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FpgaNIC: An FPGA-based Versatile 100Gb SmartNIC for GPUs

Zeke Wang, Hongjing Huang, Jie Zhang, Fei Wu, Gustavo Alonso

2022Year
58Citations
22Top-tier citations

Abstract

Network bandwidth is improving faster than the compute capacity of the host CPU, turning the CPU into a bottleneck. As a result, SmartNICs are often used to offload packet processing, even application logic, away from the CPU. However, today many applications such as Artificial Intelligence (AI) and High Performance Computing (HPC) rely on clusters of GPUs for computation. In such clusters, the majority of the network traffic is created by the GPUs. Unfortunately, commercially available multi-core SmartNICs, such as BlueFiled-2, fail to process 100Gb network traffic at line-rate with its embedded CPU, which is capable of doing control-plane management only. Commercially available FPGA-based Smart-NICs are mainly optimized for network applications running on the host CPU. To address such scenarios, in this paper we present FpgaNIC, a GPU-oriented SmartNIC to accelerate applications running on distributed GPUs. FpgaNIC is an FPGA-based, GPU-centric, versatile SmartNIC that enables direct PCIe P2P communication with local GPUs using GPU virtual address, and that provides reliable 100Gb network access to remote GPUs. FpgaNIC allows to offload various complex compute tasks to a customized data-path accelerator for line-rate in-network computing on the FPGA, thereby complementing the processing at the GPU. The data-path accelerator can be programmed using C++-based HLS (High Level Synthesis), so as to make it easier to use for software programmers. FpgaNIC has been designed to explore the design space of SmartNICs, e.g., direct, on-path, and off-path models, benefiting different type of application. It opens up a wealth of research opportunities, e.g., accelerating a broad range of distributed applications by combining GPUs and FPGAs and exploring a larger design space of SmartNICs by making them easily accessible from local GPUs.

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