  We present a parallel algorithm for the undirected $s,t$-mincut problem with
floating-point valued weights. Our overarching algorithm uses an iteratively
reweighted least squares framework. This generates a sequence of Laplacian
linear systems, which we solve using parallel matrix algorithms. Our overall
implementation is up to 30-times faster than a serial solver when using 128
cores.
