A Metrological Method for Characterizing Software-Induced Timing Uncertainty in a Representative PC-Based DAQ Ecosystem

• A novel SLOC method addresses a decades-old timing uncertainty problem. • The software processing period ( T loop ) is formally defined as a new measurand. • Log-Normal modeling captures the non-Gaussian long-tail behavior of T loop . • P 99 is established as the robust safety boundary for uncertainty budgets. • USB 3.2 Gen 1 fails to mitigate the software bottleneck vs. direct-bus PCI. The metrological traceability of PC-based data acquisition (DAQ) systems is fundamentally challenged by timing errors originating from the software stack in non-real-time operating systems (NRTOS). While hardware-level clock jitter is well-understood, software-induced timing uncertainty remains a critical, unaddressed component in total measurement uncertainty budgets. To resolve this, this paper establishes the software processing period ( T loop ) as a formal measurand and validates a novel, non-intrusive Software Loop Overhead Characterization (SLOC) methodology. By conducting a comprehensive experimental study across diverse hardware architectures (PCI, PXI, USB 2.0, and USB 3.2 Gen 1) and host platforms (from mobile-class to high-performance workstations), we demonstrate that T loop is the dominant source of system-level timing uncertainty. Advanced statistical modeling reveals that T loop exhibits a distinct non-Gaussian, heavy-tailed distribution, with Type A standard uncertainty ( u ( T loop ) ) ranging from 1.57 ms to 9.97 ms. Our cross-generational analysis identifies an architectural "version trap," confirming that modern high-bandwidth bus interfaces (USB 3.2 Gen 1) fail to inherently mitigate the timing bottleneck compared to legacy direct-bus architectures (PCI). Statistical significance tests (Welch’s t-test and Levene’s test) further clarify that modern software stacks primarily benefit jitter suppression rather than absolute latency reduction. Consequently, we recommend the 99th percentile ( P 99 ) as the robust boundary for GUM-compliant uncertainty budgets. This work provides a rigorous framework for transitioning from heuristic performance estimations to a data-driven, system-level metrology.