Kachris C., Falsafi B., Soudris D. Hardware Accelerators in Data Centers

Springer, 2019. — 280 p. — ISBN: 3319927914.This book provides readers with an overview of the architectures, programming frameworks, and hardware accelerators for typical cloud computing applications in data centers. The authors present the most recent and promising solutions, using hardware accelerators to provide high throughput, reduced latency and higher energy efficiency compared to current servers based on commodity processors. Readers will benefit from state-of-the-art information regarding application requirements in contemporary data centers, computational complexity of typical tasks in cloud computing, and a programming framework for the efficient utilization of the hardware accelerators.
In this book, we have collected the most promising and the most recent research activities in this emerging domain of heterogeneous computing that is based on the efficient utilization of hardware accelerators (i.e., FPGAs). The book contains 13 system-level architectures that show how to efficiently utilize hardware accelerators in the data centers to face emerging cloud applications.
This book provides readers with an overview of the architectures, programming frameworks, and hardware accelerators for typical cloud computing applications in data centers. The authors present the most recent and promising solutions, using hardware accelerators to provide high throughput, reduced latency and higher energy efficiency compared to current servers based on commodity processors. Readers will benefit from state-of-the-art information regarding application requirements in contemporary data centers, computational complexity of typical tasks in cloud computing, and a programming framework for the efficient utilization of the hardware accelerators.Building the Infrastructure for Deploying FPGAs in the Cloud
dReDBox: A Disaggregated Architectural Perspective for Data Centers
The Green Computing Continuum: The OPERA Perspective
Energy-Efficient Acceleration of Spark Machine Learning Applications on FPGAs
M2DC — A Novel Heterogeneous Hyperscale Microserver Platform
Towards an Energy-Aware Framework for Application Development and Execution in Heterogeneous Parallel Architectures
Enabling Virtualized Programmable Logic Resources at the Edge and the Cloud
Energy-Efficient Servers and Cloud
Developing Low-Power Image Processing Applications with the TULIPP Reference Platform Instance
Energy-Efficient Heterogeneous Computing at exaSCALE — ECOSCALE
On Optimizing the Energy Consumption of Urban Data Centers
Improving the Energy Efficiency by Exceeding the Conservative Operating Limits