  Energy conservation of large data centers for high-performance computing
workloads, such as deep learning with big data, is of critical significance,
where cutting down a few percent of electricity translates into million-dollar
savings. This work studies energy conservation on emerging CPU-GPU hybrid
clusters through dynamic voltage and frequency scaling (DVFS). We aim at
minimizing the total energy consumption of processing a batch of offline tasks
or a sequence of real-time tasks under deadline constraints. We derive a fast
and accurate analytical model to compute the appropriate voltage/frequency
setting for each task and assign multiple tasks to the cluster with heuristic
scheduling algorithms. In particular, our model stresses the nonlinear
relationship between task execution time and processor speed for
GPU-accelerated applications, for more accurately capturing real-world GPU
energy consumption. In performance evaluation driven by real-world power
measurement traces, our scheduling algorithm shows comparable energy savings to
the theoretical upper bound. With a GPU scaling interval where analytically at
most 36% of energy can be saved, we record 33-35% of energy savings. Our
results are applicable to energy management on modern heterogeneous clusters.
