Longevity Framework: Leveraging Online Integrated Aging-Aware Hierarchical Mapping and VF-Selection for Lifetime Reliability Optimization in Manycore Processors

Rathore, Vijeta; Chaturvedi, Vivek; Singh, Amit Kumar; Srikanthan, Thambipillai; Shafique, Muhammad

doi:10.1109/tc.2020.3006571

Cited by 11 publications

(14 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All applications used in these experiments were generated using the PREESM framework (version 3.4), compiled using GCC v7.5.0 optimization -O2, and executed on Sniper simulator. The energy estimation is performed with McPAT [15], which is integrated into Sniper and provides reliable power and energy figures broadly used in state-of-the-art works [12,20].…”

Section: Methodsmentioning

confidence: 99%

“…Sniper is a consolidated system simulator for multicore architectures, used in many state-of-the-art works to evaluate application's performance and, mainly, power and energy consumption [12,20]. Sniper adopts an interval-based core model simulation, which allows fast and accurate simulation.…”

Section: Model Descriptionmentioning

confidence: 99%

“…configuration 1), one private L3 cache (e.g. configuration 4), up to 32 cores sharing the same L3 (configuration [18][19][20][21][22].…”

Section: Hardware Setupmentioning

confidence: 99%

See 2 more Smart Citations

The Impact of Cache and Dynamic Memory Management in Static Dataflow Applications

Ghasemi

Ruaro

Cataldo

et al. 2022

J Sign Process Syst

View full text Add to dashboard Cite

multi/many-core architecture executing it. State-ofthe-art frameworks allow fast development of dataflow applications providing memory, communicating, and computing optimizations by design time exploration. However, the frameworks usually do not consider cache memory behavior when generating code. A generally accepted idea is that bigger and multi-level caches improve the performance of applications. This work evaluates such a hypothesis in a broad experiment campaign adopting different multi-core configurations related to the number of cores and cache parameters (size, sharing, controllers). The results show that bigger is not always better, and the foreseen future of more cores and bigger caches do not guarantee softwarefree better performance for dataflow applications. Additionally, this work investigates the adoption of two memory management strategies for dataflow applications: Copy-on-Write (CoW) and Non-Temporal Memory transfers (NTM). Experimental results addressing state-of-the-art applications show that NTM and CoW can contribute to reduce the execution time to -5.3% and -15.8%, respectively. CoW, specifically, shows improvements up to -21.8% in energy consumption with -16.8% of average among 22 different cache configurations.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Model Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

The Impact of Cache and Dynamic Memory Management in Static Dataflow Applications

Ghasemi

Ruaro

Cataldo

et al. 2022

J Sign Process Syst

View full text Add to dashboard Cite

show abstract

“…Haghbayan et al [34] proposed the Reliability-aware Mapping framework for darksilicon applications but it does not consider process variation. Alternatively, Rathore et al [35] proposed the Longevity Framework (LF) to improve the lifetime of nodes. The longevity framework considers process variation in the decision-making of assigning applications to cores.…”

Section: Related Workmentioning

confidence: 99%

AMA: An Ageing Task Migration Aware for High-Performance Computing

Ofori-Attah

Agyeman

2023

JLPEA

View full text Add to dashboard Cite

The dark-silicon challenge poses a design problem for future many-core systems. As a result of this, several techniques have been introduced to improve the number of processing elements that can be powered on. One of the techniques employed by many is Task Migration. In this paper, an Ageing Task Migration Aware for High-Performance Computing (AMA) is proposed to improve the lifetime of nodes. The proposed method determines which clusters applications are mapped to and migrates high-demand tasks amongst nodes to improve the lifetime at every epoch. Experimental results show that the proposed method outperforms state-of-the-art techniques by more than 10%.

show abstract

“…However, the long-term impact on system reliability is often not considered. To address this issue, Dynamic Reliability Management (DRM) techniques have gained increasing relevance [12,[19][20][21][22][23][24]. These DRM schemes integrate the consideration of a system's lifetime reliability into their strategies.…”

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning for Thermal and Reliability Management in Manycore Systems

Weber,

Zanini,

Moraes

2024

Preprint

View full text Add to dashboard Cite

The advent of manycore systems has led to the need for efficient dynamic thermal and reliability management techniques to increase system reliability. Increasing power density and thermal hotspots in manycore systems pose significant challenges to reliability and performance. Existing techniques often fail to scale effectively or consider long-term reliability impacts. This work aims to develop a lightweight and scalable management strategy for manycore systems that integrates dynamic thermal management (DTM) and dynamic reliability management (DRM) using application mapping and task migration. The primary contribution is the introduction of the FIT-aware Learning Heuristic for Application Allocation (FLEA), which leverages Q-learning to optimize task allocation and migration based on Failure In Time (FIT) monitoring. FLEA operates in two phases: a design phase that uses Q-learning to train a policy table (Q-table) and a runtime phase that utilizes this Q-table to make decisions on task allocation and migration. The Q-table is populated with values representing the best task deployment patterns, minimizing thermal hotspots and maximizing system reliability. The evaluation of FLEA demonstrates improvements over state-of-the-art techniques. FLEA effectively reduces the thermal amplitude, peak temperature, and spatial thermal distribution, resulting in enhanced Mean Time To Failure (MTTF) for the system.

show abstract

Longevity Framework: Leveraging Online Integrated Aging-Aware Hierarchical Mapping and VF-Selection for Lifetime Reliability Optimization in Manycore Processors

Cited by 11 publications

References 40 publications

The Impact of Cache and Dynamic Memory Management in Static Dataflow Applications

The Impact of Cache and Dynamic Memory Management in Static Dataflow Applications

AMA: An Ageing Task Migration Aware for High-Performance Computing

Reinforcement Learning for Thermal and Reliability Management in Manycore Systems

Contact Info

Product

Resources

About