  We describe an asynchronous parallel stochastic coordinate descent algorithm
for minimizing smooth unconstrained or separably constrained functions. The
method achieves a linear convergence rate on functions that satisfy an
essential strong convexity property and a sublinear rate ($1/K$) on general
convex functions. Near-linear speedup on a multicore system can be expected if
the number of processors is $O(n^{1/2})$ in unconstrained optimization and
$O(n^{1/4})$ in the separable-constrained case, where $n$ is the number of
variables. We describe results from implementation on 40-core processors.
