ACM Transactions on Programming Languages and Systems (TOPLAS)
Graph partitioning for high-performance scientific simulations
Sourcebook of parallel computing
On Parallel Stochastic Simulation of Diffusive Systems
CMSB '08 Proceedings of the 6th International Conference on Computational Methods in Systems Biology
Optimized Parallel Implementation of Gillespie's First Reaction Method on Graphics Processing Units
ICCMS '09 Proceedings of the 2009 International Conference on Computer Modeling and Simulation
Computational Biology and Chemistry
International Journal of High Performance Computing Applications
PDMC-HIBI '10 Proceedings of the 2010 Ninth International Workshop on Parallel and Distributed Methods in Verification, and Second International Workshop on High Performance Computational Systems Biology
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Important achievements in traditional biology have deepened the knowledge about living systems leading to an extensive identification of parts-list of the cell as well as of the interactions among biochemical species responsible for cell's regulation. Such an expanding knowledge also introduces new issues. For example, the increasing comprehension of the interdependencies between pathways (pathways cross-talk) has resulted, on one hand, in the growth of informational complexity, on the other, in a strong lack of information coherence. The overall grand challenge remains unchanged: to be able to assemble the knowledge of every "piece” of a system in order to figure out the behavior of the whole (integrative approach). In light of these considerations, high performance computing plays a fundamental role in the context of in-silico biology. Stochastic simulation is a renowned analysis tool, which, although widely used, is subject to stringent computational requirements, in particular when dealing with heterogeneous and high dimensional systems. Here, we introduce and discuss a methodology aimed at alleviating the burden of simulating complex biological networks. Such a method, which springs from graph theory, is based on the principle of fragmenting the computational space of a simulation trace and delegating the computation of fragments to a number of parallel processes.