Evaluation of power management strategies on actual multiprocessor platforms

136 Pages·2016·2.64 MB·English

by  Jabran Khan Jadoon

Evaluation of power management strategies on actual
multiprocessor platforms
Jabran Khan Jadoon
To cite this version:
Jabran Khan Jadoon. Evaluation of power management strategies on actual multiprocessor plat-
forms. Hardware Architecture [cs.AR]. Université Nice Sophia Antipolis, 2013. English. ￿NNT:
2013NICE4012￿. ￿tel-00838799￿
HAL Id: tel-00838799
https://theses.hal.science/tel-00838799
Submitted on 26 Jun 2013
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UNIVERSITÉ DE NICE – SOPHIA ANTIPOLIS

ÉCOLE DOCTORALE STIC
SCIENCES ET TECHNOLOGIES DE L’INFORMATION ET DE LA
COMMUNICATION
THÈSE
pour obtenir le titre de
Docteur en Sciences
de l’Université de Nice – Sophia Antipolis
Mention : Électronique
Présentée et soutenue par
Jabran KHAN JADOON
Evaluation of Power Management Strategies
on Actual Multiprocessor Platforms

Thèse dirigée par Cécile BELLEUDY
Laboratoire LEAT, Université de Nice - Sophia Antipolis - CNRS
Soutenue en 25 Mars 2013
Jury:
Nathalie JULIEN Professeur, Université de Bretagne-Sud Rapporteurs
François PECHEUX Maître de Conférence, Université Pierre et Marie Curie
Smail NIAR Professeur, Université de Valenciennes et Hainaut-Cambrésis Examinateurs
Michel Auguin Directeur de Recherche CNRS
Cécile BELLEUDY Maître de Conférence, Université de Nice-Sophia Antipolis
Sébastien BILAVARN Maître de Conférence, Université de Nice-Sophia Antipolis
@khan
ALL RIGHTS RESERVED
UNIVERSITÉ DE NICE – SOPHIA ANTIPOLIS

ÉCOLE DOCTORALE STIC
SCIENCES ET TECHNOLOGIES DE L’INFORMATION ET DE LA
COMMUNICATION
THÈSE
pour obtenir le titre de
Docteur en Sciences
de l’Université de Nice – Sophia Antipolis
Mention : Électronique
Présentée et soutenue par
Jabran KHAN JADOON
Evaluation of Power Management Strategies
on Actual Multiprocessor Platforms

Thèse dirigée par Cécile BELLEUDY
Laboratoire LEAT, Université de Nice - Sophia Antipolis - CNRS
Soutenue en 25 Mars 2013
Jury:
Nathalie JULIEN Professeur, Université de Bretagne-Sud Rapporteurs
François PECHEUX Maître de Conférence, Université Pierre et Marie Curie
Smail NIAR Professeur, Université de Valenciennes et Hainaut-Cambrésis Examinateurs
Michel Auguin Directeur de Recherche CNRS
Cécile BELLEUDY Maître de Conférence, Université de Nice-Sophia Antipolis
Sébastien BILAVARN Maître de Conférence, Université de Nice-Sophia Antipolis

ABSTRACT

The purpose of this study is to investigate how power management strategies can be
efficiently exploited in actual platforms. Primarily, the challenges in multicore based
embedded systems lies in managing the energy expenditure, determining the scheduling
behavior and establishing methods to monitor power and energy, so as to meet the
demands of the battery life and load requirements. The work presented in this
dissertation is a study of low power-aware strategies in the practical world for single and
multiprocessor platforms. The approach used for this study is based on representative
multiprocessor platforms (real or virtual) to identify the most influential parameters, at
hardware as well as application level, unlike many existing works in which these
parameters are often underestimated or sometimes even ignored. The work analyzes and
compares in detail various experimentations with different power policies based on
Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Switching (DPS)
techniques, and investigates the conditions at which these policies are effective in terms
of energy savings.
The results of these investigations reveal many interesting and notable conclusions that
can serve as prerequisites for the efficient use of power management strategies. This
work also shows the potential of advanced domain specific power strategies compared to
real world available strategies that are general purpose based in their majority. Finally,
some high level consumption models are derived from the different energy measurement
results to let the estimation of power management benefits at early stages of a system
development.

Dedicated to my beloved mother, father, brothers and sisters
and
to my beloved wife Mahrukh.

ACKNOWLEDGEMENTS
One of the joys of completion is to look over the journey past and remember all the
friends and family who have helped and supported me along this long but fulfilling road.
Completion of my PhD required countless selfless acts of support, generosity, and time
by people in my personal and academic life. I can only attempt to humbly acknowledge
and thank the people and institutions that have given me so much help throughout my
PhD career and made this dissertation possible. I am thankful to the Higher Education
Commission (HEC) of Pakistan for providing funding throughout my Masters and PhD
career.
I am sincerely thankful to Miss. Cécile Belleudy, my advisor, for being a constant source
of invaluable encouragement, aid, and expertise during my years at LEAT. I would also
like to express my heartfelt gratitude to my co-supervisor Sébastien Bilavarn. He has
provided insightful discussions about the research. I am very grateful for his scientific
advice and knowledge and many insightful discussions and suggestions. The mentoring,
friendship, and collegiality of both Cécile Belleudy and Sébastien Bilavarn enriched my
academic life and have left a profound impression on how academic research and
collaboration should ideally be conducted.
I would also like to thank Professor Nathalie Julien and François Pecheux for being my
reviewers and thoroughly reading and acknowledging my thesis. In addition, I would
like to thank Professor Michel Auguin and Professor Smail Niar to be part of my jury. It
is no easy task, reviewing a thesis, and I am grateful for their detailed reviewing. To the
many anonymous reviewers at the various international conferences, thank you for
helping to shape and guide the direction of the work with your careful and instructive
comments.
LEAT has provided a rich and fertile environment to study and explore new ideas. At
LEAT, I would first like to thank Micheal Auguin who has been extremely supportive in
allowing me to participate in lab activities whilst pursuing my PhD studies. I am grateful
for the chance to be a part of the lab and thank for welcoming me as a friend and helping
to develop the ideas in this thesis. Other colleagues who I owe gratitude for their support
of my research or major PhD milestones include: Alain Pegatoquet, Bassem Ouni, Ons
Mbarek, Khurram Bhatti, and Zeeshan Khan.