nSim-RV: A Reproducible RISC-V Framework for Scheduler-Aware Timing Scalability Under Increasing Task Concurrency

Abstract

As RISC-V processors are increasingly considered for embedded real-time and control-oriented systems, evaluating how timing behavior changes under increasing task concurrency becomes essential. Adding runnable tasks can amplify preemptions, context-switch activity, response-time variability, execution jitter, and deadline pressure. Existing RISC-V simulation and virtual-platform environments mainly target architectural exploration, functional validation, or full-system execution, and do not directly provide a controlled workflow for isolating scheduler-induced timing degradation across large configuration spaces. This paper presents nSim-RV, a configurable and reproducible RISC-V simulation and orchestration framework for scheduler-aware timing scalability evaluation. The framework combines automated campaign generation, deterministic workload configuration, structured dataset aggregation, duplicate validation, and timing-oriented metric extraction. The evaluation compares a standard shared-pipeline execution model with an nMPRA-inspired preserved-context mode under identical scheduler and workload conditions. The campaign includes CoreMark, Dhrystone, and a deterministic synthetic RT-Control workload, 2–32 concurrent tasks, 50 k–1 M cycle observation windows, cache-disabled and cache-enabled configurations, and four-stage and five-stage pipeline organizations, resulting in 864 validated configurations. Results show that increasing task concurrency amplifies timing variability and deadline pressure. Preserved-context execution reduces switching-induced disturbance and delays or reduces higher-pressure timing behavior in several trajectories. Under the five-stage cache-disabled RT-Control configuration at N = 32, it reduces the deadline miss ratio from 3.74% to 2.21%, corresponding to a 41.1% relative reduction, with the clearest benefits observed for Dhrystone and RT-Control at intermediate–high task counts.