HeteroDoop

Abstract

The deluge of data has inspired big-data processing frameworks that span across large clusters. Frameworks for MapReduce, a state-of-the-art programming model, have primarily made use of the CPUs in distributed systems, leaving out computationally powerful accelerators such as GPUs. This paper presents HeteroDoop, a MapReduce framework that employs both CPUs and GPUs in a cluster. HeteroDoop offers the following novel features: (i) a small set of directives can be placed on an existing sequential, CPU-only program, expressing MapReduce semantics; (ii) an optimizing compiler translates the directive-augmented program into a GPU code; (iii) a runtime system assists the compiler in handling MapReduce semantics on the GPU; and (iv) a tail scheduling scheme minimizes job execution time in light of disparate processing capabilities of CPUs and GPUs. This paper addresses several challenges that need to be overcome in order to support these features. HeteroDoop is built on top of the state-of-the-art, CPU-only Hadoop MapReduce framework, inheriting its functionality. Evaluation results of HeteroDoop on recent hardware indicate that usage of even a single GPU per node can improve performance by up to 2.78x, with a geometric mean of 1.6x across our benchmarks, compared to a CPU-only Hadoop, running on a cluster with 20-core CPUs.