Towards Accelerating the Network Performance on DPUs by optimising the P4 runtime.

Data Processing Units (DPUs) are becoming increasingly popular, especially for use in conjunction with Warehouse-Scale Computers (WSCs) due to their ability to handle networking functions and data-centric workloads. Cost-performance, energy efficiency, network 1/0, and batch processing workloads are important design factors for WSCs. Recent developments in AI and the never-ending increase in demand for data processing, cloud computing, and HPC set the optimisation of all those design factors as a high priority. DPUs can be utilised to achieve significant improvements in all those areas. This includes in-line network processing and upcoming enhanced security paradigms such as post-quantum cryptography (PQC) for quantum re-silient communications or software-defined perimeters (SDP) for confidential computing implementations. Being P4-enabled and dRMT-based, DPUs allow for the reconfigurability of the network traffic without the need to change the hardware. However, the network performance on such devices is only sometimes deter-ministic since the actual traffic and the rules, both of which have to do with packet processing, are not known during compile time. In this paper, we envision how the network performance on DPUs can be accelerated. We describe the challenges that negatively impact the bandwidth and latency: the complex steering pipeline and the massive runtime needed to optimise. These challenges arise from the lack of information during compile time that is only known during runtime. Thus, we envision optimising during runtime by leveraging DPUs' reconfigurability on the network 110. For this, we discuss the significant factors that must be considered to accelerate the network performance on such devices and we propose a solution for them.