  In studying the avalanches and noise in a model of hysteresis loops we have
developed two relatively straightforward algorithms which have allowed us to
study large systems efficiently. Our model is the random-field Ising model at
zero temperature, with deterministic albeit random dynamics. The first
algorithm, implemented using sorted lists, scales in computer time as O(N log
N), and asymptotically uses N (sizeof(double)+ sizeof(int)) bits of memory. The
second algorithm, which never generates the random fields, scales in time as
O(N \log N) and asymptotically needs storage of only one bit per spin, about 96
times less memory than the first algorithm. We present results for system sizes
of up to a billion spins, which can be run on a workstation with 128MB of RAM
in a few hours. We also show that important physical questions were resolved
only with the largest of these simulations.

Disordered Systems and Neural Networks;Materials Science;Statistical Mechanics