Aging-Aware Parallel Execution

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.