Distributed computing using PCs volunteered by the public can provide high computing capacity at low cost. However, computational results from volunteered PCs have a non-negligible error rate, so result validation is needed to ensure overall correctness. A generally applicable technique is "redundant computing", in which each computation is done on several separate computers, and results are accepted only if there is a consensus. Variations in numerical processing between computers (due to a variety of hardware and software factors) can lead to different results for the same task. In some cases, this can be addressed by doing a "fuzzy comparison" of results, so that two results are considered equivalent if they agree within given tolerances. However, this approach is not applicable to applications that are "divergent", that is, for which small numerical differences can produce large differences in the results. In this paper we examine the problem of validating results of divergent applications. We present a novel approach called Homogeneous Redundancy (HR), in which the redundant instances of a computation are dispatched to numerically identical computers, allowing strict equality comparison of the results. HR has been deployed in Predictor@home, a world-wide community effort to predict protein structure from sequence.