> I don't think that there are any algorithms that could operate within a generic benchmark that could have random elements in them _and_ product a useful resul

> I don't think that there are any algorithms that could operate within a generic benchmark that could have random elements in them _and_ product a useful result. One that springs to mind is Monte-Carlo sampled raytracing. An individual ray might take more or less time to compute, but the time to compute 10 million rays will be statistically be roughly constant. You could even imagine averaging a bunch of renders of the same scene from different machines to get a lower-noise result, thereby demonstrating a benchmark combined with useful work. Statistical predictability is the key. (Confession - this isn't exactly theoretical. I sometimes have occasion to render light fields, and I shard the work over as many random workstations as I can get my hands on. It's always obvious which workstations are faster than others, even without making any special attempt to balance the workload. I think this is actually a workable concept.)