Statistical Methods for Performance Analysis of Data Processing Systems in High-Performance Computing Environments

Abstract

in excessive-performance computing environments, wherein huge amounts of data want to be processed quickly, the overall performance of statistics processing systems is crucial. Analyzing the performance of these structures is essential to become aware of bottlenecks and optimize their performance. This studies aims to increase statistical strategies for overall performance analysis of facts processing systems in high-performance computing environments. The evaluation technique is to gather overall performance facts from the goal device. This fact frequently consists of numerous measurements, making it challenging to draw meaningful insights. To cope with this difficulty, statistical strategies, transformation, outlier detection, and dimensionality discount can be implemented to clear out noise and pick out styles within the records. Regression evaluation may version the relationship among gadget parameters and overall performance metrics. It helps identify which device parameters have the most considerable effect on performance and may guide similarly optimization efforts. Moreover, cluster analysis can be used to institution systems with comparable performance traits, allowing comparison and identity of pinnacle-appearing systems.