You are right that it's interesting to compare Futhark-on-GPU with X-on-GPU for various values of X. In most of our academic work, that is what we do. However

You are right that it's interesting to compare Futhark-on-GPU with X-on-GPU for various values of X. In most of our academic work, that is what we do. However, in practice, X-on-GPU where X is not Futhark is rare, because GPU programming is notoriously difficult and time-consuming. Futhark's purpose is to make high-performance data-parallel programming more accessible, even if you could potentially write a faster program yourself. There are no empirical measurements that I know of, but I would not be surprised if it is a hundred times faster to write a Futhark program than the corresponding OpenCL program. CUDA fares a little better, but not by much. So even if your hand-written program might be twice as fast as Futhark, do you really have the time to write it in the first place? And if you later want to make a small change to its logic (say, adding another parallel loop on top), you may need to rework all of your optimisations from scratch.