Scalable big data analytics for neuro-control: An HPC parallel architecture for synchronization suppression in fNIRS networks

The integration of neuro-inspired networks with cognitive control mechanisms creates a unique Big Data challenge. While the spatial resolution of fNIRS networks (typically N = 613 ) is midscale compared to social graphs, the computational complexity arises from the interaction between high-dimensional topology, temporal depth, and stochastic reinforcement learning. This continuous state-space exploration generates a data volume and processing intensity that exhibits the classic “volume” and “velocity” characteristics of Big Data. Standard serial analytics fail to address this, particularly when computing high-cost synchronization metrics like spectral coherence ( O ( N 2 ) ) and Lyapunov stability ( O ( N 3 ) ). To bridge this gap, we introduce a Scalable Hierarchical Parallel Architecture for neuro-analytics. We implement a two-level strategy: (1) High-Throughput Computing (HTC) to distribute the coarse-grained stochastic ensemble of tens of thousands of control epochs, and (2) High-Performance Computing (HPC) utilizing AVX-512 vectorization to accelerate fine-grained spectral computations. Large-scale cluster experiments demonstrate substantial speedups, while extrapolation of the measured performance to the complete workflow indicates a reduction in estimated latency from several days to hours. The results confirm that a hierarchical HPC infrastructure is not merely an optimization, but a necessary prerequisite for extracting interpretable scientific value from the massive volumes of data generated by modern neuro-control simulations in real-time.