Heterogeneous systems for energy efficient scientific computing
ARC'12 Proceedings of the 8th international conference on Reconfigurable Computing: architectures, tools and applications
Workload and power budget partitioning for single-chip heterogeneous processors
Proceedings of the 21st international conference on Parallel architectures and compilation techniques
Load balancing in a changing world: dealing with heterogeneity and performance variability
Proceedings of the ACM International Conference on Computing Frontiers
Optimization power consumption model of reliability-aware GPU clusters
The Journal of Supercomputing
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As the system scales up continuously, the problem of power consumption for high performance computing (HPC) system becomes more severe. Heterogeneous system integrating two or more kinds of processors, could be better adapted to heterogeneity in applications and provide much higher energy efficiency in theory. Many studies have shown heterogeneous system is preferable on energy consumption to homogeneous system in a multi-programmed computing environment. However, how to exploit energy efficiency (Flops/Watt) of heterogeneous system for a single application or even for a single phase in an application has not been well studied. This paper proposes a power-efficient work distribution method for single application on a CPU-GPU heterogeneous system. The proposed method could coordinate inter-processor work distribution and per-processor’s frequency scaling to minimize energy consumption under a given scheduling length constraint. We conduct our experiment on a real system, which equips with a multi-core CPU and a multi-threaded GPU. Experimental results show that, with reasonably distributing work over CPU and GPU, the method achieves 14% reduction in energy consumption than static mappings for several typical benchmarks. We also demonstrate that our method could adapt to changes in scheduling length constraint and hardware configurations.