Implementation of High Level Matrix Arithmetic Based on Serverless Platform

Abstract

This article provides a comprehensive review of the topic related to high-dimensional matrix operations based on a Serverless architecture, encompassing the entire process of data upload, parallel processing, and result aggregation. It leverages the automatic scalability of cloud services to achieve efficient computing and evaluates and optimizes system performance. High-dimensional matrix operations play a crucial role in data science, machine learning, and large-scale computing. With the rapid growth of data volume, traditional computing architectures are facing bottlenecks, necessitating efficient and scalable solutions urgently. By implementing high-dimensional matrix operations on a Serverless platform, computational costs can be reduced, resource utilization improved, and flexibility and scalability ensured. This offers new insights for handling large-scale datasets and contributes to the advancement of related fields. Research indicates that the Serverless platform can effectively support high-dimensional matrix operations and enhance computational efficiency. In the future, more complex types of operations and optimization strategies can be explored, along with the integration of edge computing to further reduce latency and improve response speed.