Parallel Algorithm and Dynamic Exponent for Diffusion-limited Aggregation

Physics – Condensed Matter – Statistical Mechanics

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

24 pages Revtex and 2 figures. A major improvement to the algorithm and smaller dynamic exponent in this version

Scientific paper

10.1103/PhysRevE.55.6211

A parallel algorithm for ``diffusion-limited aggregation'' (DLA) is described and analyzed from the perspective of computational complexity. The dynamic exponent z of the algorithm is defined with respect to the probabilistic parallel random-access machine (PRAM) model of parallel computation according to $T \sim L^{z}$, where L is the cluster size, T is the running time, and the algorithm uses a number of processors polynomial in L\@. It is argued that z=D-D_2/2, where D is the fractal dimension and D_2 is the second generalized dimension. Simulations of DLA are carried out to measure D_2 and to test scaling assumptions employed in the complexity analysis of the parallel algorithm. It is plausible that the parallel algorithm attains the minimum possible value of the dynamic exponent in which case z characterizes the intrinsic history dependence of DLA.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Parallel Algorithm and Dynamic Exponent for Diffusion-limited Aggregation does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Parallel Algorithm and Dynamic Exponent for Diffusion-limited Aggregation, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Parallel Algorithm and Dynamic Exponent for Diffusion-limited Aggregation will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-335678

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.