Hardware Accelerators in Data Centers / edited by Christoforos Kachris, Babak Falsafi, Dimitrios Soudris.

Format:

Book

Contributor:

Kachris, Christoforos, editor.

Falsafi, Babak, editor.

Soudris, Dimitrios, 1964- editor.

SpringerLink (Online service)

Series:

Engineering (Springer-11647)

Language:

English

Subjects (All):

Electronic circuits.

Microprocessors.

Signal processing.

Image processing.

Speech processing systems.

Circuits and Systems.

Processor Architectures.

Signal, Image and Speech Processing.

Local Subjects:

Circuits and Systems.

Processor Architectures.

Signal, Image and Speech Processing.

Physical Description:

1 online resource (IX, 279 pages) : 107 illustrations, 88 illustrations in color

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

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. Provides a single-source reference to the state of the art for hardware accelerators in data centers; Describes integrated frameworks for the seamless deployment of hardware accelerators; Includes several use-case scenarios of hardware accelerators for typical cloud computing applications, such as machine learning, graph computation, and databases.

Contents:

Introduction

Building the Infrastructure for Deploying FPGAs in the Cloud

dReDBox: A Disaggregated Architectural Perspective for Data Centers

The Green Computing Continuum: The OPERA Perspective

SPynq: Acceleration of Machine Learning Applications over Spark on Pynq

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

Towards Ubiquitous Low-power Image Processing Platforms

Energy-efficient Heterogeneous COmputing at exaSCALE - ECOSCALE

On Optimizing the Energy Consumption of Urban Data Centers.

Other Format:

Printed edition:

ISBN:

978-3-319-92792-3

9783319927923

OCLC:

1049568025

Access Restriction:

Restricted for use by site license.