MultiAmdahl: Optimal Resource Allocation in Heterogeneous Architectures

Abstract

Future multiprocessor chips will integrate many different units, each tailored to a specific computation. When designing such a system, the chip architect must decide how to distribute limited system resources such as area, power, and energy among the computational units. We extend MultiAmdahl, an analytical optimization technique for resource allocation in heterogeneous architectures, for energy optimality under a variety of constant system power scenarios. We conclude that reduction in constant system power should be met by reallocating resources from general-purpose computing to heterogeneous accelerator-dominated computing, to keep the overall energy consumption at a minimum. We extend this conclusion to offer an intuition regarding energy-optimal resource allocation in data center computing.