LAUSR

Multi-GPU systems are widely used in data centers to provide significant speedups to compute-intensive workloads such as deep neural network training. However, limited PCIe bandwidth between the CPU and multiple GPUs becomes a major performance bottleneck. We observe that relying on a traditional Round-Robin-based PCIe scheduling policy can result in severe bandwidth competition and stall the execution of multiple GPUs. In this article, we propose a priority-based scheduling policy which aims to overlap the data transfers and GPU execution for different applications to alleviate this bandwidth contention. We also propose a dynamic priority policy for semi-QoS management that can help applications to meet QoS requirements and improve overall multi-GPU system throughput. Experimental results show that the system throughput is improved by 7.6 percent on average using our priority-based PCIe scheduling scheme as compared with a Round-Robin-based PCIe scheduler. Leveraging semi-QoS management can help to meet defined QoS goals, while preserving application throughput.