Plusieurs types d’expérimentation existent pour étudier et comprendre les systèmes biologiques. Dans ces travaux, nous nous intéressons à la simulation in silico, c’est-à-dire à la simulation numérique de modèles sur un ordinateur. Les systèmes biologiques sont composés d’entités, à la fois nombreuses et variées, en interaction les unes avec les autres. Ainsi, ils peuvent être modélisés par l’intermédiaire de deux approches complémentaires : l’approche population-centrée et l’approche individu-centrée. Face à la multitude et à la variété des phénomènes composant les systèmes biologiques, il nous semble pertinent de coupler ces deux approches pour obtenir une modélisation mixte. En outre, en raison de la quantité conséquente d’informations que représente l’ensemble des entités et des interactions à modéliser, la simulation numérique des systèmes biologiques est particulièrement coûteuse en temps de calcul informatique. Ainsi, dans ce mémoire, nous proposons des solutions techniques de parallélisation permettant d’exploiter au mieux les performances offertes par les architectures multicoeur et multiprocesseur et les architectures graphiques pour la simulation de systèmes biologiques à base de modélisations mixtes. Nous appliquons nos travaux au domaine de la coagulation du sang et plus particulièrement à l’étude de la cinétique biochimique à l’échelle microscopique ainsi qu’à la simulation d’un vaisseau sanguin virtuel. Ces deux applications nous permettent d’évaluer les performances offertes par les solutions techniques de parallélisation que nous proposons, ainsi que leur pertinence dans le cadre de la simulation des systèmes biologiques. / Several types of experimentation exist to study and understand biological systems. Inthis document, we take an interest in in silico simulation, i.e. numerical simulation ofmodels on a computer. Biological systems are made of many various entities, interactingwith each other. Therefore, they can be modeled by two complementary approaches: thepopulation-based approach and the individual-based one. Because of the multitude anddiversity of the phenomena constituting biological systems, we find the coupling of thesetwo approaches relevant to provide a hybrid modelisation. Moreover, because of the hugequantity of data that the entities and interactions represent, numerical simulation of biologicalsystems is especially computationaly intensive. This is why, in this document, we proposeparallel computing methods to take advantage of the performances offered by multicore andmultiprocessor architectures and by graphical ones for the simulation of biological systemsusing hybrid modelisations. We apply our work to blood coagulation and especially to thestudy of biochemical kinetics at the microscopic scale and the simulation of a virtual bloodvessel. These two applications enable us to assess both the performances obtained by theparallel computing methods we proposed and their relevance for biological systems simulation.

Multi-core, multi-processor, and Graphics Processing Unit (GPU) computer architectures pose significant challenges with respect to the efficient exploitation of parallelism for large-scale, scientific computing simulations. For example, a simulation of the human tonsil at the cellular level involves the computation of the motion and interaction of millions of cells over extended periods of time. Also, the simulation of Radiative Heat Transfer (RHT) effects by the Photon Monte Carlo (PMC) method is an extremely computationally demanding problem. The PMC method is example of the Monte Carlo simulation method—an approach extensively used in wide of application areas. Although the basic algorithmic framework of these Monte Carlo methods is simple, they can be extremely computationally intensive. Therefore, an efficient parallel realization of these simulations depends on a careful analysis of the nature these problems and the development of an appropriate software framework. The overarching goal of this dissertation is develop and understand what the appropriate parallel programming model should be to exploit these disparate architectures, both from the metric of efficiency, as well as from a software engineering perspective. In this dissertation we examine these issues through a performance study of PathSim2, a software framework for the simulation of large-scale biological systems, using two different parallel architectures’ distributed and shared memory. First, a message-passing implementation of a multiple germinal center simulation by PathSim2 is developed and analyzed for distributed memory architectures. Second, a germinal center simulation is implemented on shared memory architecture with two parallelization strategies based on Pthreads and OpenMP. Finally, we present work targeting a complete hybrid, parallel computing architecture. With this work we develop and analyze a software framework for generic Monte Carlo simulations implemented on multiple, distributed memory nodes consisting of a multi-core architecture with attached GPUs. This simulation framework is divided into two asynchronous parts: (a) a threaded, GPU-accelerated pseudo-random number generator (or producer), and (b) a multi-threaded Monte Carlo application (or consumer). The advantage of this approach is that this software framework can be directly used within any Monte Carlo application code, without requiring application-specific programming of the GPU. We examine this approach through a performance study of the simulation of RHT effects by the PMC method on a hybrid computing architecture. We present a theoretical analysis of our proposed approach, discuss methods to optimize performance based on this analysis, and compare this analysis to experimental results obtained from simulations run on two different hybrid, parallel computing architectures. / Ph. D.