Thermal-Aware Scheduling for MPSoC in the Avionics Domain: Tooling and Initial Results

Ondřej Benedikt, M. Sojka, P. Zaykov, David Hornof, Matěj Kafka, P. Šůcha, Z. Hanzálek
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引用次数: 7

Abstract

The demand for high-performance computing leads to the adoption of modern Multi-Processor System-on-Chip platforms in the avionics domain, where many applications are safety-critical. To fulfill the safety requirements, it is vital to avoid the platform’s overheating. In this paper, we propose a task mapping method, MultiPAWS, for thermal-aware allocation of the safety-critical avionics workloads under time isolation constraints. With the help of MultiPAWS, we jointly find an optimal number of scheduling windows and their lengths and optimal mapping of the workload to these windows and available CPU cores. To guide the optimization, we introduce a thermal model based on power-characteristic coefficients, which we experimentally identify for a benchmark dataset on NXP i.MX8QuadMax platform (based on ARMv8 big.LITTLE architecture). Furthermore, to mimic the execution of safety-critical avionics applications, we introduce DEmOS, an open-source Linux-based scheduler. DEmOS provides a time-partitioned scheduling similar to the ARINC 653 standard. We use DEmOS for the experimental evaluation on the i.MX8 platform. The experimental results suggest that MultiPAWS achieves over a 12% decrease of the platform temperature compared to the minimum-utilization-based approach. Moreover, we demonstrate how MultiPAWS can be used in design space exploration for finding the tradeoff between the platform temperature and the length of the scheduling hyper-period.
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航空电子领域MPSoC的热感知调度:工具和初步结果
对高性能计算的需求导致航空电子领域采用现代多处理器片上系统平台,其中许多应用对安全至关重要。为了满足安全要求,避免平台过热是至关重要的。在本文中,我们提出了一种任务映射方法MultiPAWS,用于在时间隔离约束下对安全关键型航空电子工作负载进行热感知分配。在MultiPAWS的帮助下,我们共同找到了调度窗口的最优数量及其长度,以及工作负载到这些窗口和可用CPU内核的最优映射。为了指导优化,我们引入了一种基于功率特性系数的热模型,并在NXP i.MX8QuadMax平台(基于ARMv8 big)上对基准数据集进行了实验验证。小架构)。此外,为了模拟安全关键型航空电子应用程序的执行,我们引入了DEmOS,这是一个基于linux的开源调度器。DEmOS提供了类似于ARINC 653标准的分时调度。我们使用demo在i.MX8平台上进行了实验评估。实验结果表明,与基于最低利用率的方法相比,MultiPAWS可以将平台温度降低12%以上。此外,我们还演示了如何将MultiPAWS用于设计空间探索,以找到平台温度和调度超周期长度之间的权衡。
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CiteScore
1.70
自引率
14.30%
发文量
17
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