Minimizing Energy Consumption for Embedded Multicore Systems Using Cache Configuration and Task Mapping

2016 ◽  
Author(s):  
ZhiHua Gan ◽  
Mingquan Zhang ◽  
Zhimin Gu ◽  
Jizan Zhang
Keyword(s):  
Energy Consumption ◽  
Task Mapping ◽  
Multicore Systems ◽  
Cache Configuration ◽  
And Task

Efficiency Analysis of Approaches for Temperature Management and Task Mapping in Networks-on-Chip

2014 ◽  
pp. 368-398
Author(s):  
Tim Wegner ◽  
Martin Gag ◽  
Dirk Timmermann
Keyword(s):  
Thermal Management ◽  
System Performance ◽  
Task Mapping ◽  
Systematic Analysis ◽  
Networks On Chip ◽  
Chip Temperature ◽  
Management Measures ◽  
Mapping Process ◽  
On Chip ◽  
And Task

With the progress of deep submicron technology, power consumption and temperature-related issues have become dominant factors for chip design. Therefore, very large-scale integrated systems like Systems-on-Chip (SoCs) are exposed to an increasing thermal stress. On the one hand, this necessitates effective mechanisms for thermal management and task mapping. On the other hand, application of according thermal-aware approaches is accompanied by disturbance of system integrity and degradation of system performance. In this chapter, a method to predict and proactively manage the on-chip temperature distribution of systems based on Networks-on-Chip (NoCs) is proposed. Thereby, traditional reactive approaches for thermal management and task mapping can be replaced. This results in shorter response times for the application of management measures and therefore in a reduction of temperature and thermal imbalances and causes less impairment of system performance. The systematic analysis of simulations conducted for NoC sizes up to 4x4 proves that under certain conditions the proactive approach is able to mitigate the negative impact of thermal management on system performance while still improving the on-chip temperature profile. Similar effects can be observed for proactive thermal-aware task mapping at system runtime allowing for the consideration of prospective thermal conditions during the mapping process.

scholarly journals Worst-case energy consumption minimization based on interference analysis and bank mapping in multicore systems

2017 ◽  
Vol 13 (2) ◽  
pp. 155014771668696
Author(s):  
Zhihua Gan ◽  
Zhimin Gu ◽  
Hai Tan ◽  
Mingquan Zhang ◽  
Jizan Zhang
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Multicore Systems ◽  
Worst Case ◽  
Mapping Algorithm ◽  
Fine Grained ◽  
Energy Consumption Reduction ◽  
Deadline Constraints ◽  
Core Mapping ◽  
The Impact

Energy is a scarce resource in real-time embedded systems due to the fact that most of them run on batteries. Hence, the designers should ensure that the energy constraints are satisfied in addition to the deadline constraints. This necessitates the consideration of the impact of the interference due to shared, low-level hardware resources such as the cache on the worst-case energy consumption of the tasks. Toward this aim, this article proposes a fine-grained approach to analyze the bank-level interference (bank conflict and bus access interference) on real-time multicore systems, which can reasonably estimate runtime interferences in shared cache and yield tighter worst-case energy consumption. In addition, we develop a bank-to-core mapping algorithm for reducing bank-level interference and improving the worst-case energy consumption. The experimental results demonstrate that our approach can improve the tightness of worst-case energy consumption by 14.25% on average compared to upper-bound delay approach. The bank-to-core mapping provides significant benefits in worst-case energy consumption reduction with 7.23%.

scholarly journals A Correlated Model for Evaluating Performance and Energy of Cloud System Given System Reliability

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Hongli Zhang ◽  
Panpan Li ◽  
Zhigang Zhou
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Completion Time ◽  
High Performance ◽  
Virtual Machines ◽  
Memory Storage ◽  
Task Completion ◽  
Task Completion Time ◽  
Hardware Failure ◽  
And Task

The serious issue of energy consumption for high performance computing systems has attracted much attention. Performance and energy-saving have become important measures of a computing system. In the cloud computing environment, the systems usually allocate various resources (such as CPU, Memory, Storage, etc.) on multiple virtual machines (VMs) for executing tasks. Therefore, the problem of resource allocation for running VMs should have significant influence on both system performance and energy consumption. For different processor utilizations assigned to the VM, there exists the tradeoff between energy consumption and task completion time when a given task is executed by the VMs. Moreover, the hardware failure, software failure and restoration characteristics also have obvious influences on overall performance and energy. In this paper, a correlated model is built to analyze both performance and energy in the VM execution environment given the reliability restriction, and an optimization model is presented to derive the most effective solution of processor utilization for the VM. Then, the tradeoff between energy-saving and task completion time is studied and balanced when the VMs execute given tasks. Numerical examples are illustrated to build the performance-energy correlated model and evaluate the expected values of task completion time and consumed energy.

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