Locality-Aware Scheduling for Scalable Heterogeneous Environments

The popularity of heterogeneous CPU-GPU processing has increased considerably in recent years. To efficiently utilize heterogeneous resources, data processing systems depend on an appropriate workload placement strategy to assign the right amount of compute to the right processor. However, finding an optimal placement strategy is not trivial due to various complex and conflicting tradeoffs related to the characteristics of processors, the nature of the workload, and data locality. In addition, placement decisions impact workload runtime and performance cost, and also depend on the availability of potentially different implementations for CPUs and GPUs, which adds extra complexity in such heterogeneous environments. In this tutorial, we review and compare state-of-the-art strategies for workload placement on heterogeneous CPU-GPU architectures, along with runtime prediction techniques and methods to support multi-device code. We also discuss open issues and identify potentially promising future research directions.

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Locality-Aware Scheduling for Scalable Heterogeneous Environments

Cited by 3 publications

References 25 publications

Addressing Load Imbalance in Bioinformatics and Biomedical Applications: Efficient Scheduling across Multiple GPUs

Addressing Load Imbalance in Bioinformatics and Biomedical Applications: Efficient Scheduling across Multiple GPUs

A Task Based Approach for Co-Scheduling Ensemble Workloads on Heterogeneous Nodes

Workload Placement on Heterogeneous CPU-GPU Systems

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