Romain Reuillon

PARALLELIZATION OF MONTE CARLO SIMULATIONS AND SUBMISSION TO A GRID ENVIRONMENT

Monte Carlo simulations are increasingly used in medical physics. In scintigraphic imaging these simulations are used to model imaging systems and to develop and assess tomographic reconstruction algorithms and correction methods for improved image quantization. In radiotherapy-brachytherapy the goal is to evaluate accurately the dosimetry in complex phantoms and at interfaces of tissue, where analytic calculations have shown some limits. The main drawback of Monte Carlo simulations is their high computing time. The aim of our research is to reduce the computing time by parallelizing a simulation on geographically distributed processors. The method is based on the parallelization of the Random Number Generator (RNG) used in Monte Carlo simulations. The long serial of numbers used by the sequential simulation is split. Once the partitioning is done, a software application allows the user to generate automatically the files describing each simulation part. Finally, another software executes them on the DataGrid testbed using an API. All these steps have been made transparent for the user by providing a web page asking the user for all the parameters necessary to launch the simulation and retrieve results. Different tests have been done in order to show first, the reliability of the physical results obtained by concatenation of parallelized output data and secondly the time gained for jobs execution.

OpenMOLE, a workflow engine specifically tailored for the distributed exploration of simulation models

Complex-systems describe multiple levels of collective structure and organization. In such systems, the emergence of global behaviour from local interactions is generally studied through large scale experiments on numerical models. This analysis generates important computation loads which require the use of multi-core servers, clusters or grid computing. Dealing with such large scale executions is especially challenging for modellers who do not possess the theoretical and methodological skills required to take advantage of high performance computing environments. That is why we have designed a cloud approach for model experimentation. This approach has been implemented in OpenMOLE (Open MOdeL Experiment) as a Domain Specific Language (DSL) that leverages the naturally parallel aspect of model experiments. The OpenMOLE DSL has been designed to explore user-supplied models. It delegates transparently their numerous executions to remote execution environment. From a user perspective, those environments are viewed as services providing computing power, therefore no technical detail is ever exposed. This paper presents the OpenMOLE DSL through the example of a toy model exploration and through the automated calibration of a real-world complex-system model in the field of geography.

Declarative task delegation in OpenMOLE

In this paper we present OpenMOLE, a scientific framework providing a virtualized runtime environment for distributed computing. Current distributed execution systems do not hide the hardware and software heterogeneity of computing and data resources whereas OpenMOLE provides generic services to develop distributed scientific algorithms independently from the execution environment architecture. OpenMOLE uses abstraction layers to delegate computing tasks with the same high level interface for the major underlying architectures: local processors, batch systems, computational grids, Internet computing and cloud computing. The file access abstraction layer is another key feature helping a generic usage of the computation power provided by grids and clusters. The OpenMOLE framework has been tested with the exploration of a bacterial biofilm simulation with an individual-based model.

Rigorous Distribution of Stochastic Simulations Using the DistMe Toolkit

Monte Carlo simulations are considered as naturally parallel, because many replications of the same experiment can be distributed on multiple execution units to reduce the global simulation time. However, one needs to take care of the underlying random number streams and ensure that the generated streams do not show intra or inter-correlations. Such errors occur in naive parallelizing approaches, they can lead to erroneous results or to a significant loss in precision. Based on a generic and documented XML format for random number generator statuses and on automatic tools to distribute stochastic simulations, the DistMe software package eases the distribution of stochastic simulations, while keeping the quality of the parallel random number streams as a critical issue. It is written in Java and has been designed to be run on any operating system and hardware with a Java virtual machine available. It has been designed using model engineering to obtain a high quality, modular and very extensible software. This toolkit, freely available on Sourceforge, is designed to speed up Monte Carlo simulations using any parallel machine based on the bag of work paradigm. It provides the user with a set of classes representing a description at a meta level of his simulation environments. Once the developer has described his simulation using DistMe classes, simulation jobs ready for runtime are instantiated. This software is released under GPL licence and the latest development sources are available online (Sourceforge CVS). This paper presents the architecture of DistMe and simulation distribution examples for Geant4 and GATE simulations. The impact of correlations is shown on the GATE application.

Half a Billion Simulations: Evolutionary Algorithms and Distributed Computing for Calibrating the Simpoplocal Geographical Model

Multiagent geographical models integrate very large numbers of spatial interactions. In order to validate these models a large amount of computing is necessary for their simulation and calibration. Here a new data-processing chain, including an automated calibration procedure, is tested on a computational grid using evolutionary algorithms. This is applied for the first time to a geographical model designed to simulate the evolution of an early urban settlement system. The method enables us to reduce the computing time and provides robust results. Using this method, we identify several parameter settings that minimize three objective functions that quantify how closely the model results match a reference pattern. As the values of each parameter in different settings are very close, this estimation considerably reduces the initial possible domain of variation of the parameters. Thus the model is a useful tool for further multiple applications in empirical historical situations.

Beyond Corroboration: Strengthening Model Validation by Looking for Unexpected Patterns

Models of emergent phenomena are designed to provide an explanation to global-scale phenomena from local-scale processes. Model validation is commonly done by verifying that the model is able to reproduce the patterns to be explained. We argue that robust validation must not only be based on corroboration, but also on attempting to falsify the model, i.e. making sure that the model behaves soundly for any reasonable input and parameter values. We propose an open-ended evolutionary method based on Novelty Search to look for the diverse patterns a model can produce. The Pattern Space Exploration method was tested on a model of collective motion and compared to three common a priori sampling experiment designs. The method successfully discovered all known qualitatively different kinds of collective motion, and performed much better than the a priori sampling methods. The method was then applied to a case study of city system dynamics to explore the model’s predicted values of city hierarchisation and population growth. This case study showed that the method can provide insights on potential predictive scenarios as well as falsifiers of the model when the simulated dynamics are highly unrealistic.

A Modular Modelling Framework for Hypotheses Testing in the Simulation of Urbanisation

In this paper, we present a modelling experiment developed to study systems of cities and processes of urbanisation in large territories over long time spans. Building on geographical theories of urban evolution, we rely on agent-based models to 1) formalise complementary and alternative hypotheses of urbanisation and 2) explore their ability to simulate observed patterns in a virtual laboratory. The paper is therefore divided into two sections : an overview of the mechanisms implemented to represent competing hypotheses used to simulate urban evolution; and an evaluation of the resulting model structures in their ability to simulate—efficiently and parsimoniously—a system of cities (between 1000 and 2000 cities in the Former Soviet Union) over several periods of time (before and after the crash of the USSR). We do so using a modular framework of model-building and evolutionary algorithms for the calibration of several model structures. This project aims at tackling equifinality in systems dynamics by confronting different mechanisms with similar evaluation criteria. It enables the identification of the best-performing models with respect to the chosen criteria by scanning automatically the parameter space along with the space of model structures (the different combinations of mechanisms).

Urban Dynamics and Simulation Models

Despite uncertainties linked to the increasing speed of technological and societal evolution, important features of future urbanism can be predicted at the regional and global levels and even sometimes for local situations.Comparative urban studies have brought results about universal processes and typical trajectories in the history of urban systems. This analytic description provides the basis for designing robust dynamic models as well as realistic scenarios for exploring a diversity of possible urban futures. Introduction: Systems of Cities as Adaptive Complex Systems Cities are places of the world where the majority of human beings are living now and where the largest amounts of physical and societal wealth, skills, and values of humankind are concentrated. Much is expected by many stakeholders about predicting what can be expected from their future evolution. A recent report by the World Bank (2009) underlines the essential role of cities in economic development and technological and social innovations, for the first time recognizing the value of urban concentrations, even in poor countries. TheWorld Bank suggests that interventions be targeted according to the type of city, via a regional, hierarchical typology: metropolitan areas (areas of advanced urbanization) are the most liable to make use of productive investments; intermediate or small cities (intermediate urbanization) and densely populated ‘lagging areas’ diffuse this growth towards rural areas, while the incipient urbanization in sparsely populated lagging areas means that they do not draw much benefit from the process. Hence this report sets out to explore the geographical diversity of cities with respect to size and regional density, the effects of concentration on city growth and on the ability of cities to diffuse these effects towards their hinterlands. While facing tremendous challenges from the perspective of environmental change, economic competition, disruptive technological innovation or political conflicts, the resilience of cities is often questioned. The originality of our approach is to assess predictions about possible futures for cities from the knowledge that was constructed about their past dynamics (Pred 1977; Pumain et al. 2015). The knowledge we present here is new because it is inspired not by the observation of isolated cities but by the lessons learnt from their relative situation in systems of cities and

Innovative In Silico Approaches to Address Avian Flu Using Grid Technology

The recent years have seen the emergence of diseases which have spread very quickly all around the world either through human travels like SARS or animal migration like avian flu. Among the biggest challenges raised by infectious emerging diseases, one is related to the constant mutation of the viruses which turns them into continuously moving targets for drug and vaccine discovery. Another challenge is related to the early detection and surveillance of the diseases as new cases can appear just anywhere due to the globalization of exchanges and the circulation of people and animals around the earth, as recently demonstrated by the avian flu epidemics. For 3 years now, a collaboration of teams in Europe and Asia has been exploring some innovative in silico approaches to better tackle avian flu taking advantage of the very large computing resources available on international grid infrastructures. Grids were used to study the impact of mutations on the effectiveness of existing drugs against H5N1 and to find potentially new leads active on mutated strains. Grids allow also the integration of distributed data in a completely secured way. The paper proposes new approaches for the integration of existing data sources towards a global surveillance network for molecular epidemiology and in silico drug discovery.

Model exploration using OpenMOLE a workflow engine for large scale distributed design of experiments and parameter tuning

OpenMOLE is a scientific workflow engine with a strong emphasis on workload distribution. Workflows are designed using a high level Domain Specific Language (DSL) built on top of Scala. It exposes natural parallelism constructs to easily delegate the workload resulting from a workflow to a wide range of distributed computing environments. In this work, we briefly expose the strong assets of OpenMOLE and demonstrate its efficiency at exploring the parameter set of an agent simulation model. We perform a multi-objective optimisation on this model using computationally expensive Genetic Algorithms (GA). OpenMOLE hides the complexity of designing such an experiment thanks to its DSL, and transparently distributes the optimisation process. The example shows how an initialisation of the GA with a population of 200,000 individuals can be evaluated in one hour on the European Grid Infrastructure.