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Record Nr. |
UNINA9910337466403321 |
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Titolo |
Hardware Accelerators in Data Centers / / edited by Christoforos Kachris, Babak Falsafi, Dimitrios Soudris |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
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ISBN |
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Edizione |
[1st ed. 2019.] |
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Descrizione fisica |
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1 online resource (280 pages) : illustrations |
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Disciplina |
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Soggetti |
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Electronic circuits |
Microprocessors |
Signal processing |
Image processing |
Speech processing systems |
Circuits and Systems |
Processor Architectures |
Signal, Image and Speech Processing |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references and index. |
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Nota di contenuto |
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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. |
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Sommario/riassunto |
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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 |
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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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