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SIGCOMM2024顶会

OptimusPrime: Unleash Dataplane Programmability through a Transformable Architecture

Zhikang Chen, Yong Feng, Shuxin Liu, Haoyu Song, Hanyi Zhou, Tong Yun, Wenquan Xu, Tian Pan, Bin Liu

2024年份
6被引次数

摘要

Network dataplane calls for better programmability. Current programmable network processing chips are based on either pipeline or multi-core Run-To-Completion (RTC) architecture with various trade-offs in flexibility, performance, and cost. The existing attempts to amalgamate the strengths of the two are stilted and inflexible. In this paper, we challenge the status quo by introducing a more fluid and organic programmable chip architecture, OptimusPrime, built from identical hardware blocks. Unlike the conventional static hybrid architecture, OptimusPrime allows each block to be transformed into either a pipeline stage processor or a multi-core RTC processor through software-defined configuration, enabling versatile data plane programming tailored to a wide range of applications (e.g., stateful packet processing and in-network computing). We integrate the C and P4 languages for application programming and develop algorithms to map a user program to the optimal distribution of pipeline stages and RTC cores. We demonstrate the viability of OptimusPrime through practical use cases such as in-network aggregation, in-network caching, and network function integration. We developed an FPGA-based prototype and a software-based ASIC simulator to validate the feasibility of OptimusPrime, which can be used by switches and smartNICs to enhance their programmability to a new level with high performance and low cost.

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