In-Network Acceleration

Overview

With the slowdown of Moore’s law, large-scale data centers have been employing an increasing number of domain-specific accelerators (GPUs, TPUs, and FPGAs) to deliver the needed unprecedented performance for computation-intensive workloads like machine learning model training. Under this trend, the emergence of programmable network devices (P4 switches, SmartNICs, and DPUs more recently) has motivated a new concept called in-network computing, where programmable network devices (with P4/NPL languages) are instructed to accelerate application-specific computations (e.g., AllReduce for distributed machine learning training) in addition to running network functions. In-network computing brings tremendous performance benefits for a variety of distributed workloads, but also imposes challenges to the design of data center systems. We target the usability of data center in-network computing and propose programming models and management systems.

People

• 
  George Karlos
  PhD
• Amir K. Esfahani
  PhD

Support

Selected publications

No publications yet.