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Hardware accelerators in data centers

Falsafi, Babak.

Hardware accelerators in data centers

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Hardware accelerators in data centersedited by Christoforos Kachris, Babak Falsafi, Dimitrios Soudris.
其他作者:	Kachris, Christoforos.
出版者:	Cham :Springer International Publishing :2019.
面頁冊數:	ix, 279 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Computer architecture.
電子資源:	http://dx.doi.org/10.1007/978-3-319-92792-3
ISBN:	9783319927923$q(electronic bk.)

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$q(electronic bk.)

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

183006
Computer architecture.

LC Class. No.: QA76.9.A73

Dewey Class. No.: 004.35

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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.
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