NetRPC: Enabling In-Network Computation in Remote Procedure Calls
Bohan Zhao, Wenfei Wu, Wei Xu
摘要
People have shown that in-network computation (INC) significantly boosts performance in many application scenarios include distributed training, MapReduce, agreement, and network monitoring. However, existing INC programming is unfriendly to the normal application developers, demanding tedious network engineering details like flow control, packet organization, chip-specific programming language, and ASIC architecture with many limitations. We propose a general INC-enabled RPC system, NetRPC. NetRPC provides a set of familiar and lightweight interfaces for software developers to describe an INC application using a traditional RPC programming model. NetRPC also proposes a general-purpose INC implementation together with a set of optimization techniques to guarantee the efficiency of various types of INC applications running on a shared INC data plane. We conduct extensive experiments on different types of applications on the real testbed. Results show that using only about 5% or even fewer human-written lines of code, NetRPC can achieve performance similar to the state-of-the-art INC solutions.
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引用它的顶会 Paper8
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- Enabling In-Network Acceleration Over the CloudHao Wang, Decang Sun, Jinbin Hu, Kai ChenINFOCOM 2025 · 被引用 3 次
- A Generic and Efficient Communication Framework for Message-Level In-Network ComputingXinchen Wan, Luyang Li, Han Tian, Xudong Liao 等INFOCOM 2025 · 被引用 2 次
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- Scaling Distributed Machine Learning with In-Network AggregationAmedeo Sapio, Marco Canini, Chen-Yu Ho, Jacob Nelson 等NSDI 2021
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