面向穿戴應(yīng)用的大小核架構(gòu)低功耗策略研究
發(fā)布時間：2018-11-16 20:56
【摘要】：隨著技術(shù)的革新和穿戴設(shè)備的發(fā)展,目前智能穿戴設(shè)備逐漸被廣泛應(yīng)用在軍事和醫(yī)療等各個領(lǐng)域,智能穿戴設(shè)備不斷引領(lǐng)著新的潮流,改變著人們的生活,但是其所處理的應(yīng)用場景卻復(fù)雜多變。面對不斷膨脹的應(yīng)用需求,消費者要求穿戴設(shè)備在不斷減少體積和質(zhì)量的同時,進一步保證系統(tǒng)的性能和有效的降低功耗,延長使用時間和待機時間。然而性能和功耗本身就是不可調(diào)和的矛盾,體積減少、性能提升的同時,必然導(dǎo)致系統(tǒng)的功耗提升。因此功耗問題已經(jīng)嚴重制約穿戴設(shè)備的進一步發(fā)展。以ARM big.LITTLE架構(gòu)為代表的性能異構(gòu)多核處理器由多個性能和功耗不同的處理器組成,通過不同性能和功耗的處理器對不同應(yīng)用進行處理,可以有效的降低功耗。針對big.LITTLE架構(gòu)處理器合理的進程調(diào)度和電源管理,對系統(tǒng)資源按需分配,可同時兼顧高性能和低功耗。目前成熟的調(diào)度算法或者動態(tài)電源框架,都是針對SMP等系統(tǒng)進行設(shè)計和優(yōu)化,不適應(yīng)本文使用的ARM big.LITTLE性能異構(gòu)多核架構(gòu),更不適應(yīng)穿戴設(shè)備復(fù)雜多變的應(yīng)用場景。通過研究分析現(xiàn)有調(diào)度算法的不足,結(jié)合穿戴設(shè)備特殊的應(yīng)用場景,提出了動態(tài)閾值的HMPDB負載均衡調(diào)度算法。該算法根據(jù)系統(tǒng)的總體負載調(diào)整系統(tǒng)的進程遷移閾值,在保證性能和公平性的同時實現(xiàn)負載均衡,不僅可以有效的降低功耗,更能適應(yīng)穿戴設(shè)備極端復(fù)雜的應(yīng)用場景。另一方面,傳統(tǒng)的調(diào)度器與動態(tài)電源管理策略雖然都已資源分配、功耗降低為目標,但是各有側(cè)重,這些框架各自為政,勢必相互影響,造成額外的性能損失和功耗增加,本文進一步改進調(diào)度器和動態(tài)電源管理系統(tǒng),以HMPDB調(diào)度器為核心,實現(xiàn)了一個目標統(tǒng)一的HMPDB-EAS節(jié)能調(diào)度框架,通過調(diào)度器對CPU和應(yīng)用程序的負載進行統(tǒng)計分析,協(xié)調(diào)CPUFreq調(diào)頻子系統(tǒng)和CPUIdle子系統(tǒng)的運行,在保證CPU性能同時滿足系統(tǒng)的負載需求和應(yīng)用程序的性能需求的同時,降低穿戴設(shè)備的功耗,同時延長CPU處于的休眠模式的時間,進一步降低穿戴設(shè)備的功耗。經(jīng)過實驗表明,本文設(shè)計的動態(tài)節(jié)能調(diào)度框架HMPDB-EAS更加適應(yīng)穿戴設(shè)備的應(yīng)用場景,通過調(diào)度器和動態(tài)電源管理框架的協(xié)調(diào)配合,能夠在保證系統(tǒng)性能的同時有效的降低系統(tǒng)的功耗。
[Abstract]:With the innovation of technology and the development of wearable devices, smart wearable devices are widely used in military, medical and other fields. Intelligent wearable devices are leading new trends and changing people's lives. However, the application scenarios are complex and changeable. In the face of the ever-expanding application demand, consumers require wearable devices to reduce the volume and quality of the system while further ensuring the performance of the system and effectively reducing power consumption, prolonging the service time and standby time. However, the performance and power consumption itself is irreconcilable contradiction, the volume reduction, the performance enhancement at the same time, inevitably leads to the system power consumption enhancement. Therefore, the problem of power consumption has seriously restricted the further development of wearable devices. The performance heterogeneous multicore processor represented by ARM big.LITTLE architecture is composed of multiple processors with different performance and different power consumption. Different applications can be processed by different performance and power consumption processors, which can effectively reduce power consumption. According to the reasonable process scheduling and power management of the big.LITTLE architecture processor, the system resource can be allocated according to the demand, and the high performance and low power consumption can be taken into account at the same time. At present, the mature scheduling algorithms or dynamic power supply frameworks are designed and optimized for SMP and other systems, which are not suitable for the heterogeneous multi-core architecture of ARM big.LITTLE performance used in this paper, and not suitable for the complex and changeable application scenarios of wearable devices. By studying and analyzing the shortcomings of the existing scheduling algorithms and combining the special application scenarios of wearable devices, a dynamic threshold HMPDB load balancing scheduling algorithm is proposed. The algorithm adjusts the process migration threshold according to the overall load of the system and realizes load balancing while ensuring performance and fairness. It can not only effectively reduce power consumption but also adapt to the extremely complex application scenarios of wearable devices. On the other hand, although the traditional scheduler and the dynamic power management strategy have been allocated the resources, the power consumption is reduced as the goal, but each has its own emphasis, these frameworks are each other, which will inevitably affect each other, resulting in additional performance loss and increased power consumption. In this paper, the scheduler and the dynamic power management system are further improved. With HMPDB scheduler as the core, a unified HMPDB-EAS energy saving scheduling framework is implemented, and the load of CPU and application is statistically analyzed through the scheduler. The CPUFreq FM subsystem and the CPUIdle subsystem are coordinated to ensure that the CPU performance meets both the system load requirements and the application performance requirements, while reducing the power consumption of the wearable devices, while prolonging the time spent in the dormant mode of the CPU. Further reduce the power consumption of wearable devices. The experimental results show that the dynamic energy saving scheduling framework HMPDB-EAS is more suitable for wearable device application, and coordination of scheduler and dynamic power management framework. It can effectively reduce the power consumption of the system while ensuring the performance of the system.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP368.33
本文編號：2336628
[Abstract]:With the innovation of technology and the development of wearable devices, smart wearable devices are widely used in military, medical and other fields. Intelligent wearable devices are leading new trends and changing people's lives. However, the application scenarios are complex and changeable. In the face of the ever-expanding application demand, consumers require wearable devices to reduce the volume and quality of the system while further ensuring the performance of the system and effectively reducing power consumption, prolonging the service time and standby time. However, the performance and power consumption itself is irreconcilable contradiction, the volume reduction, the performance enhancement at the same time, inevitably leads to the system power consumption enhancement. Therefore, the problem of power consumption has seriously restricted the further development of wearable devices. The performance heterogeneous multicore processor represented by ARM big.LITTLE architecture is composed of multiple processors with different performance and different power consumption. Different applications can be processed by different performance and power consumption processors, which can effectively reduce power consumption. According to the reasonable process scheduling and power management of the big.LITTLE architecture processor, the system resource can be allocated according to the demand, and the high performance and low power consumption can be taken into account at the same time. At present, the mature scheduling algorithms or dynamic power supply frameworks are designed and optimized for SMP and other systems, which are not suitable for the heterogeneous multi-core architecture of ARM big.LITTLE performance used in this paper, and not suitable for the complex and changeable application scenarios of wearable devices. By studying and analyzing the shortcomings of the existing scheduling algorithms and combining the special application scenarios of wearable devices, a dynamic threshold HMPDB load balancing scheduling algorithm is proposed. The algorithm adjusts the process migration threshold according to the overall load of the system and realizes load balancing while ensuring performance and fairness. It can not only effectively reduce power consumption but also adapt to the extremely complex application scenarios of wearable devices. On the other hand, although the traditional scheduler and the dynamic power management strategy have been allocated the resources, the power consumption is reduced as the goal, but each has its own emphasis, these frameworks are each other, which will inevitably affect each other, resulting in additional performance loss and increased power consumption. In this paper, the scheduler and the dynamic power management system are further improved. With HMPDB scheduler as the core, a unified HMPDB-EAS energy saving scheduling framework is implemented, and the load of CPU and application is statistically analyzed through the scheduler. The CPUFreq FM subsystem and the CPUIdle subsystem are coordinated to ensure that the CPU performance meets both the system load requirements and the application performance requirements, while reducing the power consumption of the wearable devices, while prolonging the time spent in the dormant mode of the CPU. Further reduce the power consumption of wearable devices. The experimental results show that the dynamic energy saving scheduling framework HMPDB-EAS is more suitable for wearable device application, and coordination of scheduler and dynamic power management framework. It can effectively reduce the power consumption of the system while ensuring the performance of the system.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP368.33
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