Runtime Power Allocation Approach For Gamess Hybrid Cpu-Gpu Implementation

To improve power consumption of applications at the runtime, modern processors provide frequency scaling capabilities, which along with workload optimization, are also available on GPU accelerators. In this work, a runtime strategy is proposed to distribute a given power allocation among the host components and the GPU according to the current application performance and power usage, such that GPU execution is prioritized over CPU for power allocation to maximize application performance. Next, the strategy is tailored to an application, a quantum-chemistry package GAMESS for ab initio electronic structure calculations. Specifically, GAMESS hybrid CPU-GPU implementation as provided in the Libcchem library is considered. Experiments, performed on a 28-core node with a Kepler GPU, resulted in performance gains of up to 50% under the proposed strategy and the largest power allocation considered here as compared with the scenario when this allocation was equally distributed among the computing-platform components.