A Research of MapReduce with GPU Acceleration

MapReduce is an efficient distributed computing model on large data sets. The data processing is fully distributed on huge amount of nodes, and a MapReduce cluster is of highly scalable. However, single-node performance is gradually to be a bottleneck in computeintensive jobs, which makes it difficult to extend the MapReduce model to wider application fields such as largescale image processing and image mining. As an attempt, this paper presents an approach of GPU-accelerated MapReduce, which is implemented by Hadoop and OpenCL. Being a distinctive feature, it aims at general and inexpensive hardware platform, and it is seamlessly integrated with Apache Hadoop, the most widely used MapReduce framework. As a heterogeneous multi-machine and many-core architecture, it targets at both dataand compute-intensive applications. An almost 2 times performance improvement has been validated, without any special optimization.