobility model from publicly available trajectory history and estimating posterior location values using noisy/uncertain measurements upon which initial tasking assignments are made. These assignments may be refined locally (using exact information) and used by participants to steer their future data collection, which completes the feedback loop. We present the design of our proposed approach with rationale to suggest its value in effective mobile crowd sensing task assignment in the presence of uncertain trajectories. Layla Pournajaf, Li Xiong, Vaidy Sunderam 79 Context-aware Dynamic Data-driven Pattern Classification* [abstract]Abstract: This work aims to mathematically formalize the notion of context, with the purpose of allowing contextual decision-making in order to improve performance in dynamic data driven classification systems. We present definitions for both intrinsic context, i.e. factors which directly affect sensor measurements for a given event, as well as extrinsic context, i.e. factors which do not affect the sensor measurements directly, but do affect the interpretation of collected data. Supervised and unsupervised modeling techniques to derive context and context labels from sensor data are formulated. Here, supervised modeling incorporates the a priori known factors affecting the sensing modalities, while unsupervised modeling autonomously discovers the structure of those factors in sensor data. Context-aware event classification algorithms are developed by adapting the classification boundaries, dependent on the current operational context. Improvements in context-aware classification have been quantified and validated in an unattended sensor-fence application for US Border Monitoring. Field data, collected with seismic sensors on different ground types, are analyzed in order to classify two types of walking across the border, namely, normal and stealthy. The classification is shown to be strongly dependent on the context (specifically, soil type: gravel or moist soil). Shashi Phoha, Nurali Virani, Pritthi Chattopadhyay, Soumalya Sarkar, Brian Smith, Asok Ray

### Tools for Program Development and Analysis in Computational Science (TOOLS) Session 1

#### Chair: Jie Tao

 335 High Performance Message-Passing InfiniBand Communication Device for Java HPC [abstract]Abstract: MPJ Express is a Java messaging system that implements an MPI-like interface. It is used for writing parallel Java applications on High Performance Computing (HPC) hardware including commodity clusters. The software is capable of executing in multicore and cluster mode. In the cluster mode, it currently supports Ethernet and Myrinet based interconnects and provide specialized communication devices for these networks. One recent trend in distributed memory parallel hardware is the emergence of InfiniBand interconnect, which is a high-performance proprietary network and provides low latency and high bandwidth for parallel MPI applications. Currently there is no direct support available in Java (and hence MPJ Express) to exploit the performance benefits of InfiniBand networks. The only option to run distributed Java programs over InfiniBand networks is to rely on TCP/IP emulation layers like IP over InfiniBand (IPoIB) and Sockets Direct Protocol (SDP), which provide poor communication performance. To tackle this issue in the context of MPJ Express, this paper presents a low-level communication device called ibdev that can be used to execute parallel Java applications on InfiniBand clusters. MPJ Express is based on a layered architecture and hence users can opt to use ibdev at runtime on an InfiniBand equipped commodity cluster. ibdev improves Java application performance with access to InfiniBand hardware using native verbs API. Our performance evaluation reveals that MPJ Express achieves much better latency and bandwidth using this new device, compared to IPoIB and SDP. Improvement in communication performance is also evident in NAS parallel benchmark results where ibdev helps MPJ Express achieve better scalability and speedups as compared to IPoIB and SDP. The results show that it is possible to reduce the performance gap between Java and native languages with efficient support for low level communication libraries. Omar Khan, Mohsan Jameel, Aamir Shafi 300 A High Level Programming Environment for Accelerator-based Systems [abstract]Abstract: Some of the critical hurdles for the widespread adoption of accelerators in high performance computing are portability and programming difficulty. To be an effective HPC platform, these systems need a high level software development environment to facilitate the porting and development of applications, so they can be portable and run efficiently on either accelerators or CPUs. In this paper we present a high level parallel programming environment for accelerator-based systems, which consists of tightly coupled compilers, tools, and libraries that can interoperate and hide the complexity of the system. Ease of use is possible with compilers making it feasible for users to write applications in Fortran, C, or C++ with OpenACC directives, tools to help users port, debug, and optimize for both accelerators and conventional multi-core CPUs, and with auto-tuned scientific libraries. Luiz Derose, Heidi Poxon, James Beyer, Alistair Hart 277 Supporting relative debugging for large-scale UPC programs [abstract]Abstract: Relative debugging is a useful technique for locating errors that emerge from porting existing code to new programming language or to new computing platform. Recent attention on the UPC programming language has resulted in a number of conventional parallel programs, for example MPI programs, being ported to UPC. This paper gives an overview on the data distribution concepts used in UPC and establishes the challenges in supporting relative debugging technique for UPC programs that run on large supercomputers. The proposed solution is implemented on an existing parallel relative debugger ccdb, and the performance is evaluated on a Cray XE6 system with 16,348 cores. Minh Ngoc Dinh, David Abramson, Jin Chao, Bob Moench, Andrew Gontarek, Luiz Derose

### Tools for Program Development and Analysis in Computational Science (TOOLS) Session 2

#### Chair: Jie Tao

 97 Near Real-time Data Analysis of Core-Collapse Supernova Simulations With Bellerophon [abstract]Abstract: We present an overview of a software system, Bellerophon, built to support a production-level HPC application called CHIMERA, which simulates core-collapse supernova events at the petascale. Developed over the last four years, Bellerophon enables CHIMERA’s geographically dispersed team of collaborators to perform data analysis in near real-time. Its n-tier architecture provides an encapsulated, end-to-end software solution that enables the CHIMERA team to quickly and easily access highly customizable animated and static views of results from anywhere in the world via a web-deliverable, cross-platform desktop application. In addition, Bellerophon addresses software engineering tasks for the CHIMERA team by providing an automated mechanism for performing regression testing on a variety of supercomputing platforms. Elements of the team’s workflow management needs are met with software tools that dynamically generate code repository statistics, access important online resources, and monitor the current status of several supercomputing resources. E. J. Lingerfelt, O. E. B. Messer, S. S. Desai, C. A. Holt, E. J. Lentz 148 Toward Better Understanding of the Community Land Model within the Earth System Modeling Framework [abstract]Abstract: One key factor in the improved understanding of earth system science is the development and improvement of high fidelity earth system models. Along with the deeper understanding of system processes, the complexity of software systems of those modelling systems becomes a barrier for further rapid model improvements and validation. In this paper, we present our experience on better understanding the Community Land Model (CLM) within an earth system modelling framework. First, we give an overview of the software system of the global offline CLM system. Second, we present our approach to better understand the CLM software structure and data structure using advanced software tools. After that, we focus on the practical issues related to CLM computational performance and individual ecosystem function. Since better software engineering practices are much needed for general scientific software systems, we hope those considerations can be beneficial to many other modeling research programs involving multiscale system dynamics. Dali Wang, Joseph Schuchart, Tomislav Janjusic, Frank Winkler, Yang Xu, Christos Kartsaklis 155 Detecting and visualising process relationships in Erlang [abstract]Abstract: Static software analyser tools can help in program comprehension by detecting relations among program parts. Detecting relations among the concurrent program parts, e.g. relations between processes, is not straightforward. In case of dynamic languages only a (good) approximation of the real dependencies can be calculated. In this paper we present algorithms to build a process relation graph for Erlang programs. The graph contains direct relation through message passing and hidden relations represented by the ETS tables. Melinda Tóth, István Bozó

### Computational Optimisation in the Real World (CORW) Session 1

#### Chair: Timoleon Kipouros

 276 Extending the Front: Designing RFID Antennas using Multiobjective Differential Evolution with Biased Population Selection [abstract]Abstract: RFID antennas are ubiquitous, so exploring the space of high efficiency and low resonant frequency antennas is an important multiobjective problem. Previous work has shown that the continuous solver differential evolution (DE) can be successfully applied to this discrete problem, but has difficulty exploring the region of solutions with lowest resonant frequency. This paper introduces a modified DE algorithm that uses biased selection from an archive of solutions to direct the search toward this region. Results indicate that the proposed approach produces superior attainment surfaces to the earlier work. The biased selection procedure is applicable to other population-based approaches for this problem. James Montgomery, Marcus Randall, Andrew Lewis 396 Local Search Enabled Extremal Optimisation for Continuous Inseparable Multi-objective Benchmark and Real-World Problems [abstract]Abstract: Local search is an integral part of many meta-heuristic strategies that solve single objective optimisation problems. Essentially, the meta-heuristic is responsible for generating a good starting point from which a greedy local search will find the local optimum. Indeed, the best known solutions to many hard problems (such as the travelling salesman problem) have been generated in this hybrid way. However, for multiple objective problems, explicit local search strategies are relatively rarely mentioned or applied. In this paper, a generic local search strategy is developed, particularly for problems where it is difficult or impossible to determine the contribution of individual solution components (often referred to as inseparable problems). The meta-heuristic adopted to test this is extremal optimisation, though the local search technique may be used by any meta-heuristic. To supplement the local search strategy a diversication strategy that draws from the external archive is incorporated into the local search strategy. Using benchmark problems, and a real-world airfoil design problem, it is shown that this combination leads to improved solutions. Marcus Randall, Andrew Lewis, Jan Hettenhausen, Timoleon Kipouros 411 A Web-Based System for Visualisation-Driven Interactive Multi-Objective Optimisation [abstract]Abstract: Interactive Multi-Objective Optimisation is an increasingly growing field of evolutionary and swarm intelligence-based algorithms. By involving a human decision a set of relevant non-dominated points can often be acquired at significantly lower computational costs than with \textit{a posteriori} algorithms. An often neglected issue in interactive optimisation is the issue of user interface design and the application of interactive optimisation as a design tool in engineering applications. This paper will discuss recent advances made in and moduli for an interactive multi-objective particle swarm optimisation algorithm. The focus of current implementation is on an aeronautics engineering applications, however, use of it for a wide range of other optimisation problems is conceivable. Jan Hettenhausen, Andrew Lewis, Timoleon Kipouros

### Computational Optimisation in the Real World (CORW) Session 2

#### Chair: Andrew Lewis

 92 A Hybrid Harmony Search Algorithm for Solving Dynamic Optimisation Problems [abstract]Abstract: Many optimisation problems are dynamic in the sense that changes occur during the optimisation process, and therefore are more challenging than the stationary problems. The occurrences of such problems have attracted researchers into studying areas of artificial intelligence and operational research. To solve dynamic optimisation problems, the proposed approaches should not only attempt to seek the global optima but be able to also keep track of changes in the track record of landscape solutions. Population-based approaches have been intensively investigated to address these problems, as solutions are scattered over the entire search space and therefore helps in recognizing any changes that occur in the search space but however, optimisation algorithms that have been used to solve stationary problems cannot be directly applied to handle dynamic problems without any modifications such as in maintaining population diversity. In this research work, one of the most recent new population-based meta-heuristic optimisation technique called a harmony search algorithm for dynamic optimization problems is investigated. This technique mimics the musical process when a musician attempts to find a state of harmony. In order to cope with a dynamic behaviour, the proposed harmony search algorithm was hybridised with a (i) random immigrant, (ii) memory mechanism and (iii) memory based immigrant scheme. This hybridisation processes help to keep track of the changes and to maintain the population diversity. The performance of the proposed harmony search is verified by using the well-known dynamic test problem called the Moving Peak Benchmark (MPB) with a variety of peaks. The empirical results demonstrate that the proposed algorithm is able to obtain competitive results, but not the best for most of the cases, when compared to the best known results in the scientific literature published so far. Ayad Turky, Salwani Abdullah, Nasser Sabar 313 Constraint Programming and Ant Colony System for the Component Deployment Problem [abstract]Abstract: Contemporary motor vehicles have increasing numbers of automated functions to augment the safety and comfort of a car. The automotive industry has to incorporate increasing numbers of processing units in the structure of cars to run the software that provides these functionalities. The software components often need access to sensors or mechanical devices which they are designed to operate. The result is a network of hardware units which can accommodate a limited number of software programs, each of which has to be assigned to a hardware unit. A prime goal of this deployment problem is to nd softwareto-hardware assignments that maximise the reliability of the system. In doing so, the assignments have to observe a number of constraints to be viable. This includes limited memory of a hardware unit, collocation of software components on the same hardware units, and communication between software components. Since the problem consists of many constraints with a signicantly large search space, we investigate an ACO and constraint programming (CP) hybrid for this problem. We nd that despite the large number of constraints, ACO on its own is the most eective method providing good solutions by also exploring infeasible regions. Dhananjay Thiruvady, I. Moser, Aldeida Aleti, Asef Nazari 416 Electrical Power Grid Network Optimisation by Evolutionary Computing [abstract]Abstract: A major factor in the consideration of an electrical power network of the scale of a national grid is the calculation of power flow and in particular, optimal power flow. This paper considers such a network, in which distributed generation is used, and examines how the network can be optimized, in terms of transmission line capacity, in order to obtain optimal or at least high-performing configurations, using multi-objective optimisation by evolutionary computing methods. John Oliver, Timoleon Kipouros, Mark Savill

### International Workshop on Advances in High-Performance Computational Earth Sciences (IHPCES) Session 1

#### Chair: Kengo Nakajima

 408 Application-specific I/O Optimizations on Petascale Supercomputers [abstract]Abstract: Data-intensive science frontiers and challenges are emerging as computer technology has evolved substantially. Large-scale simulations demand significant I/O workload, and as a result the I/O performance often becomes a bottleneck preventing high performance in scientific applications. In this paper we introduce a variety of I/O optimization techniques developed and implemented when scaling a seismic application to petascale. These techniques include file system striping, data aggregation, reader/writer limiting and less interleaving of data, collective MPI-IO, and data staging. The optimizations result in nearly perfect scalability of the target application on some of the most advanced petascale systems. The techniques introduced in this paper are applicable to other scientific applications facing similar petascale I/O challenges. Efecan Poyraz, Heming Xu, Yifeng Cui 264 A physics-based Monte Carlo earthquake disaster simulation accounting for uncertainty in building structure parameters [abstract]Abstract: Physics-based earthquake disaster simulations are expected to contribute to high-precision earthquake disaster prediction; however, such models are computationally expensive and the results typically contain significant uncertainties. Here we describe Monte Carlo simulations where 10,000 calculations were carried out with stochastically varied building structure parameters to model 3,038 buildings. We obtain the spatial distribution of the damage caused for each set of parameters, and analyze these data statistically to predict the extent of damage to buildings. Shunsuke Homma, Kohei Fujita, Tsuyoshi Ichimura, Muneo Hori, Seckin Citak, Takane Hori 391 A quick earthquake disaster estimation system with fast urban earthquake simulation and interactive visualization [abstract]Abstract: In the immediate aftermath of an earthquake, quick estimation of damage to city structures can facilitate prompt, effective post-disaster measures. Physics-based urban earthquake simulations, using measured ground motions as input, are a possible means of obtaining reasonable estimates. The difficulty of such estimation lies in carrying out the simulation and arriving at a thorough understanding of large-scale time series results in a limited amount of time. We developed an estimation system based on fast urban earthquake disaster simulation, together with an interactive visualization method suitable for GPU workstations. Using this system, an urban area with more than 100,000 structures can be analyzed within an hour and visualized interactively. Kohei Fujita, Tsuyoshi Ichimura, Muneo Hori, M. L. L. Wijerathne, Seizo Tanaka 397 Several hundred finite element analyses of an inversion of earthquake fault slip distribution using a high-fidelity model of the crustal structure [abstract]Abstract: To improve the accuracy of inversion analysis of earthquake fault slip distribution, we performed several hundred analyses using a 10^8-degree-of-freedom finite element (FE) model of the crustal structure. We developed a meshing method and an efficient computational method for these large FE models. We applied the model to the inversion analysis of coseismic fault slip distribution for the 2011 Tohoku-oki Earthquake. The high resolution of our model provided a significant improvement of the fidelity of the simulation results compared to existing computational approaches. Ryoichiro Agata, Tsuyoshi Ichimura, Kazuro Hirahara, Mamoru Hyodo, Takane Hori, Muneo Hori

### International Workshop on Advances in High-Performance Computational Earth Sciences (IHPCES) Session 2

#### Chair: Huilin Xing

 334 An out-of-core GPU approach for Accelerating Geostatistical Interpolation [abstract]Abstract: Geostatistical methods provide a powerful tool to understand the complexity of data arising from Earth sciences. Since the mid 70’s, this numerical approach is widely used to understand the spatial variation of natural phenomena in various domains like Oil and Gas, Mining or Environmental Industries. Considering the huge amount of data available, standard imple- mentations of these numerical methods are not efficient enough to tackle current challenges in geosciences. Moreover, most of the software packages available for geostatisticians are de- signed for a usage on a desktop computer due to the trial and error procedure used during the interpolation. The Geological Data Management (GDM ) software package developed by the French geological survey (BRGM) is widely used to build reliable three-dimensional geological models that require a large amount of memory and computing resources. Considering the most time-consuming phase of kriging methodology, we introduce an efficient out-of-core algorithm that fully benefits from graphics cards acceleration on desktop computer. This way we are able to accelerate kriging on GPU with data 4 times bigger than a classical in-core GPU algorithm, with a limited loss of performances. Victor Allombert, David Michea, Fabrice Dupros, Christian Bellier, Bernard Bourgine, Hideo Aochi, Sylvain Jubertie 401 Mesh generation for 3D geological reservoirs with arbitrary stratigraphic surface constraints [abstract]Abstract: With the advanced image, drilling and field observation technology, geological structure of reservoirs can be described in more details. A novel 3D mesh generation method for meshing reservoir models is proposed and implemented with arbitrary stratigraphical surface constraints, which ensures the detailed structure geometries and material properties of reservoirs are better described and analysed. The stratigraphic interfaces are firstly extracted and meshed, and then a tetrahedron mesh is generated in 3D with the constraints of such meshed surfaces. The proposed approach includes the following five steps: (1) extracting stratum interfaces; (2) creating a background mesh with size field on the interfaces; (3) constructing geodesic isolines from interface boundaries to the interior; (4) employing a geodesic-based approach to create surface triangles on the area between adjacent isolines and then merge them together; (5) generating tetrahedron mesh for 3D reservoirs with constraints of generated surface triangular mesh. The proposed approach has been implemented and applied to the Lawn Hill reservoir as a practical example to demonstrate its effectiveness and usefulness. Huilin Xing, Yan Liu 403 Performance evaluation and case study of a coupling software ppOpen-MATH/MP [abstract]Abstract: We are developing a coupling software ppOpen-MATH/MP. ppOpen-MATH/MP is characterized by its wide applicability. This feature comes from the design that grid point correspondence and interpolation coefficients should be calculated in advance. However, calculation of these values on the unstructured grid model requires a lot of computation time in general. So, we developed new effective algorithm and program for calculating the grid point correspondence as a pre-processor of ppOpen-MATH/MP. In this article, an algorithm and performance evaluation of the program is presented in the first half, and next, an application example of ppOpen-MATH/MP, targeting atmospheric model NICAM and ocean model COCO coupling, is described. Takashi Arakawa, Takahiro Inoue, Masaki Sato 402 Implementation and Evaluation of an AMR Framework for FDM Applications [abstract]Abstract: In order to execute various finite-difference method applications on large-scale parallel computers with a reasonable cost of computer resources, a framework using an adaptive mesh refinement (AMR) technique has been developed. AMR can realize high-resolution simulations while saving computer resources by generating and removing hierarchical grids dynamically. In the AMR framework, a dynamic domain decomposition (DDD) technique, as a dynamic load balancing method, is also implemented to correct the computational load imbalance between each process associated with parallelization. By performing a 3D AMR test simulation, it is confirmed that dynamic load balancing can be achieved and execution time can be reduced by introducing the DDD technique. Masaharu Matsumoto, Futoshi Mori, Satoshi Ohshima, Hideyuki Jitsumoto, Takahiro Katagiri, Kengo Nakajima

### Workshop on Computational Chemistry and Its Applications (CCA) Session 1

#### Room: Bluewater II

 112 Computer-aided design of stereocontrol agents for radical polymerization [abstract]Abstract: Controlling the stereochemistry of a polymer is highly desirable as this can affect its physical properties, such as its crystallinity, melting point, solubility and mechanical strength. Stereoregular polymers are normally prepared using expensive transition metal catalysts, which typically require demanding reaction conditions, and extending stereochemical-control to free radical polymerization has been a long sought goal. For monomers containing carbonyl groups certain Lewis acids have been shown to be capable of manipulating the stereochemistry, presumably via coordination to the polymer and/or monomer side chains so as to constrain their relative orientations during the propagation step. However, specific mechanistic details have yet to be clarified, and the degree of stereocontrol remains poor. To address these problems, we have been using computational chemistry, supported by polymerization experiments, to study the mechanism of stereocontrol in a variety of free-radical polymerization processes, and to predict the effect of solvents and novel control agents on polymer tacticity. Interestingly we have discovered that many Lewis acids do selectively coordinate to the terminal and penultimate radical side chains in a manner that should, in principle, facilitate control. However, this coordination is driven by the resulting stabilization of the propagating radical, which, ironically, deactivates it toward propagation. At the same time a less energetically favourable, non-controlling coordination mode involving the monomer side chain catalyzes propagation reaction and this provides the dominant reaction path. On this basis we suggest that simultaneous coordination to the monomer and propagating radical using bridging ligands or mixed Lewis acids may provide the way forward. Michelle Coote, Benjamin Noble and Leesa Smith 38 Correlation between Franck-Condon Factors and Average Internulcear Separations for Diatomics Using the Fourier Grid Hamiltonian Method [abstract]Abstract: The Fourier Grid Hamiltonian (FGH) Method is used to compute the vibrational eigenvalues and eigenfunctions of bound states of diatomic molecules. For these computations, the Hulburt and Hirschfelder (HH) potential model for diatomics is used. These potential energy functions are used for constructing and diagonalizing the molecular Hamiltonians. The vibrational wave functions for the ground and the excited states are used to calculate the Franck-Condon factors (FCFs), r-Centroids and average internuclear separations which play a significant role in determining the intensity of the bands in electronic transitions. The results of FCFs and r-Centroids for diatomic molecules such as H2, N2, CO, I2 and HF using the FGH method are compared with other methods. The FGH method provides an efficient and accurate alternative to calculate FCFs and other parameters that depend on the vibrational wavefunctions of the ground and exited electronic states. The Franck-Condon profiles indicate a strong correlation between the values of Franck-Condon factors and the mean internuclear separations for the corresponding transitions. Mayank Kumar Dixit, Abhishek Jain, Bhalachandra Laxmanrao Tembe 122 Using hyperheuristics to improve the determination of the kinetic constants of a chemical reaction in heterogeneous phase [abstract]Abstract: The reaction in the human stomach when neutralizing acid with an antacid tablet is simulated and the evolution over time of the concentration of all chemical species present in the reaction medium is obtained. The values of the kinetic parameters of the chemical reaction can be determined by integrating the equation of the reaction rate. This is a classical optimization problem that can be approached with metaheuristic methods. The use of a parallel, parameterized scheme for metaheuristics facilitates the development of metaheuristics and their application. The unified scheme can also be used to implement hyperheuristics on top of parameterized metaheuristics, so selecting appropriate values for the metaheuristic parameters, and consequently the metaheuristic itself. The hyperheuristic approach provides satisfactory values for the metaheuristic parameters and, consequently, satisfactory metaheuristics for the problem of determining the kinetic constants. José Matías Cutillas Lozano, Domingo Gimenez 267 Speeding up Monte Carlo Molecular Simulation by a Non-Conservative Early Rejection Scheme [abstract]Abstract: Molecular simulation describes fluid systems in detailed fashion. In general, they are more accurate and representative than equations of state. However, they require much more computational efforts. Several techniques have been developed in order to speed up Monte Carlo (MC) molecular simulations while preserving their precision. In particular, early rejection schemes are capable of reducing computational cost by reaching the rejection decision for the undesired MC trials at early stages. In this work, the introduced scheme is based on the fact that the energy due to interaction between any couple of Lennard-Jones (LJ) sites cannot be lower than a certain minimum energy that can be easily computed. It is called “non-conservative” as it generates slightly different Markov chains than the ones generated by the conventional algorithms. Nonetheless, the numerical experiments conducted show that these modifications are not significant, and both the proposed and the conventional methods converge to the same ensemble averages. In this study, the non-conservative scheme is first introduced and then compared to the conservative and bond formation early rejection schemes. The method was tested for LJ particles in canonical ensemble at several thermodynamic conditions. Results showed a relation between the thermodynamic conditions and the percentage of the CPU time saved. In principle, more CPU time was saved at conditions with high rejection rates for the MC trials. The non-conservative early rejection scheme was successful in saving more than 45 % of the CPU time needed by the conventional algorithms in canonical ensemble. Finally, this work presents an efficient early rejection method to accelerate MC molecular simulations which is easily extendable to other ensembles and complex molecules. Ahmad Kadoura, Amgad Salama and Shuyu Sun

### Workshop on Computational Chemistry and Its Applications (CCA) Session 2

#### Room: Bluewater II

 21 A Computational Study of 2-Selenobarbituric Acid: Conformational Analysis, Enthalpy of Formation, Acidity and Basicity [abstract]Abstract: A computational study of the compound containing selenium, 2-selenobarbituric acid, has been carried out. Tautomerism has been studied not only in neutral forms but also in the protonated and deprotonated species. The most stable tautomers for neutral and deprotonated species are equivalent to those obtained by different authors for the analogous barbituric and 2-thiobarbituric acids. However, the most stable tautomer for the protonated 2-selenobarbituric acid differs of that proposed for the analogous compounds. The enthalpy of formation in the gas phase, and the gas-phase acidity and basicity of 2-selenobarbituric acid have been calculated at the G3 and G4 levels, together with the corresponding values for barbituric and 2-thiobarbituric acids. The calculated acidity shows that 2-selenobarbituric acid is a very strong Brønsted acid in the gas phase. Rafael Notario 139 Origin of the Extra Stability of Alloxan.A Computation Study [abstract]Abstract: Detailed DFT computations and quantum dynamics simulations have been carried out to establish the origin of the extra stability of alloxan.. The effect of solvent, basis set and DFT methods have been examined. Two non-covalent intermolecular dimers of alloxan, namely the H-bonded and the dipolar dimers have been investigated to establish their relative stability. Quantum chemical topology features and NBO analysis have been performed. Saadullah Aziz, Rifaat Hilal, Basmah Allehyani, Shabaan Elroby 303 The Impact of p-orbital on Optmization of ReH7(PMe3)2 Compound [abstract]Abstract: This study investigates the importance of the p-function used in the computational modeling. The geometric changes of ReH7(PMe3)2 system is used as the model compound. 6-31G, 6-311G and 6-311++G basis sets were used for all elements except Re, which used Christiansen et. al. basis set. Upon removing the p-function on metal, we noticed the geometric changes are minimal as long as triple-zeta basis sets are used for rest of elements. While the relative energy profile of a reaction would still reasonably assemble each other, a direct comparison in energy between the basis set with and without p-function is not recommended Nnenna Elechi, Daniel Tran, Mykala Taylor, Odaro Adu, Huajun Fan 60 Exploring the Conical Intersection Seam in Cytosine: A DFT and CASSCF Study [abstract]Abstract: The geometry, energetics and dipole moment of the most stable conformers of cytosine in the ground state were calculated at different density functional methods, namely, B3LYP, M06-2X, ωB97-D and PEBPEB methods and the 6-311++G(3df,3pd) basis set. The most stable conformer, the keto-amino conformer is only 1 Kcal/mol more stable than the imino-enol form. The ultrafast radiationless decay mechanism has been theoretically investigated using Complete Active Space Multiconfiguration SCF calculation. The conical intersection seam was searched in the full dimensional space for the vibrational degrees of freedom. A new conical intersection has been identified, a semi-planar conical intersection (SPCI) with main deformations inside the cytosine ring and C=O bond. The g-vector and h-vector for the semi-planar conical intersection were calculated and discussed along with their geometrical parameters. A classical trajectory dynamic simulation has been performed to characterize and identify the evolution of geometry and energy changes of the SPCI with time. Rifaat Hilal, Saadullah Aziz, Shabaan Elrouby, Walid Hassan

### Architecture, Languages, Compilation and Hardware support for Emerging ManYcore systems (ALCHEMY) Session 1

#### Chair: Stéphane Louise

 348 τC: C with Process Network Extensions for Embedded Manycores [abstract]Abstract: Current and future embedded manycores targets bring complex and heterogeneous architectures with a large number of processing cores, making both parallel programming to this scale and understanding the architecture itself a daunting task. Process Networks and other dataflow based Models of Computation (MoC) are a good base to present a universal model of the underlying manycore architectures to the programmer. If a language displays a simple to grasp MoC in a consistent way across architectures, the programmer can concentrate the efforts on optimizing the expression of parallelism in the application instead of porting a given code on a given system. Such goal would provide the C-language equivalent for the manycores. In this paper, we present a process network extension to C called τ C and its mapping to both a POSIX target and the P2012/STHORM platform, and show how the language offers an architecture independent solution of this problem. Thierry Goubier, Damien Couroussé, Selma Azaiez 96 Application-Level Performance Optimization: A Computer Vision Case Study on STHORM [abstract]Abstract: Computer vision applications constitute one of the key drivers for embedded many-core architectures. In order to exploit the full potential of such systems, a balance between computation and communication is critical, but many computer vision algorithms present a highly data-dependent behavior that complexify this task. To enable application performance optimization, the development environment must provide the developer with tools for fast and precise application-level performance analysis. We describe the process to port and optimize a face detection application onto the STHORM many-core accelerator using the STHORM OpenCL SDK. We identify the main factors that limit performance and discern the contributions arising from: the application itself, the OpenCL programming model, and the STHORM OpenCL SDK. Finally, we show how these issues can be addressed in the future to enable developers to further improve application performance. Vítor Schwambach, Sébastien Cleyet-Merle, Alain Issard, Stéphane Mancini 387 Generating Code and Memory Buffers to Reorganize Data on Many-core Architectures [abstract]Abstract: The dataflow programming model has shown to be a relevant approach to efficiently run massively parallel applications over many-core architectures. In this model, some particular builtin agents are in charge of data reorganizations between user agents. Such agents can Split, Join and Duplicate data onto their communication ports. They are widely used in signal processing for example. These system agents, and their associated implementations, are of major importance when it comes to performances, because they can stand on the critical path (think about Amdhal's law). Furthermore, a particular data reorganization can be expressed by the developer in several ways, that may lead to inefficient solutions (mostly unneeded data copies and transfers). In this paper, we propose several strategies to manage data reorganization at compile time, with a focus on indexed accesses to shared buffers to avoid data copies. These strategies are complementary: they ensure correctness for each system agent configuration, as well as performance when possible. They have been implemented within the Sigma-C industry-grade compilation toolchain and evaluated over the Kalray MPPA 256-core processor. Loïc Cudennec, Paul Dubrulle, François Galea, Thierry Goubier, Renaud Sirdey 359 Self-Timed Periodic Scheduling For Cyclo-Static DataFlow Model [abstract]Abstract: Real-time and Time constrained applications programmed on many-core systems can suffer from unmet timing constraints even with correct-by-construction schedules. Such unexpected results are usually caused by unaccounted for delays of resource sharing (\emph{e.g.} the communication medium). In this paper we address the three main sources of unpredictable behaviors: First, we propose to use a deterministic Model of Computation (MoC), more specifically, the well-formed CSDF subset of process networks; Second, we propose a run-time management strategy of shared resources to avoid unpredictable timings; Third, we promote the use of a new scheduling policy, the so-said Self-Timed Periodic (STP) scheduling, to improve performance and decrease synchronization costs by taking into account resource sharing or resource constraints. This is a quantitative improvement above state-of-the-art scheduling policies which assumed fixed delays of inter-processor communication and did not take correctly into account subtle effects of synchronization. Dkhil Ep.Jemal Amira, Xuankhanh Do, Stephane Louise, Dubrulle Paul, Christine Rochange