Distributed Reinforcement Learning for Power Limited Many-Core System Performance Optimization

2015 ◽  
Author(s):  
Zhuo Chen ◽  
Diana Marculescu
Keyword(s):  
Reinforcement Learning ◽  
Performance Optimization ◽  
System Performance ◽  
Core System ◽  
Many Core ◽  
Distributed Reinforcement

scholarly journals DTaPO: Dynamic Thermal-Aware Performance Optimization for Dark Silicon Many-Core Systems

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1980
Author(s):  
Mohammed Sultan Mohammed ◽  
Ali A. M. Al-Kubati ◽  
Norlina Paraman ◽  
Ab Al-Hadi Ab Rahman ◽  
M. N. Marsono
Keyword(s):  
Performance Optimization ◽  
System Performance ◽  
Cold Start ◽  
Experimental Results ◽  
Dark Silicon ◽  
Task Migration ◽  
Core System ◽  
Chip Temperature ◽  
System Temperature ◽  
Many Core

Future many-core systems need to handle high power density and chip temperature effectively. Some cores in many-core systems need to be turned off or ‘dark’ to manage chip power and thermal density. This phenomenon is also known as the dark silicon problem. This problem prevents many-core systems from utilizing and gaining improved performance from a large number of processing cores. This paper presents a dynamic thermal-aware performance optimization of dark silicon many-core systems (DTaPO) technique for optimizing dark silicon a many-core system performance under temperature constraint. The proposed technique utilizes both task migration and dynamic voltage frequency scaling (DVFS) for optimizing the performance of a many-core system while keeping system temperature in a safe operating limit. Task migration puts hot cores in low-power states and moves tasks to cooler dark cores to aggressively reduce chip temperature while maintaining high overall system performance. To reduce task migration overhead due to cold start, the source core (i.e., active core) keeps its L2 cache content during the initial migration phase. The destination core (i.e., dark core) can access it to reduce the impact of cold start misses. Moreover, the proposed technique limits tasks migration among cores that share the last level cache (LLC). In the case of major thermal violation and no cooler cores being available, DVFS is used to reduce the hot cores temperature gradually by reducing their frequency. Experimental results for different threshold temperatures show that DTaPO can keep the average system temperature below the thermal limit. Affirmatively, the execution time penalty is reduced by up to 18% compared with using only DVFS for all thermal thresholds. Moreover, the average peak temperature is reduced by up to 10.8°C. In addition, the experimental results show that DTaPO improves the system’s performance by up to 80% compared to optimal sprinting patterns (OSP) and reduces the temperature by up to 13.6°C.

scholarly journals Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control

2021 ◽  
pp. 1-1
Author(s):  
Federico Venturini ◽  
Federico Mason ◽  
Francesco Pase ◽  
Federico Chiariotti ◽  
Alberto Testolin ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Uav Swarm ◽  
Distributed Reinforcement
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