Reliable power and time-constraints-aware predictive management of heterogeneous exascale systems

Fornaciari, William; Agosta, Giovanni; Atienza, David; Brandolese, Carlo; Cammoun, Leila; Cremona, Luca; Cilardo, Alessandro; Farrés, Albert; Flich, José; Hernández, Carles; Kulchewski, Michal; Libutti, Simone; Martínez, J.M.; Massari, Giuseppe; Oleksiak, Ariel; Pupykina, Anna; Reghenzani, Federico; Tornero, Rafael; Zanella, Michele; Zapater, Marina; Zoni, Davide

doi:10.1145/3229631.3239368

Cited by 18 publications

(15 citation statements)

References 25 publications

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“…One of the most important challenges brought by current heterogeneous and reconfigurable systems is thermal modeling. As pointed out in Reference [ 70 ]: "In traditional high-performance CPUs, the knowledge of the chip floorplan allows to identify, at design time, the hot-spots. However, this is not feasible if the system is equipped with reconfigurable fabrics (e.g., FPGAs) for application-specific acceleration purposes.…”

Section: Thermal Modeling and Reliability In Heterogeneous Reconfigurmentioning

confidence: 99%

Predictive Reliability and Fault Management in Exascale Systems

et al. 2020

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Performance and power constraints come together with Complementary Metal Oxide Semiconductor technology scaling in future Exascale systems. Technology scaling makes each individual transistor more prone to faults and, due to the exponential increase in the number of devices per chip, to higher system fault rates. Consequently, High-performance Computing (HPC) systems need to integrate prediction, detection, and recovery mechanisms to cope with faults efficiently. This article reviews fault detection, fault prediction, and recovery techniques in HPC systems, from electronics to system level. We analyze their strengths and limitations. Finally, we identify the promising paths to meet the reliability levels of Exascale systems.

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Section: Thermal Modeling and Reliability In Heterogeneous Reconfigurmentioning

confidence: 99%

Predictive Reliability and Fault Management in Exascale Systems

et al. 2020

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“…This research was partially funded by EU project RECIPE H2020 (grant no. 801137) [21]. The test statistic of Kolmogorov-Smirnov test is [28]:…”

Section: Acknowledgmentsmentioning

confidence: 99%

Dealing with Uncertainty in pWCET Estimations

Reghenzani

Santinelli

Fornaciari

2020

ACM Trans. Embed. Comput. Syst.

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The problem of estimating a tight and safe Worst-Case Execution Time (WCET), needed for certification in safety-critical environment, is a challenging problem for modern embedded systems. A possible solution proposed in last years is to exploit statistical tools to obtain a probability distribution of the WCET. These probabilistic real-time analyses for WCET are however subject to errors, even when all the applicability hypotheses are satisfied and verified. This is caused by the uncertainties of the probabilistic-WCET distribution estimator. This article aims at improving the measurement-based probabilistic timing analysis approach providing some techniques to analyze and deal with such uncertainties. The so-called region of acceptance model based on state-of-the-art statistical test procedures is defined over the distribution space parameters. From this model, a set of strategies is derived and discussed, to provide the methodology to deal with the trade-off safety/tightness of the WCET estimation. These techniques are then tested over real datasets, including industrial safety-critical applications, to show the increased value of using the proposed approach in probabilistic WCET analyses.

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“…This work is supported by NSF grant CNS-1850851, a startup grant from the University of Central Florida, the EU H2020 project RECIPE [1], [2], [23] grant 801137, and a HiPEAC collaboration grant 2019.…”

Section: Acknowledgmentsmentioning

confidence: 99%

Optimizing Energy in Non-Preemptive Mixed-Criticality Scheduling by Exploiting Probabilistic Information

Bhuiyan

Reghenzani

Fornaciari

et al. 2020

IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

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The strict requirements on the timing correctness biased the modeling and analysis of real-time systems towards the worst-case performances. Such focus on the worst-case, however, does not provide enough information to effectively steer the resource/energy optimization. In this paper, we integrate a probabilistic-based energy prediction strategy with the precise scheduling of mixed-criticality tasks, where the timing correctness must be met for all tasks at all scenarios. The Dynamic Voltage and Frequency Scaling (DVFS) is applied to this precise scheduling policy to enable energy minimization. We propose a probabilistic technique to derive an energy-efficient speed (for the processor) that minimizes the average energy consumption, while guaranteeing the (worst-case) timing correctness for all tasks, including lo-criticality ones, under any execution condition. We present a response time analysis for such systems under the nonpreemptive fixed-priority scheduling policy. Finally, we conduct an extensive simulation campaign based on randomly generated task sets to verify the effectiveness of our algorithm (w.r.t. energy savings) and it reports up to 46% energy-saving.

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Reliable power and time-constraints-aware predictive management of heterogeneous exascale systems

Cited by 18 publications

References 25 publications

Predictive Reliability and Fault Management in Exascale Systems

Predictive Reliability and Fault Management in Exascale Systems

Dealing with Uncertainty in pWCET Estimations

Optimizing Energy in Non-Preemptive Mixed-Criticality Scheduling by Exploiting Probabilistic Information

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