Proceedings of the International Conference on Supercomputing

Despite the widespread adoption of heterogeneous clusters in modern data centers, modeling heterogeneity is still a big challenge, especially for large-scale MapReduce applications. In a CPU/GPU hybrid heterogeneous cluster, allocating more computing resources to a MapReduce application does not always mean better performance, since simultaneously running CPU and GPU tasks will contend for shared resources. This paper proposes a heterogeneity model to predict the shared resource contention between the simultaneously running tasks of a MapReduce application when heterogeneous computing resources (e.g. CPUs and GPUs) are allocated. To support the approach, we present a heterogeneous MapReduce framework, Hadoop+, which enables CPUs and GPUs to process big data coordinately, and leverages the heterogeneity model to assist users in selecting the computing resources for different purposes. Our experimental results show three benefits. First, Hadoop+ exploits GPU capability, and achieves 1.4x to 16.1x speedups over Hadoop for 5 real applications when running individually. Second, the heterogeneity model can be used to allocate GPUs among multiple simultaneously running MapReduce applications, bringing up to 36.9% (17.6% in average) speedup when multiple applications are running simultaneously. Third, the model is verified to be able to select the optimal or most cost-effective resource consumption