In multicore processor systems, being able to accurately predict the future provides new
optimization opportunities, which otherwise could not be exploited. For example, an oracle …

Due to the high integration density and roadblock of voltage scaling, modern multicore
processors experience higher power densities than previous technology scaling nodes …

A machine learning approach to multicore resource management produces self-optimizing
on-chip hardware agents capable of learning, planning, and continuously adapting to …

This paper presents a supervised learning based power management framework for a multi-
processor system, where a power manager (PM) learns to predict the system performance …

Muiticomputers have the potential to deliver Gigaflop performance on many scientific
applications. Initial implementations of parallel programs on these machines, however, are …

Existing power management techniques operate by reducing performance capacity
(frequency, voltage, size) when performance demand is low. In the case of multicore …

Future microprocessors may become so power constrained that not all transistors will be
able to be powered on at once. These systems will be required to nimbly adapt to changes …

The objectives of this paper are (1) to develop a frequency planning methodology that
maximizes the total performance of multi-core processors and that limits their maximum …

Multi-core and many-core were already major trends for the past six years, and are expected
to continue for the next decades. With these trends of parallel computing, it becomes …

Due to fundamental physical limitations and power constraints, we are witnessing a radical
change in commodity microprocessor architectures to multicore designs. Continued …