A parallel algorithm is presented for computing the Correlation Dimension (h 2 ) from a time series generated by a dynamical system, using the method of correlation integrals. Three versions are discussed: the ยฎrst computes all distances between points in the phase space, whereas the second and third compute only distances less than a threshold ; the third version in particular is very powerful since it employs a box-assisted approach and linked lists for a fast search of neighboring points.
The parallelization is designed for coarse-grained multiprocessor systems with distributed memory and is accomplished using a message passing model and partitioning points evenly among processors. Uniform implementation and computational analysis allow a clear comparison of the three versions.
The algorithms, tested on the Transtech PARAstation multiprocessor, are well balanced, give a linear speed-up and show a good scalability. The third version is particularly suitable for fast processing of very long time series and allows the estimation of h 2 even for medium-and high-dimensional systems, where an extremely large number of points is needed.
The algorithms can be adapted with few modiยฎcations to the computation of the generalized dimensions h q , and they can also be useful in other applications involving the ecient computation of distances between points in a large set. More generally, the computational framework can be used in similar problems involving long-range interactions.