A Waterfall Model to Achieve Energy Efficient Tasks Mapping for Large Scale GPU Clusters

High energy consumption has become a critical problem for supercomputer systems. GPU clusters are becoming an increasingly popular architecture for building supercomputers because of its great improvement in performance. In this paper, we first formulate the tasks mapping problem as a mini-mal energy consumption problem with deadline constraint. Its optimizing object is very different from the traditional mapping problem which often aims at minimizing make span or minimizing response time. Then a Waterfall Energy Consumption Model, which abstracts the energy consumption of one GPU cluster system into several levels from high to low, is proposed to achieve an energy efficient tasks mapping for large scale GPU clusters. Based on our Waterfall Model, a new task mapping algorithm is developed which tries to apply different energy saving strategies to keep the system remaining at lower energy levels. Our mapping algorithm adopts the Dynamic Voltage Scaling, Dynamic Resource Scaling and $beta$-migration for GPU sub-task to significantly reduce the energy consumption and achieve a better load balance for GPU clusters. A task generator based on the real task traces is developed and the simulation results show that our mapping algorithm based on the Waterfall Model can reduce nearly 50% energy consumption compared with traditional approaches which can only run at a high energy level. Not only the task deadline can be satisfied, but also the task execution time of our mapping algorithm can be reduced.