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cuTauLeaping: A GPU-Powered Tau-Leaping Stochastic Simulator for Massive Parallel Analyses of Biological Systems

Figure 4

Pseudocode of cuTauLeaping – kernel .

Device-side pseudocode of kernel in cuTauLeaping, implementing the subdivision of threads according to the value and the execution of a tau-leaping step. The kernel starts by loading the vectors and – which correspond to the current state of the system and to the values of stochastic constants, respectively – from the global memory areas that contain these data for all threads. Since these information are frequently accessed, they are immediately copied into the faster shared memory as vectors x and c, respectively. The kernel continues by verifying that the value for the running thread is not equal to the signal of terminated execution (i.e., ). Then, it calculates the propensity functions of all reactions and accumulates their values in ; if , the remaining time instants where the dynamics of the system is sampled are set to the current state and the simulation is terminated. The kernel concludes the phase P1 by calculating a putative value for the tau-leaping step: if is smaller than , then thread is halted and is set to 0, so that it will perform the SSA steps during the next phase. Otherwise, the tau-leaping algorithm is performed by executing a set of non-critical reactions and (possibly) one critical reaction and, if the simulation has overrun one of the sampling time instants, the state stored in is determined by linear interpolation.

doi: https://doi.org/10.1371/journal.pone.0091963.g004