Over at Georgia Tech’s HPC Garage, Rich Vuduc writes that a new technical report presents a thought-experiment on the question of whether engineering an algorithm to optimize time differs from doing so with respect to energy Joules.
Our goal is to explain—in simple, analytic terms accessible to algorithm designers and performance tuners—how the time, energy, and power to execute an algorithm relate. The model considers an algorithm in terms of operations, concurrency, and memory traffic; and a machine in terms of the time and energy costs per operation or per word of communica- tion. We confirm the basic form of the model experimentally. From this model, we suggest under what conditions we ought to expect an algorithmic time-energy trade-off, and show how algorithm properties may help inform power management.