Re-evaluating compute performance in SBC clusters: HPL benchmarking across generations

Single Board Computer Clusters (SBCCs) are increasingly used as accessible, low-power platforms for parallel and distributed computing, particularly in edge and fog environments. Yet their performance remains underexplored through reproducible, tuned evaluations. This paper presents a benchmarking methodology based on the High Performance Linpack (HPL) benchmark, selected for its use of dense linear algebra kernels common in scientific and machine learning workloads. The evaluation includes HPL parameter tuning, compiler configuration, and comparison of ATLAS vs. OpenBLAS. We apply the methodology SBCs spanning a decade of development: Raspberry Pi 1B, 3B, 4B, and 5, Cubieboard 2, Odroid U3, and Odroid-MC1. Results show that software-level tuning without overclocking or hardware modification can yield performance improvements of up to 2.3 × over prior reports. A 146 × increase in HPL performance between the Pi 1B and Pi 5 illustrates the evolution in computational capability within a stable form factor. OpenBLAS outperforms ATLAS on newer platforms, while ATLAS retains marginal advantages on older boards. The findings provide a reproducible baseline for SBCC performance evaluation and support their relevance for benchmarking, education, and energy-efficient high-performance workloads in scenarios where conventional clusters are impractical due to cost, size, or power.