  This paper presents, implements, and evaluates a power-regulation technique
for multicore processors, based on an integral controller with adjustable gain.
The gain is designed for wide stability margins, and computed in real time as
part of the control law. The tracking performance of the control system is
robust with respect to modeling uncertainties and computational errors in the
loop. The main challenge of designing such a controller is that the power
dissipation of program-workloads varies widely and often cannot be measured
accurately; hence extant controllers are either ad hoc or based on a-priori
modeling characterizations of the processor and workloads. Our approach is
different. Leveraging the aforementioned robustness it uses a simple textbook
modeling framework, and adjusts its parameters in real time by a
system-identification module. In this it trades modeling precision for fast
computations in the loop making it suitable for on-line implementation in
commodity data-center processors. Consequently, the proposed controller is
agnostic in the sense that it does not require any a-priori system
characterizations. We present an implementation of the controller on Intel's
fourth-generation microarchitecture, Haswell, and test it on a number of
industry benchmark programs which are used in scientific computing and
datacenter applications. Results of these experiments are presented in detail
exposing the practical challenges of implementing provably-convergent power
regulation solutions in commodity multicore processors.
