将AI推理任务调度至风电场旁的模块化数据中心，用绿色电力高效运行。
AI Greenferencing: Routing AI Inferencing to Green Modular Data Centers with Heron
- 把AI推理任务动态路由到风电场附近的模块化数据中心。
- 相比现有方案，整体计算吞吐量最高提升80%。
- 适合关注绿色算力与低碳部署的研究者和企业
由于AI计算高功耗密度和新兴的推理负载，人工智能算力需求空前增长。供电端方面，大量风力发电资源因电网接入排队而闲置。本文提出将AI算力任务迁移到与风电场共址的模块化计算集群中。通过精准的部署规模设计，可实现超过600万块高端GPU今日即部署，直接利用风电源头的廉价绿电。我们构建了Heron——一个跨站点软件路由器，能利用多个风电场间发电的互补性，在风电波动时动态调度推理任务。基于Azure的一周编码与对话生成负载数据及真实风力发电波动数据，实验表明Heron相较当前最优方案，可使整体算力吞吐量提升高达80%。
原文摘要 · Abstract (English)
AI power demand is growing unprecedentedly thanks to the high power density of AI compute and the emerging inferencing workload. On the supply side, abundant wind power is waiting for grid access in interconnection queues. In this light, this paper argues bringing AI workload to modular compute clusters co-located in wind farms. Our deployment right-sizing strategy makes it economically viable to deploy more than 6 million high-end GPUs today that could consume cheap, green power at its source. We built Heron, a cross-site software router, that could efficiently leverage the complementarity of power generation across wind farms by routing AI inferencing workload around power drops. Using 1-week ofcoding and conversation production traces from Azure and (real) variable wind power traces, we show how Heron improves aggregate goodput of AI compute by up to 80% compared to the state-of-the-art.
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