异构大数据编程环境Hadoop+

Hadoop+: A Big-data Programming Framework for Heterogeneous Computing Environments

互联网和物联网技术的飞速发展开启了＂大数据＂时代。目前,硬件的高速发展催生了许多异构芯片,它们越来越多地出现在大规模数据中心里,支持不同的应用程序,在提升性能的同时降低整体功耗。文章重点介绍了基于Map Reduce编程模型的Hadoop＋框架的设计与实现,它允许用户在单个任务中调用CUDA/Open CL的并行实现,并能通过异构任务模型帮助用户。在我们的实验平台上,五种常见机器学习算法使用Hadoop＋框架相对于Hadoop能达到1.4×~16.1×的加速比,在Hadoop＋框架中使用异构任务模型指导其资源分配策略,对单个应用负载上最高达到36.0%的性能提升;对多应用的混合负载,最多能减少36.9%,平均17.6%的应用执行时间。

The rapid development of Internet and Internet of Things opens the era of big data. Currently, heterogeneous architectures are being widely adopted in large-scale datacenters, for the sake of performance improvement and reduction of energy consumption. This paper presents the design and implementation of Hadoop＋, a programming framework that implements Map Reduce and enables invocation of parallelized CUDA/Open CL within a map/reduce task, and helps the user by taking advantage of a heterogeneous task model. Experimental result shows that Hadoop＋ attains 1.4× to 16.1× speedups over Hadoop for five commonly used machine learning algorithms. Coupled with a heterogeneous task model that helps allocate computing resouces, Hadoop＋ brings a 36.0% improvement in data processing speed for single-application workloads, and for mixed workloads of multiple applications, the execution time is reduced by up to 36.9% with an average 17.6%.