# Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments
## 7 Conclusions and Future Directions  
The integration of High-Performance Computing (HPC) with big data processing presents transformative opportunities for modern industrial IoT (IIoT) environments, where real-time analytics, scalability, and energy efficiency are paramount. This study investigated the performance of Apache Spark on ARM-based HPC systems, particularly those utilizing Scalable Vector Extensions (SVEs), to evaluate their efficacy in handling industrial-scale big data workloads within cloud-edge computing architectures. Our experiments demonstrated that ARM-based processors, such as the Rhea-GPP prototype under the European Processor Initiative (EPI), can deliver competitive performance for distributed data processing. The adoption of vectorial instructions (SVEs) significantly enhanced parallel task execution, underscoring their potential to accelerate in-memory frameworks like Spark in industrial settings. However, we identified challenges in toolchain integration and platform support, which must be addressed to fully realize these benefits. These findings have critical implications for industrial IoT and edge computing. First, ARM-based architectures, with their energy-efficient design, are well-suited for edge deployments in smart factories, where minimizing power consumption while maintaining low-latency processing is essential. Second, the scalability of Spark on ARM-based HPC systems suggests promising applications in real-time industrial analytics, such as digital twin simulations, AI-driven predictive maintenance, and dynamic process monitoring. Third, our results align with the vision of a seamless cloud-edge continuum, where HPC-enhanced edge nodes collaborate with centralized cloud systems to optimize Industry 4.0 and 5.0 workloads. Future research should prioritize optimizing SVE support in big data frameworks like Spark to streamline industrial adoption. Further investigations into ARM-based HPC systems could explore their performance in specific IIoT use cases, such as real-time anomaly detection or large-scale sensor data fusion. Additionally, the interplay between 5G/6G networks and edge-tier HPC deployments warrants exploration to enable ultra-low-latency data exchange in smart manufacturing. By advancing ARM-based HPC solutions, this work contributes to the broader goals of industrial digitalization, sustainable computing, and European technological sovereignty. Collaborative efforts across academia and industry will be vital to refine these technologies for next-generation smart factories and beyond. We also recognize that further investigation, particularly through low-level profiling tools such as JIT logs and perf counters, is needed to validate the extent of SVE instruction usage and optimize runtime performance in future deployments. Conceptualization, L.R., I.M.A.J. and R.V. ; methodology, L.R., R.V. and I.M.A.J. ; software, L.R. ; validation, L.R., I.M.A.J. and R.V. ; formal analysis, R.V., I.M.A.J. and L.R. ; investigation, R.V., I.M.A.J. and L.R. ; resources, P.B. and L.F.; data curation, R.V., I.M.A.J. and L.R. ; writing--original draft preparation, R.V., I.M.A.J., L.F., L.R. and P.B. ; writing--review and editing, R.V., I.M.A.J., L.F., L.R. and P.B. ; visualization, R.V. and L.R. ; supervision, P.B., L.F., R.V. and I.M.A.J. ; project administration, R.V., L.F. and P.B. ; funding acquisition, P.B. All authors have read and agreed to the published version of the manuscript. SoBigData.it receives funding from European Union-NextGenerationEU-National Recovery and Resilience Plan (Piano Nazionale di Ripresa e Resilenza, PNNRR)-Project: \"SoBigData.it-Strengthening the Italian RI for Social Mining and Big Data Analytics\"-Prot. IR0000013-Avviso n. 3264 del 28/12/2021. Data Availability Statement: Dataset available on request from the authors. Conflicts of Interest: The authors declare no conflicts of interest.