Advanced Techniques for Power, Energy, and Thermal Management for Clustered Manycores

Product Description

This book focuses on two of the most relevant problems related to power management on multicore and manycore systems. Specifically, one part of the book focuses on maximizing/optimizing computational performance under power or thermal constraints, while another part focuses on minimizing energy consumption under performance (or real-time) constraints.

From the Back Cover

This book focuses on two of the most relevant problems related to power management on multicore and manycore systems. Specifically, one part of the book focuses on maximizing/optimizing computational performance under power or thermal constraints, while another part focuses on minimizing energy consumption under performance (or real-time) constraints.

Provides a comprehensive introduction to energy, power, and temperature management, highlighting the different optimization goals, particularly computational performance, power and energy consumption, and temperature;
Highlights the differences and similarities between the two key challenges of performance optimization under power or thermal constraints and energy minimization under performance constraints;
Discusses in detail several means that can be used to optimize performance or energy while satisfying the desired constraints, including core heterogeneity, task-to-core assignment/mapping, dynamic power management (DPM), and dynamic voltage and frequency scaling (DVFS).

About the Author

Santiago Pagani is currently a Staff Firmware Engineer & Team Lead at ARM Ltd. (Cambridge, united Kingdom), where he runs an Agile firmware development team working on key components for the next generation Mali GPU products. He received his Diploma in Electronics Engineering from the National Technological University (UTN), Argentina, in 2010. He received his Ph.D. in Computer Science from the Karlsruhe Institute of Technology (KIT) with “Summa cum Laude” in 2016. From 2003 until 2012, he worked as a hardware and software developer in the industry sector for several companies in Argentina, including 2 years as a technical group leader. From 2012 until 2017, he worked as a research scientist (doctoral researcher and later post-doc) as part of the research staff at KIT. He received two Best Paper Awards (IEEE RTCSA in 2013 and IEEE/ACM CODES+ISSS in 2014), one Feature Paper of the Month (IEEE Transactions on Computers in 2017), and three HiPEAC Paper Awards. He received the 2017 ACM SIGBED “Paul Caspi Memorial Dissertation Award” in recognition of an outstanding Ph.D. dissertation. His interests include embedded systems, real-time systems, energy-efficient scheduling, temperature-aware scheduling, and power-aware designs.
Jian-Jia Chen is a Professor of Computer Science at TU Dortmund, Germany. Prior to his current position, he was with the Department of Informatics at Karlsruhe Institute of Technology (KIT) in Germany as a Junior Professor for Institute for Process Control and Robotics (IPR). He received his B.S. degree from the Department of Chemistry at National Taiwan University 2001. He obtained his Ph.D degree in June 2006 with Ph.D Dissertation “Energy-Efficient Scheduling for Real-Time Tasks in Uniprocessor and Homogeneous Multiprocessor Systems.” Between Jan. 2008 and April 2010, he was a postdoc researcher at Computer Engineering and Networks Laboratory (TIK) in Swiss Federal Institute of Technology (ETH) Zurich, Switzerland. Dr. Chen’s research interests include real-time systems, embedded systems, energy-efficient and power-aware designs, reliability system designs, and design automation.

He has published more than 175 research papers in prestigious journals and conferences in the areas of real-time embedded systems, lower-power system designs, and distributed computing. He has served as TPC member in several international conferences in real-time and embedded systems such as (RTSS, RTAS, RTCSA, DATE, ICCAD, etc.), and associate editors and guest editors in international journa