Optimal Energy Management Strategy for Parallel Scheduling
Xin Li Li · Applied Mechanics and Materials · 2014
Large-scale data streams processing is now fundamental to many data processing applications. There is growing focus on manipulating Large-sca le data streams on GPUs in order to improve the data throughput. Hence, there is a need to investigate the parallel scheduling strategy at the task level for the Large-scale data stream s processing, and to support them efficiently. We propose two different parallel scheduling strategies to handle massive data stream s in real time. Additionally, massive data stream s processing on GPUs is energy-consumed computation task. So we consider the power efficiency as an important factor to the parallel strategies. We present an approximation method to quantify the power efficiency for massive data streams during the computing phase. Finally, we test and compare the two parallel scheduling strategies on a large quantity of synthetic and real stream datas. The simulation experiments and compuatation results in practice both prove the accuracy of analysis on performance and power efficiency.