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.
