Performance And Power Optimization In Heterogeneous Multi-core Embedded Systems

Heterogeneous multi-core embedded systems have been widely applied in many fields,and their impacts on the human life keep growing.The increasing numbers of function,information and complicated functionality have illuminated the information unbalancing problem in various domains.Such unbalancing problems can gravely affect system performance and power consumption,which are significant to heterogeneous multi-core systems.How to improve performance and reduce power for embedded multi-core systems has attracted considerable research attention.In this thesis we focus on the system performance and power optimization for heterogeneous multi-core embedded systems.In order to study our problem,a common application scene that assigning a stream of general tasks into a heterogeneous multi-core system is considered.In the considered system,each core has preloaded a stream of dedicated tasks,and equips with different operation frequencies and power consumption.The stream of dedicated tasks preloaded on different core has various average arrival rate,average task size,and urgent level.To ensure the dedicated tasks can be executed in a manner of timing and improve the system utilization,three scheduling strategies,which are dedicated tasks without priority,prioritized dedicated tasks without preemption,and prioritized dedicated tasks with preemption,are provided.Each core can employ any one of the three strategies according to the urgency level of the dedicated tasks preloaded on it.Since the arrival rate,task size and corresponding response time of tasks have certain randomness,the system is modeled with queue theory.The major work and contributions of this paper are as follows.The power-constrained performance optimization under fixed frequency.The frequent processor frequency adjustment will shorten the life of processor.In many costsensitive systems the frequency adjustment is not advised to be employed.Therefore,we study the problem of power-constrained performance optimization under the case that the frequency of each core always keeps stable.We treat the frequency of each core as a continuous variable,and model the problem as a multi-variable and multi-constraint problem.A performance optimization algorithm is proposed that can obtain the global optimal performance for general tasks.Based on the proposed algorithm,the relationships between system performance and tasks arrival rate,tasks allocation,properties of core,and the tasks scheduling strategy are analyzed.The performance-constrained power optimization under up-and-down frequency.When the core is in idle state,reducing work frequency of core is a commonly used energy-saving technology.In embedded systems,some computing nodes are employed to perform urgent applications,this implies that these nodes cannot sleep completely even if the CPUs are idle.Therefore,we study the problem of performance-constraint power optimization under the case that the frequency is not the same when the CPU is in busy state and idle state.We model the problem also as a multi-variable and multi-constraint problem,and propose a power optimization algorithm that can obtain the optimal system power.Furthermore,when the problem is difficult to obtain the optimal solution because of the excessive imbalance among the number,size,and performance constraints of dedicated tasks assigned,a data fitting method is employed to assist to resolve the problem.Based on experiments,we analyze the impacts of different parameters on the system power optimization,and effectiveness of the power optimization.Finally,the consistency between theory solution and practical solution is verified on a practical multi-core embedded platform.Joint performance and power optimization.On embedded systems,both performance optimization and power optimization are of great importance.There also exists contradiction between them.The two problems,which are maximizing performance with limited power,and minimizing power while satisfying given performance,are usually studied separately.Therefore,we study the problem of performance and power joint optimization.According to the practical core frequency,the adjustable frequencies of processor are modeled as a set of discrete speeds.The problem of concern is not only a multi-variable and multi-constraint problem,but also a combinatorial optimization problem.In this investigation,we propose a novel load-balancing algorithm to obtain the optimal performance of general tasks while satisfying the performance requirements of dedicated tasks.The proposed load-balancing algorithm skillfully utilizes Karush-Kuhn-Tunker theory to solve performance optimization problem.It can also recursively solve the problem by transforming the problem into its sub-problem when the excessive imbalance among the number,size,and performance constraints of dedicated tasks assigned to each core causes the problem to became difficult to solve.On the basis of the above algorithm,we propose a joint performance and power optimization algorithm based on the ratio between performance and power.The proposed method of this study can not only solve the problem of optimizing power under given performance,but also solve the problem of optimizing performance under given power.Finally,extensive simulations are performed to verify our analytical results.