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- W4247484589 abstract "We present a parallel algorithm for computing the correlation dimension (D2) from a time series generated by a dynamic system, using the method of correlation integrals, which essentially requires the computation of distances among a set of points in the state space. The parallelization is suitable for coarse-grained multiprocessor systems with distributed memory and is carried out using a virtually shared memory model. The algorithm simultaneously gives all the correlation integrals at various state space dimensions needed to estimate the D2. Two versions are discussed: the first computes all distances between points; the second computes only distances less than a fixed ϵ, and employs a box-assisted approach and linked lists for an efficient search of neighbouring points. The algorithms, coded in Fortran 77, are tested on a heterogeneous network of workstations consisting of various DEC Alphas of different powers, interconnected by Ethernet; the Network Linda parallel environment is used. A detailed analysis of performance is carried out using the generalization of speed-up and efficiency for heterogeneous systems. The algorithms are fully asynchronous and so intrinsically balanced. In almost all the situations they provide a unitary efficiency. The second version greatly reduces the computational work, thus making it possible to tackle D2 estimation even for medium and high-dimensional systems, where an extremely large number of points is involved. The algorithms can also be employed in other applicative contexts requiring the efficient computation of distances among a large set of points. The method proposed for the analysis of performance can be applied to similar problems. © 1998 John Wiley & Sons, Ltd." @default.
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- W4247484589 date "1998-08-25" @default.
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- W4247484589 title "Computing the correlation dimension on a network of workstations" @default.
- W4247484589 doi "https://doi.org/10.1002/(sici)1096-9128(19980825)10:10<737::aid-cpe371>3.3.co;2-8" @default.
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