LifeSim: A lifetime reliability simulator for manycore systems

Rohith, R; Rathore, Vijeta; Chaturvedi, Vivek; Singh, Amit Kumar; Srikanthan, Thambipillai; Lam, Siew-Kei

doi:10.1109/ccwc.2018.8301711

Cited by 11 publications

(9 citation statements)

References 13 publications

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“…Cycle-accurate experiments are conducted using an extended version of Sniper called LifeSim, 23 a simulation tool which integrates Sniper, 24 McPat 25 and HotSpot 26 and the proposed approach. LifeSim simulator consists of a set of applications which spawns a number of threads which run simultaneously and can be picked from any of the available benchmarks.…”

Section: Simulation Set Up and Resultsmentioning

confidence: 99%

ABENA: An Ageing before Temperature Electromigration-Aware Neighbour Allocation for Many-Core Architectures

Ofori-Attah

Agyeman

2021

2021 4th International Conference on Data Storage and Data Engineering

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The percentage of inactive nodes or dark nodes (we refer to dark nodes as dumso) in many-core systems increases because of power dissipation caused by continuous scaling in technology. To address this challenge, existing work employ several techniques. Some techniques place the dumso nodes strategically between active nodes to alleviate the temperature by choosing the nodes which are far away from each other. However, this increases the latency between nodes which require inter communication leading to performance degradation. Others employ Dynamic Thermal Management (DTM) to vary the Voltage Frequency (V/F) Scaling of the nodes whilst Task migration is used to migrate tasks. In this paper, an Ageing Before Temperature Eletromigration-Aware Neighbour Allocation (ABENA) is proposed to alleviate the temperature of the nodes by using the Lifetime of nodes as the main parameter. Experiments show that our approach improves the lifetime of nodes.

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Section: Simulation Set Up and Resultsmentioning

confidence: 99%

ABENA: An Ageing before Temperature Electromigration-Aware Neighbour Allocation for Many-Core Architectures

Ofori-Attah

Agyeman

2021

2021 4th International Conference on Data Storage and Data Engineering

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“…This section presents the experimental setup, results, and discussions of the proposed work. Experiments and evaluations are conducted using an extended version of Sniper [38] called LifeSim [39], which integrates McPat [40] for power simulation, HotSpot [41] to model the thermal profile and generate thermal values. Additionally, LifeSim also integrates RAMP [42] to model the lifetime of the nodes.…”

Section: Environmental Evaluationmentioning

confidence: 99%

An Ageing-Aware and Temperature Mapping Algorithm for Multilevel Cache Nodes

Ofori-Attah

Agyeman

2023

IEEE Access

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Increase in chip inactivity in the future threatens the performance of many-core systems and therefore, efficient techniques are required for continuous scaling of transistors. As of a result of this challenge, future proposed many-core system designs must consider the possibility of a 50% functioning chip per time as well maintaining performance. Fortunately, this 50% inactivity can be increased by managing the temperature of active nodes and the placement of the dark nodes to leverage a balance working chip whilst considering the lifetime of nodes. However, allocating dark nodes inefficiently can increase the temperature of the chip and increase the waiting time of applications. Consequently, due to stochastic application characteristics, a dynamic rescheduling technique is more desirable compared to fixed design mapping. In this paper, we propose an Ageing Before Temperature Electromigration-Aware, Negative Bias Temperature Instability (NBTI) & Time-dependent Dielectric Breakdown (TDDB) Neighbour Allocation (ABENA 2.0), a dynamic rescheduling management system which considers the ageing and temperature before mapping applications. ABENA also considers the location of active and dark nodes and migrate task based on the characteristics of the nodes. Our proposed algorithm employ Dynamic Voltage Frequency Scaling (DVFS) to reduce the Voltage and Frequency (VF) of the nodes. Results show that, our proposed methods improve the ageing of nodes compared to a conventional round-robin management system by 10% in temperature, and 10% ageing.

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“…6 shows the experimental setup of the proposed work. LifeSim simulation tool [35] for many-core systems was used. LifeSim is a tool that integrates Sniper [36] with HotSpot [37] thermal simulator.…”

Section: A Experimental Setupmentioning

confidence: 99%

PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core Systems

Mohammed¹,

Paraman

Rahman

et al. 2021

IEEE Access

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Future many-core systems need to address the dark silicon problem, where some cores would be turned off to control the chip's thermal and power density, which effectively limits the performance gain from having a large number of processing cores. Task migration technique has been previously proposed to improve many-core system performance by moving tasks between active and dark cores. As task migration imposes system performance overhead due to the large wake-up latency of the dark cores, this paper proposes a prediction-based early wake-up (PEW) to reduce the dark cores' wake-up latency during task migration. A window-based online ridge regression (RR) is used as the prediction model. The prediction model uses the past window's thermal, power, and core status (i.e., active or dark) to predict the future core temperatures at run-time. If task migration is predicted in the next control period, the proposed PEW puts the dark cores in a power state with low wake-up latency. Thus, the proposed PEW reduces the time for the dark cores to start executing the tasks. The comparison results show that our proposed PEW reduces the completion time by up to 7.9% and 4.1% compared to non-early wake-up (NoEW) and a fixed threshold wake-up (FEW), respectively. It also shows that the proposed PEW increases the MIPS/Watt by up to 5.5% and 2.3% over NoEW and FEW, respectively. These results show that the proposed PEW improves the many-core system's overall performance in terms of reducing dark cores' wake-up latency and increasing the number of executed instructions per Watt.INDEX TERMS Dark silicon, many-core systems, task migration, dynamic voltage frequency scaling (DVFS), ridge regression, early wake-up, dark core wake-up latency.

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LifeSim: A lifetime reliability simulator for manycore systems

Cited by 11 publications

References 13 publications

ABENA: An Ageing before Temperature Electromigration-Aware Neighbour Allocation for Many-Core Architectures

ABENA: An Ageing before Temperature Electromigration-Aware Neighbour Allocation for Many-Core Architectures

An Ageing-Aware and Temperature Mapping Algorithm for Multilevel Cache Nodes

PEW: Prediction-Based Early Dark Cores Wake-up Using Online Ridge Regression for Many-Core Systems

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