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Energy conservation of large data centers for high-performance computingworkloads, such as deep learning with big data, is of critical significance,where cutting down a few percent of electricity translates into million-dollarsavings. This work studies energy conservation on emerging CPU-GPU hybridclusters through dynamic voltage and frequency scaling (DVFS). We aim atminimizing the total energy consumption of processing a batch of offline tasksor a sequence of real-time tasks under deadline constraints. We derive a fastand accurate analytical model to compute the appropriate voltage/frequencysetting for each task and assign multiple tasks to the cluster with heuristicscheduling algorithms. In particular, our model stresses the nonlinearrelationship between task execution time and processor speed forGPU-accelerated applications, for more accurately capturing real-world GPUenergy consumption. In performance evaluation driven by real-world powermeasurement traces, our scheduling algorithm shows comparable energy savings tothe theoretical upper bound. With a GPU scaling interval where analytically atmost 36% of energy can be saved, we record 33-35% of energy savings. Ourresults are applicable to energy management on modern heterogeneous clusters.

Energy-aware Task Scheduling with Deadline Constraint in DVFS-enabled Heterogeneous Clusters