StaR: Breaking the Scalability Limit for RDMA

Xizheng Wang, Guo Chen*, Xijin Yin, Huichen Dai, Bojie Li, Binzhang Fu, Kun Tan

• We propose StaR (Stateless RDMA), which solves the scalability problem of RDMA by transferring states to the other communication end. Leveraging the asymmetric communication pattern in data center applications, StaR lets the communication end with low concurrency save states for the other end with high concurrency, thus making the RNIC on the bottleneck side to be stateless. We have implemented StaR on an FPGA board with 10Gbps network port and evaluated its performance on a testbed with 9 machines all equipped with StaR NICs. The experimental results show that in high concurrency scenarios, the throughput of StaR can reach up to 4.13x and 1.35x of the original RNIC and the latest software-based solution, respectively.

sRDMA: A General and Low-Overhead RDMA Scheduler for RDMA(pre to publish)

Xizheng Wang, Shuai Wang*, Dan Li

• We propose sRDMA, a general and lowoverhead scheduler working in user-space RDMA driver. sRDMA allows the application to express the expected transfer order to RDMA hardware via work requests (WRs). With priority information in WRs, sRDMA slices and schedules WRs to achieve desired order of message transfer and reduce blocking impact of large messages in the same RDMA connection. Our experiments show that sRDMA can improve the performance of applications, e.g., TensorFlow, by up to 54%, and sRDMA has negligible overhead in terms of CPU and flow throughput.