In-Network Collective Operations: Game Changer or Challenge for AI Workloads?
It addresses the challenge of improving efficiency in AI workloads through networking innovations, but is incremental as it summarizes existing opportunities and obstacles without presenting new results.
This paper explores the potential of in-network collective operations (INC) to accelerate collective operations in AI workloads, outlining performance benefits and six key obstacles for both Edge-INC and Core-INC implementations.
This paper summarizes the opportunities of in-network collective operations (INC) for accelerated collective operations in AI workloads. We provide sufficient detail to make this important field accessible to non-experts in AI or networking, fostering a connection between these communities. Consider two types of INC: Edge-INC, where the system is implemented at the node level, and Core-INC, where the system is embedded within network switches. We outline the potential performance benefits as well as six key obstacles in the context of both Edge-INC and Core-INC that may hinder their adoption. Finally, we present a set of predictions for the future development and application of INC.