Sampling Chaotic Trajectories Quickly in Parallel

Abstract

The parallel computational complexity of the quadratic map is studied. A parallel algorithm is described that generates typical pseudotrajectories of length t in a time that scales as log t and increases slowly in the accuracy demanded of the pseudotrajectory. Long pseudotrajectories are created in parallel by putting together many short pseudotrajectories; Monte Carlo procedures are used to eliminate the discontinuities between these short pseudotrajectories and then suitably randomize the resulting long pseudotrajectory. Numerical simulations are presented that show the scaling properties of the parallel algorithm. The existence of the fast parallel algorithm provides a way to formalize the intuitive notion that chaotic systems do not generate complex histories.