Energy study of Monte Carlo and Quasi-Monte Carlo algorithms for solving integral equations

In the past few years the development of exascale computing technology necessitated to obtain an estimate for the energy consumption when large-scale problems are solved with different high-performance computing (HPC) systems. In this paper we study the energy efficiency of a class of Monte Carlo (MC) and Quasi-Monte Carlo (QMC) algorithms for a given integral equation using hybrid HPC systems. The algorithms are applied to solve quantum kinetic integral equations describing ultra-fast transport in quantum wire. We compare the energy performance of the algorithms using a GPU-based computer platform and CPU-based computer platform both with and without hyper-threading (HT) technology. We use SPRNG library and CURAND generator to produce parallel pseudo-random (PPR) sequences for the MC algorithms on CPU-based and GPU-based platforms, respectively. For our QMC algorithms Sobol and Halton sequences are used to produce parallel quasi-random (PQR) sequences. We compare the obtained results of the tested algorithms with respect to the given energy metric. The results of our study demonstrate the importance of taking into account not only scalability of the HPC intensive algorithms but also their energy efficiency They also show the need for further optimisation of the QMC algorithms when GPU-based computing platforms are used. © The Authors. Published by Elsevier B.V

On the performance, scalability and sensitivity analysis of a large air pollution model

Computationally efficient sensitivity analysis of a large-scale air pollution model is an important issue we focus on in this paper. Sensitivity studies play an important role for reliability analysis of the results of complex nonlinear models as those used in the air pollution modelling. There is a number of uncertainties in the input data sets, as well as in some internal coefficients, which determine the speed of the main chemical reactions in the chemical part of the model. These uncertainties are subject to our quantitative sensitivity study. Monte Carlo and quasi-Monte Carlo algorithms are used in this study. A large number of numerical experiments with some special modifications of the model must be carried out in order to collect the necessary input data for the particular sensitivity study. For this purpose we created an efficient high performance implementation SA-DEM, based on the MPI version of the package UNI-DEM. A large number of numerical experiments were carried out with SA-DEM on the IBM MareNostrum III at BSC - Barcelona, helped us to identify a severe performance problem with an earlier version of the code and to resolve it successfuly. The improved implementation appears to be quite efficient for that challenging computational problem, as our experiments show. Some numerical results with performance and scalability analysis of these results are presented in the paper. © The Authors. Published by Elsevier B.V

Comparing electoral campaigns by analysing online data

Our work addresses the influence of ICT technologies for campaigning purposes on the evolving dynamics of information flows from the eminently social beings that candidates are. Our approach combines an analysis of contents to technological and methodological concerns. In particular, this paper presents results concerning three of the data collections life cycle phases: collection, cleaning, and storage. The result is a data collection ready to be analysed for different purposes. The paper also describes our experimental validation for comparing political campaigns behaviour in France and the United Kingdom during the European elections in 2014. © The Authors. Published by Elsevier B.V

A stochastic approach to solving bilevel natural gas cash-out problems

We study a special bilevel programming problem that arises in transactions between a Natural Gas Shipping Company and a Pipeline Operator. Because of the business relationships between these two actors, the timing, and objectives of their decision-making process are different. In order to model that, bilevel programming was traditionally used. Apart from the theoretical studies of the problem to facilitate its solution a linear reformulation is required, as well as heuristic approaches, and branch-and-bound techniques may be applied. We present a linear programming reformulation of the latest version of the model, which is easier and faster to solve numerically. This reformulation makes it easier to theoretically analyze the problem, allowing us to draw some conclusions about the nature of the solution. Since elements of uncertainty are definitely present in the bilevel natural gas cash-out problem, its stochastic formulation is developed in the form of a bilevel multi-stage stochastic programming model with recourse. After reducing the original formulation to a bilevel linear problem, a stochastic scenario tree is defined by its node events, and time series forecasting is used to produce stochastic values for data of natural gas price and demand. Numerical experiments were run to compare the stochastic solution with the perfect information solution and the expected value solutions. © The Authors. Published by Elsevier B.V

Iterative Projection approach for solving the Territorial Business Sales optimization problem

A well designed territory enhances customer coverage, increases sales, fosters fair performance and rewards systems and lower travel cost. This paper considers a real life case study to design a sales territory for a business sales plan. The business plan consists in assigning the optimal quantity of sellers to a territory including the scheduling and routing plans for each seller. The problem is formulated as a combination of assignment, scheduling and routing optimization problems. The solution approach considers a meta-heuristic using stochastic iterative projection method for large systems. Several real life instances of different sizes were tested with stochastic data to represent raise/fall in the customers demand as well as the appearance/loss of customers. © 2017 The Authors. Published by Elsevier B.V

Handling single node failures using agents in computer clusters

The work reported in this paper is motivated towards handling single node failures for parallel summation algorithms in computer clusters. An agent based approach is proposed in which a task to be executed is decomposed to sub-tasks and mapped onto agents that traverse computing nodes. The agents intercommunicate across computing nodes to share information during the event of a predicted node failure. Two single node failure scenarios are considered. The Message Passing Interface is employed for implementing the proposed approach. Quantitative results obtained from experiments reveal that the agent based approach can handle failures more efficiently than traditional failure handling approaches