Applied Reconfigurable Computing - 12th International Symposium, ARC 2016 Mangaratiba, RJ, Brazil, March 22-24, 2016 Proceedings
Applied Reconfigurable Computing - 12th International Symposium, ARC 2016 Mangaratiba, RJ, Brazil, March 22-24, 2016 Proceedings
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应用可重构计算 - 第 12 届国际研讨会，ARC 2016，巴西 RJ 曼加拉蒂巴，2016 年 3 月 22-24 日会议记录
DOI：
10.1007/978-3-319-30481-6_1
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
Kachris C
中科院分区：
文献类型：
--
作者：
Kachris C
云计算、大数据和社交网络等新兴的网络应用程序产生了对托管数十万台服务器的强大中心的需求。目前，数据中心基于通用处理器，提供高灵活性，但缺乏定制加速器的能源效率。VINEYARD旨在开发一个基于新服务器的节能数据中心集成平台，该服务器具有新颖的粗粒度和细粒度可编程硬件加速器。它还将建立一个高级编程框架，使最终用户能够通过采用典型的数据中心编程框架（例如MapReduce，Storm，Spark等）在异构计算系统中无缝利用这些加速器。此外，该编程框架将允许硬件加速器在异构基础设施中交换，以提供高灵活性和能源效率。VINEYARD将促进目前仅限于嵌入式系统的软IP核心行业向数据中心市场的扩展。VINEYARD计划在三个真实的用例中展示其方法的优势：（a）用于高精度大脑建模的生物信息学应用，（B）两个关键的金融应用，以及（c）大数据分析应用。
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.