This book provides a comprehensive guide to the design of sustainable and green computing systems (GSC). Coverage includes important breakthroughs in various aspects of GSC, including multi-core architectures, interconnection technology, data centers, high performance computing (HPC), and sensor networks. The authors address the challenges of power efficiency and sustainability in various contexts, including system design, computer architecture, programming languages, compilers and networking.

This book provides a comprehensive guide to the design of sustainable and green computing systems (GSC). Coverage includes important breakthroughs in various aspects of GSC, including multi-core architectures, interconnection technology, data centers, high performance computing (HPC), and sensor networks. The authors address the challenges of power efficiency and sustainability in various contexts, including system design, computer architecture, programming languages, compilers and networking.

Graph representations are pervasive in scientific and social computing. They serve as vital tools to model the interplay between different interacting entities. This monograph delves into the problem of community detection, which is one of the most widely used graph operations toward scientific discovery. Community detection refers to the process of identifying tightly-knit subgroups of vertices in a large graph. These sub-groups (or communities) represent vertices that are tied together through common structure or function. Identification of communities could help in understanding the modular organization of complex networks. However, owing to large data sizes and high computational costs, performing community detection at scale has become increasingly challenging. This monograph presents a detailed review and analysis of some of the leading computational methods and implementations developed for executing community detection on modern day multicore and manycore architectures. The intention is to: a) define the problem of community detection and highlight its scientific significance; b) relate to challenges in parallelizing the operation on modern day architectures; c) provide a detailed report and logical organization of the approaches that have been designed for various architectures; and d) provide insights into the strengths and suitability of different architectures for community detection, and a preview into the future trends of the area. While the focus is on community detection, the challenges, and techniques to overcome the challenges, transcend to several other graph problems that have applications in science and data analytics.

Sustainable Wireless Network-on-Chip Architectures focuses on developing novel Dynamic Thermal Management (DTM) and Dynamic Voltage and Frequency Scaling (DVFS) algorithms that exploit the advantages inherent in WiNoC architectures. The methodologies proposed-combined with extensive experimental validation-collectively represent efforts to create a sustainable NoC architecture for future many-core chips. Current research trends show a necessary paradigm shift towards green and sustainable computing. As implementing massively parallel energy-efficient CPUs and reducing resource consumption become standard, and their speed and power continuously increase, energy issues become a significant concern. The need for promoting research in sustainable computing is imperative. As hundreds of cores are integrated in a single chip, designing effective packages for dissipating maximum heat is infeasible. Moreover, technology scaling is pushing the limits of affordable cooling, thereby requiring suitable design techniques to reduce peak temperatures. Addressing thermal concerns at different design stages is critical to the success of future generation systems. DTM and DVFS appear as solutions to avoid high spatial and temporal temperature variations among NoC components, and thereby mitigate local network hotspots.