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The Gillespie algorithm is a powerful computational tool to simulate the dynamics of a system of interacting chemical species in regimes where particle numbers are small, and stochastic fluctuations are large [1]. This well-known algorithm becomes computationally demanding when one attempts to sample a large number of configurations, e.g. looking for rare samples in the dynamics, or simulating a large number of species or reactions. In this internship, we propose to develop a new method to increase the computational yield of the algorithm, by leveraging the boolean representation of numbers as they are stored in a computer [2].
This method allows for simulating simultaneously multiple copies of the system, which share the same random number used to draw the system samples. Because the random-number generation is the bottleneck of the simulation, this parallel algorithm yields a significant gain in performance. Most importantly, given that the copies of the system are initialized with random, independent initial conditions, their dynamics
is independent, and it allows...
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