  The objective of the paper is to establish a computable upper bound for the
almost sure convergence rate for a class of ratio consensus algorithms defined
via column-stochastic matrices. Our result extends the works of Iutzeler et al.
(2013) on similar bounds that have been obtained in a more restrictive setup
with limited conclusions. The present paper complements the results of
Gerencs\'er and Gerencs\'er (2021), identifying the exact almost sure
convergence rate of a wide class of ratio consensus algorithms in terms of a
spectral gap, which is, however, not computable in general. The upper bound
provided in the paper will be compared to the actual rate of almost sure
convergence experimentally on a range of modulated random geographic graphs
with random local interactions.
