This paper presents a solution to the challenge of mitigating carbon emissions from large-scale high performance computing (HPC) systems and datacenters that host machine learning (ML) inference services. ML inference is critical to modern technology products, but it is also a significant contributor to datacenter compute cycles and carbon emissions. We introduce Clover, a carbon-friendly ML inference service runtime system that balances performance, accuracy, and carbon emissions through mixed-quality models and GPU resource partitioning. Our experimental results demonstrate that Clover is effective in substantially reducing carbon emissions while maintaining high accuracy and meeting service level agreement (SLA) targets. Therefore, it is a promising solution toward achieving carbon neutrality in HPC systems and datacenters.

翻译：本文提出了一种应对大规模高性能计算系统及托管机器学习推理服务的数据中心中碳排放挑战的解决方案。机器学习推理是现代技术产品的关键环节，同时也是数据中心计算周期与碳排放的重要来源。我们介绍了Clover——一种碳友好的机器学习推理服务运行时系统，通过混合质量模型与GPU资源分区来平衡性能、准确率与碳排放。实验结果表明，Clover能够在维持高准确率并满足服务等级协议目标的同时，有效降低碳排放。因此，它是推动高性能计算系统与数据中心实现碳中和目标的一种有前景的解决方案。