Hypersparse Traffic Matrix Construction using GraphBLAS on a DPU

TL;DR

This paper demonstrates the use of GraphBLAS on an ARM-based DPU to efficiently construct hypersparse traffic matrices at high speed, enabling advanced network analytics.

Contribution

It presents the first performance evaluation of GraphBLAS on a DPU and ARM system for hypersparse traffic matrix construction.

Findings

Constructed traffic matrices at over 18 million packets per second.

First reported GraphBLAS performance results on a DPU and ARM system.

Shows potential for high-speed network analytics using DPUs.

Abstract

Low-power small form factor data processing units (DPUs) enable offloading and acceleration of a broad range of networking and security services. DPUs have accelerated the transition to programmable networking by enabling the replacement of FPGAs/ASICs in a wide range of network oriented devices. The GraphBLAS sparse matrix graph open standard math library is well-suited for constructing anonymized hypersparse traffic matrices of network traffic which can enable a wide range of network analytics. This paper measures the performance of the GraphBLAS on an ARM based NVIDIA DPU (BlueField 2) and, to the best of our knowledge, represents the first reported GraphBLAS results on a DPU and/or ARM based system. Anonymized hypersparse traffic matrices were constructed at a rate of over 18 million packets per second.