Optimization and Analysis of Matrix Operations Leveraging Alibaba Cloud

With the advancement of digital transformation, cloud computing technology has become a critical support for scientific computing. This paper proposes a cloud-based matrix operation pipeline method based on Alibaba Cloud to address the performance bottlenecks of traditional local computing models in handling large-scale matrix operations. By migrating matrix operations to the cloud, this method leverages the elastic resource allocation capability of Alibaba Cloud Function Compute (FC) to handle dynamic computing tasks and combines Alibaba Cloud Object Storage Service (OSS) for long-term secure data storage, constructing an innovative "storage-computation separation" architecture. The system design adopts a modular approach, including modules for data upload, matrix parsing, matrix multiplication, and result processing. Through algorithm optimization (e.g., using `float32` data types and block transmission strategies) and exception handling mechanisms, the system's computational efficiency and fault tolerance are significantly improved. Experimental results show that this method demonstrates superior performance in handling large-scale matrix operations, with the maximum supported matrix dimension increased to 20×20, and cold start latency optimized from 3.2 seconds to within 1.1 seconds. Future research will explore more efficient matrix operation algorithms, parallel computing technologies, and data compression strategies to further enhance system performance and applicability.