Treehouse: A Case For Carbon-Aware Datacenter Software

Abstract

The end of Dennard scaling and the slowing of Moore's Law has put the energy use of datacenters on an unsustainable path. Datacenters are already a significant fraction of worldwide electricity use, with application demand scaling at a rapid rate. We argue that substantial reductions in the carbon intensity of datacenter computing are possible with a software-centric approach: by making energy and carbon visible to application developers on a fine-grained basis, by modifying system APIs to make it possible to make informed trade offs between performance and carbon emissions, and by raising the level of application programming to allow for flexible use of more energy efficient means of compute and storage. We also lay out a research agenda for systems software to reduce the carbon footprint of datacenter computing.

EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy

“…Although present-day hardware only provides coarse-grained energy measurement capabilities, we believe that there is still immense potential to achieve fine-grained energy attribution with a software-based approach [4]. In this work, we demonstrate finegrained energy attribution of CPU and DRAM with coarse-grained measurements from Intel RAPL meter.…”

“…Furthermore, these tools do not separate their own energy cost from the measurements of target. We find that the lack of such accounting in fine-grained, software-centric attribution methodology [4] leads to more than 46.3% overestimation and 93.3% underestimation (Fig. 1), which could be detrimental for sustainable runtime operations.…”

Making machine learning on HPC systems cost-effective and carbon-friendly

“…Sustainable AI [105] presents an end-to-end analysis of how Meta uses hardware-software co-design to reduce its AI carbon footprint. Similar efforts toward reducing carbon emission also appear in Google and Microsoft [106,107,108,109] related to ML training and software development, but none of these prior works address the challenge of making ML inferences more carbon-friendly.…”