Cooperative Slack Management: Saving Energy of Multicore Processors by Trading Performance Slack between QoS-Constrained Applications
Artikel i vetenskaplig tidskrift, 2022
Processor resources can be adapted at runtime according to the dynamic behavior of applications to reduce the energy consumption of multicore processors without affecting the Quality-of-Service (QoS). To achieve this, an online resource management scheme is needed to control processor configurations such as cache partitioning, dynamic voltage-frequency scaling, and dynamic adaptation of core resources.Prior State-of-the-art has shown the potential for reducing energy without any performance degradation by coordinating the control of different resources. However, in this article, we show that by allowing short-term variations in processing speed (e.g., instructions per second rate), in a controlled fashion, we can enable substantial improvements in energy savings while maintaining QoS. We keep track of such variations in the form of performance slack. Slack can be generated, at some energy cost, by processing faster than the performance target. On the other hand, it can be utilized to save energy by allowing a temporary relaxation in the performance target. Based on this insight, we present Cooperative Slack Management (CSM). During runtime, CSM finds opportunities to generate slack at low energy cost by estimating the performance and energy for different resource configurations using analytical models. This slack is used later when it enables larger energy savings. CSM performs such trade-offs across multiple applications, which means that the slack collected for one application can be used to reduce the energy consumption of another. This cooperative approach significantly increases the opportunities to reduce system energy compared with independent slack management for each application. For example, we show that CSM can potentially save up to 41% of system energy (on average, 25%) in a scenario in which both prior art and an extended version with local slack management for each core are ineffective.
performance and energy modeling
dynamic core resizing