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

arXiv: https://arxiv.org/abs/2506.22929 · HTML: https://mlsanity.com/paper/2506.22929/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 34 references that are in the map.
- First posted on arXiv 28 Jun 2025.

## Builds on (most cited first, 1 of 1)

- [High Performance Dataframes from Parallel Processing Patterns](https://mlsanity.com/paper/2209.06146/) (2022)

## Built on by (most cited first, 1 of 1)

- [Scaling Federated Linear Contextual Bandits via Sketching](https://mlsanity.com/paper/2605.00500/) (2026)

## Abstract (arXiv)

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.