Computation has been pushed to the edge to decrease latency and alleviate the computational burden of the IoT applications in the cloud. However, the increasing processing demands of edge applications make necessary the employment of platforms that exploit thread-level parallelism (TLP). Yet, power and heat dissipation rise as TLP inadvertently increases or when parallelism is not cleverly exploited, which may be the result of the nonideal use of a given parallel program interface (PPI). Besides the common issues, such as the need for more robust power sources and better cooling, heat also adversely affects aging, accelerating phenomenons, such as negative bias temperature instability (NBTI) and hot-carrier injection (HCI), which further reduces processor lifetime. Hence, considering that increasing the lifespan of an edge device is key, so the number of times the application set may execute until its end-of-life is maximized, we propose BALDER. It is a learning framework capable of automatically choosing optimal configuration executions (PPI and number of threads) according to the parallel application at hand, aiming to maximize the tradeoff between aging and performance. When executing ten well-known applications on two multicore embedded architectures, we show that BALDER can find a nearly optimal configuration for all our experiments.

中文翻译：

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老化感知并行执行

计算已被推到边缘以减少延迟并减轻云中物联网应用程序的计算负担。然而，边缘应用程序不断增长的处理需求使得有必要使用利用线程级并行性 (TLP) 的平台。然而，当 TLP 无意中增加或没有巧妙利用并行性时，功耗和散热会增加，这可能是给定并行程序接口 (PPI) 使用不理想的结果。除了需要更强大的电源和更好的冷却等常见问题外，热量还会对老化和加速现象产生不利影响，例如负偏置温度不稳定性 (NBTI) 和热载流子注入 (HCI)，从而进一步缩短处理器寿命. 因此，考虑到增加边缘设备的使用寿命是关键，因此应用程序集在其生命周期结束之前可以执行的次数最大化，我们建议 BALDER。它是一个学习框架，能够根据手头的并行应用程序自动选择最佳配置执行（PPI 和线程数），旨在最大化老化和性能之间的权衡。当在两个多核嵌入式架构上执行十个著名的应用程序时，我们表明 BALDER 可以为我们所有的实验找到一个近乎最佳的配置。