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Reflective On-Chip Resource Management Policies for Energy-Efficient Heterogeneous Multiprocessors

May 17, 2018 @ 3:00 pm - 4:00 pm PDT

Name: Tiago Mück
Date: May 16, 2018

Time: 2:00pm

Location: Donald Bren Hall 2011

Committee: Nikil Dutt (Chair), Alex Nicolau, Tony Givargis


Effective exploitation of power-performance tradeoffs in heterogeneous many-core platforms (HMPs), requires intelligent on-chip resource management at different layers, in particular at the operating system level. Operating systems need to continuously analyze the application behavior and find a proper answer for questions such as: What is the most power efficient core type to execute the application without violating its performance requirements? or Which option is more power-efficient for the current application: an out-of-order core at a lower frequency or an inorder core at a higher frequency?
Unfortunately, existing operating systems (e.g. Linux) do not offer mechanisms to properly address these questions and therefore are unable to fully exploit architectural heterogeneity for scalable energy-efficient execution of dynamic workloads.
This dissertation proposes a holistic approach for performing resource allocation decisions and power management by leveraging concepts from reflective software.
The general idea of reflection is to change your actions based on both external feedback and introspection (i.e., self-assessment).
From a practical computer system perspective, reflection means performing resource management actions considering both sensing information (e.g., readings from performance counters, power sensors, etc.) to assess the current system state, as well as models to predict the behavior of the system before performing an action.
In this context, this dissertation describes MARS, a Middleware for Adaptive Reflective computer Systems. MARS consists of a framework and a set of models for creating reflective resource managers. MARS is implemented and evaluated on top a real Linux-based platform. Furthermore, MARS also provides an offline simulation infrastructure for fast prototyping of policies and large-scale or long-term policy evaluation.
Experimental evaluation shows that MARS’s models allow different policies for task mapping and dynamic voltage scaling to be seamlessly integrated, resulting in up-to 1.8x energy efficiency improvements without performance degradation when compared to vanilla Linux.


May 17, 2018
3:00 pm - 4:00 pm PDT
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