High level programming framework for FPGAs in the data center
High level programming framework for FPGAs in the data center
复制标题
数据中心 FPGA 高级编程框架
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
M. Wright
中科院分区：
文献类型：
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作者：
Oren Segal;M. Margala;S. R. Chalamalasetti;M. Wright
异构计算为数据中心的节能计算提供了一种很有前途的解决方案。基于FPGA的异构计算是一个特别有前途的方向，因为它允许为以数据为中心的并行应用创建定制硬件解决方案。延迟FPGA作为主流高性能计算设备的广泛采用的主要问题之一是对它们进行编程的困难。OpenCL旨在解决与异构设备编程相关的困难和不一致性，不幸的是，由于其复杂性，它为许多软件程序员设置了很高的标准，使他们无法直接受益于OpenCL和异构计算所提供的计算能力和能源效率。这项工作提出了一个努力，以弥补差距，通过扩展现有的Java编程框架（APARAPI），基于OpenCL，使它可以用来编程FPGA在一个高层次的抽象和增加的可编程性。我们运行几个真实的世界的算法来评估的APARAPI框架的性能在低端和高端系统。在低端和高端系统上，我们分别发现运行NBody模拟时功耗降低了78- 80%，速度提高了4.8X-5.3X，而K-Means MapReduce算法运行在Hadoop框架和APARAPI之上，功耗降低了65- 80%，速度提高了6.2X-7 X。
Heterogeneous computing offers a promising solution for energy efficient computing in the data center. FPGA based heterogeneous computing is an especially promising direction since it allows for the creation of custom hardware solutions for data centric parallel applications. One of the main issues delaying wide spread adoption of FPGAs as main stream high performance computing devices is the difficulty in programming them. OpenCL was meant to address the difficulties and the non-uniformity related to programming heterogeneous devices, unfortunately because of its complexity it sets the bar high for many software programmers, preventing them from directly benefiting from the computing power and energy efficiency that OpenCL and heterogeneous computing have to offer. This work presents an effort to bridge the gap by extending an existing Java programming framework (APARAPI), based on OpenCL, so that it can be used to program FPGAs at a high level of abstraction and increased ease of programmability. We run several real world algorithms to assess the performance of the APARAPI framework on both a low end and a high end system. On the low end and high and systems respectively we find up to 78-80 percent power reduction and 4.8X-5.3X speed increase running NBody simulation, as well as up to 65-80 percent power reduction and 6.2X-7X speed increase for a K-Means MapReduce algorithm running on top of the Hadoop framework and APARAPI.
影响因子：
4.6
作者：
J. Xu
通讯作者：
J. Xu