The VINEYARD approach

Versatile, integrated, accelerator-based, heterogeneous data centres

Conference Paper (2016)
Author(s)

Christoforos Kachris (Institute of Computer and Communications Systems (ICCS))

Dimitrios Soudris (Institute of Computer and Communications Systems (ICCS))

G. Gaydadjiev (Maxeler Technologies)

Huy-Nam Nguyen (Bull Systems)

Dimitrios S. Nikolopoulos (Queen's University Belfast)

Angelos Bilas (Foundation for Research and Technology - Hellas (FORTH))

Neil Morgan (Hartree Centre)

C. Strydis (NEURASMUS BV)

Christos Tsalidis (Neurocom Luxembourg)

John Balafas (ATHEX)

Ricardo Jimenez-Peris (LeanXcale)

Alexandre Almeida (Globaz)

Affiliation
External organisation
DOI related publication
https://doi.org/10.1007/978-3-319-30481-6_1
More Info
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Publication Year
2016
Language
English
Affiliation
External organisation
Volume number
9625
Pages (from-to)
3-13
ISBN (print)
9783319304809

Abstract

Emerging web applications like cloud computing, Big Data and social networks have created the need for powerful centres hosting hundreds of thousands of servers. Currently, the data centres are based on general purpose processors that provide high flexibility buts lack the energy efficiency of customized accelerators. VINEYARD aims to develop an integrated platform for energy-efficient data centres based on new servers with novel, coarse-grain and fine-grain, programmable hardware accelerators. It will, also, build a high-level programming framework for allowing end-users to seamlessly utilize these accelerators in heterogeneous computing systems by employing typical data-centre programming frameworks (e.g. MapReduce, Storm, Spark, etc.). This programming framework will, further, allow the hardware accelerators to be swapped in and out of the heterogeneous infrastructure so as to offer high flexibility and energy efficiency. VINEYARD will foster the expansion of the soft-IP core industry, currently limited in the embedded systems, to the data-centre market. VINEYARD plans to demonstrate the advantages of its approach in three real use-cases (a) a bio-informatics application for high-accuracy brain modeling, (b) two critical financial applications, and (c) a big-data analysis application.

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