Parallel Load Balancing Strategies for Ensembles of Stochastic Biochemical Simulations

The evolution of biochemical systems where some chemical species are present with only a small number of molecules, is strongly influenced by discrete and stochastic effects that cannot be accurately captured by continuous and deterministic models. The budding yeast cell cycle provides an excellent example of the need to account for stochastic effects in biochemical reactions. To obtain statistics of the cell cycle progression, a stochastic simulation algorithm must be run thousands of times with different initial conditions and parameter values. In order to manage the computational expense involved, the large ensemble of runs needs to be executed in parallel. The CPU time for each individual task is unknown before execution, so a simple strategy of assigning an equal number of tasks per processor can lead to considerable work imbalances and loss of parallel efficiency. Moreover, deterministic analysis approaches are ill suited for assessing the effectiveness of load balancing algorithms in this context. Biological models often require stochastic simulation. Since generating an ensemble of simulation results is computationally intensive, it is important to make efficient use of computer resources. This paper presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms when applied to large ensembles of stochastic biochemical simulations. Two particular load balancing strategies (point-to-point and all-redistribution) are discussed in detail. Simulation results with a stochastic budding yeast cell cycle model confirm the theoretical analysis. While this work is motivated by cell cycle modeling, the proposed analysis framework is general and can be directly applied to any ensemble simulation of biological systems where many tasks are mapped onto each processor, and where the individual compute times vary considerably among tasks

Parallel Stochastic Evolution Algorithms for Constrained Multiobjective Optimization

Stochastic evolution (StocE) is an evolutionary metaheuristic that has shown to achieve better solution qualities and runtimes when compared to some other well established stochastic metaheuristics. However, unlike these metaheuristics, parallelization of StocE has not been explored before. In this paper, we discuss a comprehensive set of parallel strategies for StocE using a constrained multiobjective VLSI cell placement as an optimization problem. The effectiveness of the proposed strategy is demonstrated by comparing its results with results of parallel SA algorithms on the same optimization problem

Parallel Stochastic Evolution Algorithms for Constrained Multiobjective Optimization

Stochastic evolution (StocE) is an evolutionary metaheuristic that has shown to achieve better solution qualities and runtimes when compared to some other well established stochastic metaheuristics. However, unlike these metaheuristics, parallelization of StocE has not been explored before. In this paper, we discuss a comprehensive set of parallel strategies for StocE using a constrained multiobjective VLSI cell placement as an optimization problem. The effectiveness of the proposed strategy is demonstrated by comparing its results with results of parallel SA algorithms on the same optimization problem

Parallel processing in immune networks

In this work we adopt a statistical mechanics approach to investigate basic, systemic features exhibited by adaptive immune systems. The lymphocyte network made by B-cells and T-cells is modeled by a bipartite spin-glass, where, following biological prescriptions, links connecting B-cells and T-cells are sparse. Interestingly, the dilution performed on links is shown to make the system able to orchestrate parallel strategies to fight several pathogens at the same time; this multitasking capability constitutes a remarkable, key property of immune systems as multiple antigens are always present within the host. We also define the stochastic process ruling the temporal evolution of lymphocyte activity, and show its relaxation toward an equilibrium measure allowing statistical mechanics investigations. Analytical results are compared with Monte Carlo simulations and signal-to-noise outcomes showing overall excellent agreement. Finally, within our model, a rationale for the experimentally well-evidenced correlation between lymphocytosis and autoimmunity is achieved; this sheds further light on the systemic features exhibited by immune networks.Comment: 21 pages, 9 figures; to appear in Phys. Rev.