Mathematical Computation on High-dimensional Data via Array Programming and Parallel Acceleration
Year: 2025
Type: preprint
Abstract: While deep learning excels in natural image and language processing, its application to high-dimensional data faces computational challenges due to the dimensionality curse. Current large-scale data tools focus on business-oriented descriptive statistics, lacking mathematical statistics support for advanced analysis. We propose a parallel computation architecture based on space completeness, decomposing high-dimensional data into dimension-independent structures for distributed processing. This framework enables seamless integration of data mining and parallel-optimized machine learning methods, supporting scientific computations across diverse data types like medical and natural images within a unified system.
Source: arXiv (Cornell University)
Author: Zhang, Chen
Language: English
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4416508667
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