A High-Performance RDMA NIC With Ultrahighly Scalable Connections
Remote direct memory access (RDMA) technology has significantly enhanced network bandwidth and decreased transmission latency through kernel bypass and protocol offloading, overcoming obstacles in distributed computing systems. However, with the deployment of more intricate services in RDMA networks, current RDMA network interface cards (RNICs) have experienced a notable performance decline as the number of queue pair (QP) connections increases, substantially constraining the broad acceptance of RDMA networks. To address this challenge, this article proposes a novel RNIC architecture with high connection scalability. This architecture incorporates a multitiered cache structure to handle diverse communication contexts, enabling RNIC to support ultrahigh QP connection numbers while minimizing on-chip memory usage. In addition, the architecture facilitates chain prefetching, allowing on-chip caches to manage multiple concurrent requests; thus, averting latency resulting from cache misses and access conflicts during communication under concurrent multiple QP scenarios. This ensures transmission performance in multi-QPs connection scenarios. This article implements and validates the performance of a 100G RNIC based on this architecture on Xilinx’s U280 FPGA. With approximately 1 M memory usage on-chip for context, it can support 64 K performant QP connections (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$25\times $ </tex-math></inline-formula> than CX-6) and can be extended if necessary. Experimental results confirm the high connection scalability of the RNIC, achieving approximately 92 Gb/s network throughput for data packet transmission with concurrent execution of 1–64 K QPs.
Authors
- Qilin Dai (ORCID: https://orcid.org/0000-0001-8680-4306)
- Xuefei Yang (ORCID: https://orcid.org/0000-0002-2321-7662)
- S. Ye
- Zhenlong Wan
- Yingcheng Lin
- Ling Zhang (ORCID: https://orcid.org/0000-0002-1672-9715)
- Wei Gu (ORCID: https://orcid.org/0000-0001-6104-6350)
- Hang Liu (ORCID: https://orcid.org/0000-0001-9664-3331)
- Pingjing Liu (ORCID: https://orcid.org/0009-0005-6613-1745)
Institutions
- Chongqing University (CN)
- Huawei Technologies (China) (CN)
- Shanghai Public Security Bureau (CN)
- China National Chemical Engineering (China) (CN)
Publication Details
- Journal
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
- Published
- 2024-12-11
- DOI
- https://doi.org/10.1109/tcad.2024.3514782
- Citations
- 2
- Primary Topic
- Neural Networks and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.69
Funders
- National Key Research and Development Program of China