Abstract

The research examines how High-Performance Computing (HPC) and cloud-native environments can meet through scalable computing environments. The study measures the optimization of computational efficiency of large-scale data processing through containerization and hardware acceleration. With the help of a synthetic dataset containing 411 examples of high-dimensional performance measurements, the research modeling simulates different workload distributions in hybrid infrastructures. The main operated tools are Kubernetes as an orchestration tool, Docker as an environment isolation tool, and dedicated software libraries as a tool that monitors GPU acceleration. Findings have shown that a combination of containerization and parallel processing can lower the latency by a large margin whilst ensuring that hardware is utilized fully. It is concluded in the abstract that a single piece of architecture is needed to handle modern data-intensive tasks.

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