Mathematical Computation on High-dimensional Data via Array Programming and Parallel Acceleration

• URL: http://arxiv.org/abs/2506.22929v1
• Date: Sat, 28 Jun 2025 15:42:23 GMT
• Title: Mathematical Computation on High-dimensional Data via Array Programming and Parallel Acceleration
• Authors: Chen Zhang,
• Abstract summary: We propose a parallel computation architecture based on space completeness, decomposing high-dimensional data into dimension-independent structures for distributed processing.<n>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.
• Score: 6.920979776722456
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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