Quantifying the energy cost of data movement for emerging smart phone workloads on mobile platforms

摘要

In portable computing systems like smartphones, energy is generally a key but limited resource where application cores have been proven to consume a significant part of it. To understand the characteristics of the energy consumption, in this paper, we focus our attention on the portion of energy that is spent to move data to the application core's internal registers from the memory system. The primary motivation for this focus comes from the relatively higher energy cost associated with a data movement instruction compared to that of an arithmetic instruction. Another important factor is the distributive computing nature among different units in a SoC which leads to a higher data movement to/from the application cores. We perform a detailed investigation to quantify the impact of data movement on overall energy consumption of a popular, commercially-available smart phone device. To aid this study, we design micro-benchmarks that generate desired data movement patterns between different levels of the memory hierarchy and measure the instantaneous power consumed by the device when running these micro-benchmarks. We extensively make use of hardware performance counters to validate the micro-benchmarks and to characterize the energy consumed in moving data. We take a step further to utilize this calculated energy cost of data movement to characterize the portion of energy that an application spends in moving data for a wide range of popular smart phone workloads. We find that a considerable amount of total device energy is spent in data movement (an average of 35% of the total device energy). Our results also indicate a relatively high stalled cycle energy consumption (an average of 23.5%) for current smart phones. To our knowledge, this is the first study that quantifies the amount of data movement energy for emerging smart phone applications running on a recent, commercial smart phone device. We hope this characterization study and the insights developed in the pape- can inspire innovative designs in smart phone architectures with improved performance and energy efficiency.