Accelerating 3D Cellular Automata Computation with GP GPU in the Context of Integrative Biology

In this paper we explore the possibility of using GP GPU technology (General Purpose Graphical Processing Unit) in the context of integrative biology. For more than a decade, 3D cellular automata represent a promising approach to handling multi-scale modeling of organs. However, the computing time of such huge automata has limited the experiments. Current GP GPUs now allow the execution of hundreds of threads with a regular PC hosting a device card. This capability can be exploited in the case of cellular automata where each cell has to compute the same algorithm. We have implemented two algorithms to compare different memory usage. The performances show very significant speedup even when compared to the latest CPU processors. The interconnection of GP GPU boards and servers will be considered to build a local grid of hybrid machines

Warp-Level Parallelism: Enabling Multiple Replications In Parallel on GPU

Best paper awardInternational audienceStochastic simulations need multiple replications in order to build confidence intervals for their results. Even if we do not need a large amount of replications, it is a good practice to speed-up the whole simulation time using the Multiple Replications In Parallel (MRIP) approach. This approach usually supposes to have access to a parallel computer such as a symmetric mul-tiprocessing machine (with many cores), a computing cluster or a computing grid. In this paper, we propose Warp-Level Parallelism (WLP), a GP-GPU-enabled solution to compute MRIP on GP-GPUs (General-Purpose Graphics Processing Units). These devices display a great amount of parallel computational power at low cost, but are tuned to process efficiently the same operation on several data, through different threads. Indeed, this paradigm is called Single Instruction, Multiple Threads (SIMT). Our approach proposes to rely on small threads groups, called warps, to perform independent computations such as replications. We have benchmarked WLP with three different models: it allows MRIP to be computed up to six times faster than with the SIMT computing paradigm

Parallel stepwise stochastic simulation: Harnessing GPUs to Explore Possible Futures States of a Chromosome Folding Model Thanks to the Possible Futures Algorithm (PFA)

International audienceFor the sake of software compatibility, simulations are often parallelized withoutmuch code rewriting. Performances can be further improved by optimizing codes so that to use themaximum power offered by parallel architectures. While this approach can provide some speed-up,performance of parallelized codes can be strongly limited a priori because traditional algorithmshave been designed for sequential technologies. Thus, additional increase of performance shouldultimately rely on some redesign of algorithms.Here, we redesign an algorithm that has traditionally been used to simulate the folding proper-ties of polymers. We address the issue of performance in the context of biological applications,more particularly in the active field of chromosome modelling. Due to the strong confinementof chromosomes in the cells, simulation of their motion is slowed down by the laborious searchfor the next valid states to progress. Our redesign, that we call the Possible Futures Algorithm(PFA), relies on the parallel computation of possible evolutions of the same state, which effectivelyincreases the probability to obtain a valid state at each step. We apply PFA on a GPU-basedarchitecture, allowing us to optimally reduce the latency induced by the computation overhead ofpossible futures. We show that compared to the initial sequential model the acceptance rate of newstates significantly increases without impacting the execution time. In particular, the stronger theconfinement of the chromosome, the more efficient PFA becomes, making our approach appealingfor biological applications.While most of our results were obtained using Fermi architecture GPUs from NVIDIA, we highlightimproved performance on the cutting-edge Kepler architecture K20 GPUs

Parallélisation et optimisation d'un simulateur de morphogénèse d'organes. Application aux éléments du rein

Depuis plusieurs dizaines d années, la modélisation du vivant est un enjeu majeur qui nécessite de plus en plus de travaux dans le domaine de la simulation. En effet, elle ouvre la porte à toute une palette d applications: l aide à la décision en environnement et en écologie, l aide à l enseignement, l aide à la décision pour les médecins, l aide à la recherche de nouveaux traitements pharmaceutiques et la biologie dite prédictive , etc. Avant de pouvoir aborder un problème, il est nécessaire de pouvoir modéliser de façon précise le système biologique concerné en précisant bien les questions auxquelles devra répondre le modèle. La manipulation et l étude de systèmes complexes, les systèmes biologiques en étant l archétype, pose, de façon générale, des problèmes de modélisation et de simulation. C est dans ce contexte que la société Integrative BioComputing (IBC) développe depuis le début des années 2000 un prototype d une Plateforme Générique de Modélisation et de Simulation(la PGMS) dont le but est de fournir un environnement pour modéliser et simuler plus simplement les processus et les fonctions biologiques d un organisme complet avec les organes le composant. La PGMS étant une plateforme générique encore en phase de développement, elle ne possédait pas les performances nécessaires pour permettre de réaliser la modélisation et la simulation d éléments importants dans des temps suffisamment courts. Il a donc été décidé, afin d améliorer drastiquement les performances de la PGMS, de paralléliser et d optimiser l implémentation de celle-ci; le but étant de permettre la modélisation et la simulation d organes complets dans des temps acceptables. Le travail réalisé au cours de cette thèse a donc consisté à traiter différents aspects de la modélisation et de la simulation de systèmes biologiques afin d accélérer les traitements de ceux-ci. Le traitement le plus gourmand en termes de temps de calcul lors de l exécution de la PGMS, le calcul des champs physicochimiques, a ainsi fait l objet d une étude de faisabilité de sa parallélisation. Parmi les différentes architectures disponibles pour paralléliser une telle application, notre choix s est porté sur l utilisation de GPU (Graphical Processing Unit) à des fins de calculs généralistes aussi couramment appelé GPGPU (General-Purpose computation on Graphics Processing Units). Ce choix a été réalisé du fait, entre autres, du coût réduit du matériel et de sa très grande puissance de calcul brute qui en fait une des architectures de parallélisation les plus accessibles du marché. Les résultats de l étude de faisabilité étant particulièrement concluant, la parallélisation du calcul des champs a ensuite été intégrée à la PGMS. En parallèle, nous avons également mené des travaux d optimisations pour améliorer les performances séquentielles de la PGMS. Le résultat de ces travaux est une augmentation de la vitesse d exécution d un facteur 18,12x sur les simulations les plus longues (passant de 16 minutes pour la simulation non optimisée utilisant un seul cœur CPU à 53 secondes pour la version optimisée utilisant toujours un seul cœur CPU mais aussi un GPU GTX500). L autre aspect majeur traité dans ces travaux a été d améliorer les performances algorithmiques pour la simulation d automates cellulaires en trois dimensions. En effet, ces derniers permettent aussi bien de simuler des comportements biologiques que d implémenter des mécanismes de modélisation tels que les interactions multi-échelles. Le travail de recherche s est essentiellement effectué sur des propositions algorithmiques originales afin d améliorer les simulations réalisées par IBC sur la PGMS. L accélération logicielle, à travers l implémentation de l algorithme Hash Life en trois dimensions, et la parallélisation à l aide de GPGPU ont été étudiées de façon concomitante et ont abouti à des gains très significatifs en temps de calcul.For some years, living matter modeling has been a major challenge which needs more and more research in the simulation field. Indeed, the use of models of living matter have multiple applications: decision making aid in environment or ecology, teaching tools, decision making aid for physician, research aid for new pharmaceutical treatment and predictive biology, etc. But before being able to tackle all these issues, the development of a correct model, able to give answer about specific questions, is needed. Working with complex systems biologic system being the archetype of them raises various modeling and simulation issues. It is in this context that the Integrative BioComputing (IBC) company have been elaborating, since the early 2000s, the prototype of a generic platform for modeling and simulation (PGMS). Its goal is to provide a platform used to easily model and simulate biological process of a full organism, including its organs. Since the PGMS was still in its development stage at the start of my PhD, the application performance prevented the modeling and simulation of large biological components in an acceptable time. Therefore, it has been decide to optimize and parallelize its computation to increase significantly the PGMS performances. The goal was to enable the use of the PGMS to model and simulate full organs in acceptable times. During my PhD, I had to work on various aspects of the modeling and simulation of biological systems to increase their process speed. Since the most costly process during the PGMS execution was the computation of chemical fields, I had to study the opportunity of parallelizing this process. Among the various hardware architectures available to parallelize this application, we chose to use graphical processing units for general purpose computation (GPGPUs). This choice was motivated, beside other reasons, by the low cost of the hardware compared to its massive computation power, making it one of the most affordable parallel architecture on the market. Since the results of the initial feasibility study were conclusive, the parallelization of the fields computation has been integrated into the PGMS. In parallel to this work, I also worked on optimizing the sequential performance of the application. All these works lead to an increase of the software performances achieving a speed-up of 18.12x for the longest simulation (from 16 minutes for the non-optimized version with one CPU core to 53 seconds for the optimized version, still using only one core on the CPU but also a GPU GTX500). The other major aspect of my work was to increase the algorithmic performances for the simulation of three-dimensional cellular automata. In fact, these automata allow the simulation of biological behavior as they can be used to implement various mechanisms of a model such as multi-scale interactions. The research work consisted mainly in proposing original algorithms to improve the simulation provided by IBC on the PGMS. The sequential speed increase, thanks to the three-dimensional Hash Life implementation, and the parallelization on GPGPU has been studied together and achieved major computation time improvement.CLERMONT FD-Bib.électronique (631139902) / SudocSudocFranceF

Refounding of Activity Concept ? Towards a Federative Paradigm for Modeling and Simulation

Journal : Simulation, Transactions of the Society for Modeling and Simulation InternationalInternational audienceCurrently, the widely used notion of activity is increasingly present in computer science. However, because this notion is used in specific contexts, it becomes vague. Here, the notion of activity is scrutinized in various contexts and, accord-ingly, put in perspective. It is discussed through four scientific disciplines: computer science, biology, economics, and epis-temology. The definition of activity usually used in simulation is extended to new qualitative and quantitative definitions. In computer science, biology and economics disciplines, the new simulation activity definition is first applied critically. Then, activity is discussed generally. In epistemology, activity is discussed, in a prospective way, as a possible framework in models of human beliefs and knowledge

The sub-mm J=6-5 line of 13CO in Orion

We present a fully sampled map covering the Orion Hot Core and dense molecular ridge, in the sub-millimeter J=6-5 rotational transition of 13CO, at 0.45 mm with a resolution of 13 arcsec and 0.5 km s^-1. The map covers 3 arc min by 2arc min . The profile centered on the Hot Core peaks at 8.5 km s^-1 and has a peak intensity of 40 K, corrected antenna temperature. It shows line wings from 30 km s^-1 to -20 km s^-1. The map of intensity, integrated from 0 to +18 km s^-1, shows a prominent maximum <5 arcsec from the center of the Orion Hot Core. The FWHP is 3 arcsec, larger than the regions containing complex molecules. Single dish measurements of lines from the J=2-1 or J=1-0 transitions of CO isotopes show no such distinct maximum. Correcting for optical depth 1.5 in the J=6-5 line of 13CO, and assuming that the level populations are thermalized at 150 K, the beam averaged column density between 0 to +18 km s^-1 is N(13CO )=6.8 10^17 cm^-2 and N(CO)=5.2 10^19 cm-2. When combined with published dust emission data, the CO/ H2 number ratio is 2 {\cdot} 10^-5, a factor of ~5 lower than the canonical value, 10^-4. For the Orion South and Orion Ridge region, the column density of CO is <25% of that found for the Hot Core but CO/H2 ratios are similar. Models of Photodissociation Regions, PDRs, predict that CO lines from PDRs are only marginally optically thick. Thus our map traces warm and dense molecular gas rather than PDRs.Comment: 23 pages total, including 5 figure

The predictive ability of the 313 variant–based polygenic risk score for contralateral breast cancer risk prediction in women of European ancestry with a heterozygous BRCA1 or BRCA2 pathogenic variant

Abstract: Purpose: To evaluate the association between a previously published 313 variant–based breast cancer (BC) polygenic risk score (PRS313) and contralateral breast cancer (CBC) risk, in BRCA1 and BRCA2 pathogenic variant heterozygotes. Methods: We included women of European ancestry with a prevalent first primary invasive BC (BRCA1 = 6,591 with 1,402 prevalent CBC cases; BRCA2 = 4,208 with 647 prevalent CBC cases) from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA), a large international retrospective series. Cox regression analysis was performed to assess the association between overall and ER-specific PRS313 and CBC risk. Results: For BRCA1 heterozygotes the estrogen receptor (ER)-negative PRS313 showed the largest association with CBC risk, hazard ratio (HR) per SD = 1.12, 95% confidence interval (CI) (1.06–1.18), C-index = 0.53; for BRCA2 heterozygotes, this was the ER-positive PRS313, HR = 1.15, 95% CI (1.07–1.25), C-index = 0.57. Adjusting for family history, age at diagnosis, treatment, or pathological characteristics for the first BC did not change association effect sizes. For women developing first BC < age 40 years, the cumulative PRS313 5th and 95th percentile 10-year CBC risks were 22% and 32% for BRCA1 and 13% and 23% for BRCA2 heterozygotes, respectively. Conclusion: The PRS313 can be used to refine individual CBC risks for BRCA1/2 heterozygotes of European ancestry, however the PRS313 needs to be considered in the context of a multifactorial risk model to evaluate whether it might influence clinical decision-making

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Parallelization and optimization of an organ morphogenesis simulator. Application to the elements of the kidney

Depuis plusieurs dizaines d’années, la modélisation du vivant est un enjeu majeur qui nécessite de plus en plus de travaux dans le domaine de la simulation. En effet, elle ouvre la porte à toute une palette d’applications : l’aide à la décision en environnement et en écologie, l’aide à l’enseignement, l’aide à la décision pour les médecins, l’aide à la recherche de nouveaux traitements pharmaceutiques et la biologie dite « prédictive », etc. Avant de pouvoir aborder un problème, il est nécessaire de pouvoir modéliser de façon précise le système biologique concerné en précisant bien les questions auxquelles devra répondre le modèle. La manipulation et l’étude de systèmes complexes, les systèmes biologiques en étant l’archétype, pose, de façon générale, des problèmes de modélisation et de simulation. C’est dans ce contexte que la société Integrative BioComputing (IBC) développe depuis le début des années 2000 un prototype d’une Plateforme Générique de Modélisation et de Simulation (la PGMS) dont le but est de fournir un environnement pour modéliser et simuler plus simplement les processus et les fonctions biologiques d’un organisme complet avec les organes le composant. La PGMS étant une plateforme générique encore en phase de développement, elle ne possédait pas les performances nécessaires pour permettre de réaliser la modélisation et la simulation d’éléments importants dans des temps suffisamment courts. Il a donc été décidé, afin d’améliorer drastiquement les performances de la PGMS, de paralléliser et d’optimiser l’implémentation de celle-ci ; le but étant de permettre la modélisation et la simulation d’organes complets dans des temps acceptables. Le travail réalisé au cours de cette thèse a donc consisté à traiter différents aspects de la modélisation et de la simulation de systèmes biologiques afin d’accélérer les traitements de ceux-ci. Le traitement le plus gourmand en termes de temps de calcul lors de l’exécution de la PGMS, le calcul des champs physicochimiques, a ainsi fait l’objet d’une étude de faisabilité de sa parallélisation. Parmi les différentes architectures disponibles pour paralléliser une telle application, notre choix s’est porté sur l’utilisation de GPU (Graphical Processing Unit) à des fins de calculs généralistes aussi couramment appelé GPGPU (General-Purpose computation on Graphics Processing Units). Ce choix a été réalisé du fait, entre autres, du coût réduit du matériel et de sa très grande puissance de calcul brute qui en fait une des architectures de parallélisation les plus accessibles du marché. Les résultats de l’étude de faisabilité étant particulièrement concluant, la parallélisation du calcul des champs a ensuite été intégrée à la PGMS. En parallèle, nous avons également mené des travaux d’optimisations pour améliorer les performances séquentielles de la PGMS. Le résultat de ces travaux est une augmentation de la vitesse d’exécution d’un facteur 18,12x sur les simulations les plus longues (passant de 16 minutes pour la simulation non optimisée utilisant un seul cœur CPU à 53 secondes pour la version optimisée utilisant toujours un seul cœur CPU mais aussi un GPU GTX500). L’autre aspect majeur traité dans ces travaux a été d’améliorer les performances algorithmiques pour la simulation d’automates cellulaires en trois dimensions. En effet, ces derniers permettent aussi bien de simuler des comportements biologiques que d’implémenter des mécanismes de modélisation tels que les interactions multi-échelles. Le travail de recherche s’est essentiellement effectué sur des propositions algorithmiques originales afin d’améliorer les simulations réalisées par IBC sur la PGMS. L’accélération logicielle, à travers l’implémentation de l’algorithme Hash‑Life en trois dimensions, et la parallélisation à l’aide de GPGPU ont été étudiées de façon concomitante et ont abouti à des gains très significatifs en temps de calcul.For some years, living matter modeling has been a major challenge which needs more and more research in the simulation field. Indeed, the use of models of living matter have multiple applications: decision making aid in environment or ecology, teaching tools, decision making aid for physician, research aid for new pharmaceutical treatment and “predictive” biology, etc. But before being able to tackle all these issues, the development of a correct model, able to give answer about specific questions, is needed. Working with complex systems –biologic system being the archetype of them– raises various modeling and simulation issues. It is in this context that the Integrative BioComputing (IBC) company have been elaborating, since the early 2000s, the prototype of a generic platform for modeling and simulation (PGMS). Its goal is to provide a platform used to easily model and simulate biological process of a full organism, including its organs. Since the PGMS was still in its development stage at the start of my PhD, the application performance prevented the modeling and simulation of large biological components in an acceptable time. Therefore, it has been decide to optimize and parallelize its computation to increase significantly the PGMS performances. The goal was to enable the use of the PGMS to model and simulate full organs in acceptable times. During my PhD, I had to work on various aspects of the modeling and simulation of biological systems to increase their process speed. Since the most costly process during the PGMS execution was the computation of chemical fields, I had to study the opportunity of parallelizing this process. Among the various hardware architectures available to parallelize this application, we chose to use graphical processing units for general purpose computation (GPGPUs). This choice was motivated, beside other reasons, by the low cost of the hardware compared to its massive computation power, making it one of the most affordable parallel architecture on the market. Since the results of the initial feasibility study were conclusive, the parallelization of the fields computation has been integrated into the PGMS. In parallel to this work, I also worked on optimizing the sequential performance of the application. All these works lead to an increase of the software performances achieving a speed-up of 18.12x for the longest simulation (from 16 minutes for the non-optimized version with one CPU core to 53 seconds for the optimized version, still using only one core on the CPU but also a GPU GTX500). The other major aspect of my work was to increase the algorithmic performances for the simulation of three-dimensional cellular automata. In fact, these automata allow the simulation of biological behavior as they can be used to implement various mechanisms of a model such as multi-scale interactions. The research work consisted mainly in proposing original algorithms to improve the simulation provided by IBC on the PGMS. The sequential speed increase, thanks to the three-dimensional Hash Life implementation, and the parallelization on GPGPU has been studied together and achieved major computation time improvement

Parallélisation et optimisation d'un simulateur de morphogénèse d'organes. Application aux éléments du rein

For some years, living matter modeling has been a major challenge which needs more and more research in the simulation field. Indeed, the use of models of living matter have multiple applications: decision making aid in environment or ecology, teaching tools, decision making aid for physician, research aid for new pharmaceutical treatment and “predictive” biology, etc. But before being able to tackle all these issues, the development of a correct model, able to give answer about specific questions, is needed. Working with complex systems –biologic system being the archetype of them– raises various modeling and simulation issues. It is in this context that the Integrative BioComputing (IBC) company have been elaborating, since the early 2000s, the prototype of a generic platform for modeling and simulation (PGMS). Its goal is to provide a platform used to easily model and simulate biological process of a full organism, including its organs. Since the PGMS was still in its development stage at the start of my PhD, the application performance prevented the modeling and simulation of large biological components in an acceptable time. Therefore, it has been decide to optimize and parallelize its computation to increase significantly the PGMS performances. The goal was to enable the use of the PGMS to model and simulate full organs in acceptable times. During my PhD, I had to work on various aspects of the modeling and simulation of biological systems to increase their process speed. Since the most costly process during the PGMS execution was the computation of chemical fields, I had to study the opportunity of parallelizing this process. Among the various hardware architectures available to parallelize this application, we chose to use graphical processing units for general purpose computation (GPGPUs). This choice was motivated, beside other reasons, by the low cost of the hardware compared to its massive computation power, making it one of the most affordable parallel architecture on the market. Since the results of the initial feasibility study were conclusive, the parallelization of the fields computation has been integrated into the PGMS. In parallel to this work, I also worked on optimizing the sequential performance of the application. All these works lead to an increase of the software performances achieving a speed-up of 18.12x for the longest simulation (from 16 minutes for the non-optimized version with one CPU core to 53 seconds for the optimized version, still using only one core on the CPU but also a GPU GTX500). The other major aspect of my work was to increase the algorithmic performances for the simulation of three-dimensional cellular automata. In fact, these automata allow the simulation of biological behavior as they can be used to implement various mechanisms of a model such as multi-scale interactions. The research work consisted mainly in proposing original algorithms to improve the simulation provided by IBC on the PGMS. The sequential speed increase, thanks to the three-dimensional Hash Life implementation, and the parallelization on GPGPU has been studied together and achieved major computation time improvement.Depuis plusieurs dizaines d’années, la modélisation du vivant est un enjeu majeur qui nécessite de plus en plus de travaux dans le domaine de la simulation. En effet, elle ouvre la porte à toute une palette d’applications : l’aide à la décision en environnement et en écologie, l’aide à l’enseignement, l’aide à la décision pour les médecins, l’aide à la recherche de nouveaux traitements pharmaceutiques et la biologie dite « prédictive », etc. Avant de pouvoir aborder un problème, il est nécessaire de pouvoir modéliser de façon précise le système biologique concerné en précisant bien les questions auxquelles devra répondre le modèle. La manipulation et l’étude de systèmes complexes, les systèmes biologiques en étant l’archétype, pose, de façon générale, des problèmes de modélisation et de simulation. C’est dans ce contexte que la société Integrative BioComputing (IBC) développe depuis le début des années 2000 un prototype d’une Plateforme Générique de Modélisation et de Simulation (la PGMS) dont le but est de fournir un environnement pour modéliser et simuler plus simplement les processus et les fonctions biologiques d’un organisme complet avec les organes le composant. La PGMS étant une plateforme générique encore en phase de développement, elle ne possédait pas les performances nécessaires pour permettre de réaliser la modélisation et la simulation d’éléments importants dans des temps suffisamment courts. Il a donc été décidé, afin d’améliorer drastiquement les performances de la PGMS, de paralléliser et d’optimiser l’implémentation de celle-ci ; le but étant de permettre la modélisation et la simulation d’organes complets dans des temps acceptables. Le travail réalisé au cours de cette thèse a donc consisté à traiter différents aspects de la modélisation et de la simulation de systèmes biologiques afin d’accélérer les traitements de ceux-ci. Le traitement le plus gourmand en termes de temps de calcul lors de l’exécution de la PGMS, le calcul des champs physicochimiques, a ainsi fait l’objet d’une étude de faisabilité de sa parallélisation. Parmi les différentes architectures disponibles pour paralléliser une telle application, notre choix s’est porté sur l’utilisation de GPU (Graphical Processing Unit) à des fins de calculs généralistes aussi couramment appelé GPGPU (General-Purpose computation on Graphics Processing Units). Ce choix a été réalisé du fait, entre autres, du coût réduit du matériel et de sa très grande puissance de calcul brute qui en fait une des architectures de parallélisation les plus accessibles du marché. Les résultats de l’étude de faisabilité étant particulièrement concluant, la parallélisation du calcul des champs a ensuite été intégrée à la PGMS. En parallèle, nous avons également mené des travaux d’optimisations pour améliorer les performances séquentielles de la PGMS. Le résultat de ces travaux est une augmentation de la vitesse d’exécution d’un facteur 18,12x sur les simulations les plus longues (passant de 16 minutes pour la simulation non optimisée utilisant un seul cœur CPU à 53 secondes pour la version optimisée utilisant toujours un seul cœur CPU mais aussi un GPU GTX500). L’autre aspect majeur traité dans ces travaux a été d’améliorer les performances algorithmiques pour la simulation d’automates cellulaires en trois dimensions. En effet, ces derniers permettent aussi bien de simuler des comportements biologiques que d’implémenter des mécanismes de modélisation tels que les interactions multi-échelles. Le travail de recherche s’est essentiellement effectué sur des propositions algorithmiques originales afin d’améliorer les simulations réalisées par IBC sur la PGMS. L’accélération logicielle, à travers l’implémentation de l’algorithme Hash‑Life en trois dimensions, et la parallélisation à l’aide de GPGPU ont été étudiées de façon concomitante et ont abouti à des gains très significatifs en temps de calcul