Bridging Fintech And Structural Engineering: A Framework For High-Performance Gpu Computing Across Industries

Abstract

The need for a consistent, effective, and scalable computational framework has been brought to light by the increased demand for high-performance computing in structural engineering and fintech. In order to optimize large-scale, data-intensive tasks across several areas, this study suggests a GPU-accelerated approach. The framework's execution time, throughput, and memory usage were assessed using simulated datasets that represented high-frequency financial transactions and structural engineering mesh studies. The findings show that GPU acceleration retains excellent memory efficiency (~80–87%), decreases execution times by up to 78%, and increases throughput by up to 3.5×. Cross-domain study validates the transferability and flexibility of the framework by showing how optimization techniques, like parallelized Monte Carlo simulations in FinTech, can be successfully used to structural analysis jobs like finite element modeling. According to these results, a generalized high-performance computing framework can overcome computational difficulties unique to a certain industry and provide a reliable way to speed up intricate, parallelizable activities in a variety of fields.