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

Hypersparse Traffic Matrix Construction using GraphBLAS on a DPU