Although performance is not the goal here, I wasn't happy that the cluster wasn't benching anywhere near what I expected with HPL. After some head scratching, I found out that the math library that I used to build the benchmark (libatlas) is built for soft-float in the RPi repos.
I rebuilt HPL with the libopenblas math library, added the head node into the pool, and hooked up my bench supply so I can use its current meter.
Now the terrible cluster does a max of 1.281 GFLOPS, and drew an average of 4.962W over the run. That means it's only 72 million times slower than the fastest computer on the June '17 Top500, and at an efficiency of .258 GFLOPS/W is 4.9 times more efficent than the least efficient computer in the same list.
(corrected with a slightly higher score after retesting 10/5/17)