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Hardware accelerators in data centers

Falsafi, Babak.

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Hardware accelerators in data centers

Record Type:	Electronic resources : Monograph/item
Title/Author:	Hardware accelerators in data centers/ edited by Christoforos Kachris, Babak Falsafi, Dimitrios Soudris.
other author:	Falsafi, Babak.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	ix, 279 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Computer architecture. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-92792-3
ISBN:	9783319927923

Hardware accelerators in data centers
Hardware accelerators in data centers[electronic resource] /edited by Christoforos Kachris, Babak Falsafi, Dimitrios Soudris. - Cham :Springer International Publishing :2019. - ix, 279 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9783319927923

Standard No.: 10.1007/978-3-319-92792-3doiSubjects--Topical Terms:

559837
Computer architecture.

LC Class. No.: QA76.9.A73

Dewey Class. No.: 004.35

Hardware accelerators in data centers
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245 0 0 $a Hardware accelerators in data centers $h [electronic resource] / $c edited by Christoforos Kachris, Babak Falsafi, Dimitrios Soudris.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a ix, 279 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 0 $a Computer architecture. $3 559837
650 0 $a High performance computing. $3 591827
650 1 4 $a Engineering. $3 586835
650 2 4 $a Circuits and Systems. $3 896527
650 2 4 $a Processor Architectures. $3 892680
650 2 4 $a Signal, Image and Speech Processing. $3 891073
700 1 $a Falsafi, Babak. $3 880029
700 1 $a Kachris, Christoforos. $3 3379458
700 1 $a Soudris, Dimitrios. $3 1071506
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856 4 0 $u http://dx.doi.org/10.1007/978-3-319-92792-3
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