CFD Simulation and Optimization of the Cooling of Open Compute Machine Learning “Big Sur” Server

摘要

In recent years, there have been phenomenal increases in Artificial Intelligence and Machine Learning that require data collection, mining and using data sets to teach computers certain things to learn, analyze image and speech recognition. Machine Learning tasks require a lot of computing power to carry out numerous calculations. Therefore, most servers are powered by Graphics Processing Units (GPUs) instead of traditional CPUs. GPUs provide more computational throughput per dollar spent than traditional CPUs. Open Compute Servers forum has introduced the state-of-the-art machine learning servers “Big Sur” recently. Big Sur unit consists of 4OU (OpenU) chassis housing eight NVidia Tesla M40 GPUs and two CPUs along with SSD storage and hot-swappable fans at the rear. Management of the airflow is a critical requirement in the implementation of air cooling for rack mount servers to ensure that all components, especially critical devices such as CPUs and GPUs, receive adequate flow as per requirement. In addition, component locations within the chassis play a vital role in the passage of airflow and affect the overall system resistance. In this paper, sizeable improvement in chassis ducting is targeted to counteract effects of air diffusion at the rear of air flow duct in “Big Sur” Open Compute machine learning server wherein GPUs are located directly downstream from CPUs. A CFD simulation of the detailed server model is performed with the objective of understanding the effect of air flow bypass on GPU die temperatures and fan power consumption. The cumulative effect was studied by simulations to see improvements in fan power consumption by the server. The reduction in acoustics noise levels caused by server fans is also discussed.