# Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments
## 3 System Architecture  
Building upon the architectural foundations established in Section 2, this investigation focuses on evaluating ARM-based High-Performance Computing architectures, particularly the Rhea-GPP processor that is being developed under the European Processor Initiative (EPI), for their potential to enhance big data processing in industrial IoT and edge computing scenarios. Given the ongoing development status of the EPI project, our experimental framework employs the V1 ARM processor as a proxy, which embodies the same fundamental design principles as the forthcoming Rhea-GPP: namely, the combination of high-performance vector processing capabilities with exceptional energy efficiency--a critical requirement for sustainable Industry 4.0 and 5.0 implementations. The V1 ARM processor's incorporation of 256-byte Scalable Vector Extensions (SVEs) represents a significant advancement for industrial applications, enabling fine-grained parallel processing that proves particularly beneficial for real-time analytics and streaming data operations. This architectural approach distinguishes itself from conventional x86 architectures (such as those featuring Intel AVX instructions) by achieving superior energy efficiency without compromising computational performance, making it exceptionally well-suited for distributed edge-cloud industrial systems where both power consumption and processing density are paramount concerns. In practical smart factory applications, these vector extensions can dramatically accelerate critical operations including high-frequency sensor data analysis (such as FFT-based vibration monitoring), real-time quality assurance processes (like parallel image processing for defect identification), and digital twin synchronization tasks (including the concurrent simulation of multiple production line scenarios). However, the full realization of these performance benefits depends on comprehensive software stack support across multiple layers, from the operating system through to the Java Virtual Machine (JVM) to big data frameworks like Apache Spark. This requirement becomes particularly acute in hybrid HPC-edge deployments, where industrial workflows must carefully balance the competing demands of low-latency edge processing and compute-intensive cloud or HPC-based simulations. The current study addresses two fundamental research questions with significant implications for industrial IoT implementations. First, we examine the readiness of the existing big data software stack, particularly focusing on Apache Spark's implementation in the JVM, to fully utilize SVE instructions for industrial applications. Our evaluation centers on OpenJDK version 16 and later, which introduced experimental SVE support through a dedicated JVM flag--a critical development for Java-based industrial applications (including Spark and Flink implementations) running on ARM-based edge nodes. Second, we investigate the performance characteristics of ARM-based HPC systems when executing industrial big data workloads, comparing them to traditional cloud and data center deployments. This analysis is particularly relevant given the unique challenges posed by industrial IoT environments, including data locality considerations (contrasting HPC's shared storage models with edge computing's distributed data paradigms), the need to support mixed workload types (combining streaming edge operations with batch-oriented cloud/HPC processing), and stringent quality-of-service requirements (balancing latency-sensitive control loops against throughput-oriented analytical processes). To our knowledge, this is the first detailed investigation into deploying big data frameworks like Apache Spark on ARM-based HPC systems tailored for industrial IoT scenarios. The results provide valuable guidance for improving performance and energy efficiency in edge-cloud computing infrastructures.