Advanced techniques for power, energy, and thermal management for clustered manycores 1st ed.

Author

• Pagani, Santiago

Additional Author(s)

• Chen, Jian-Jia
• Shafique, Muhammad
• Henkel, Jörg

Publisher

Cham, Switzerland : Springer International Publishing, 2018

Language

English

ISBN

9783319774794

Series

Subject(s)

• MICROELECTRONICS
• MICROPROCESSORS
• MULTIPROCESSORS

Notes

. .

Abstract

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).

Physical Dimension

Number of Page(s)

1 online resource (l, 250 p.)

Dimension

-

Other Desc.

ill. (in color.)

Summary / Review / Table of Content

Introduction --
Background and Related Work --
System Model --
Experimental Framework --
Thermal Safe Power (TSP) --
Transient and Peak Temperature Computation based on Matrix Exponentials (MatEx) --
Selective Boosting for Multicore Systems (seBoost) --
Energy and Peak Power Efficiency Analysis for Simple Approximation Schemes --
Energy-Efficient Task-to-core Assignment for Homogeneous Clustered Manycores --
Energy-Efficient Task-to-core Assignment for Heterogeneous Clustered Manycores --
Conclusions.

Exemplar(s)

#	Accession No.	Call Number	Location	Status
1.	01130/20	004.35 Pag A	Online !	Available