Accelerate MapReduce on GPUs with multi-level reduction

Abstract

With Graphics Processing Units (GPUs) becoming more and more popular in general purpose computing, more attentions have been paid on building a framework to provide convenient interfaces for GPU programming. MapReduce can greatly simplify the programming for data-parallel applications in cloud computing environment, and it is also naturally suitable for GPUs. However, there are some problems in recent reduction-based MapReduce implementation on GPUs. Its performance is dramatically degraded when handling massive distinct keys because the massive data cannot be stored in tiny shared memory entirely. A new MapReduce framework on GPUs, called Jupiter, is proposed with continuous reduction structure. Two improvements are supported in Jupiter, a multi-level reduction scheme tailored for GPU memory hierarchy and a frequency-based cache policy on key-value pairs in shared memory. Shared memories are utilized efficiently for various data-parallel applications whether involving little or abundant distinct keys. Experiments show that Jupiter can achieve up to 3x speedup over the original reduction-based GPU MapReduce framework on the applications with lots of distinct keys.