ML-in-C

This article investigates the impact of memory layout on array traversal in the context of machine learning. A 1D array was compared against a 2D array, both representing a 1,000,000$\times$100 matrix. 53,046 paired measurements were collected over the span of 6 hours, which were used to analyze execution times and apply statistical hypothesis testing. Results show that the 1D array consistently outpaced the 2D array by ~0.002734 seconds per traversal, revealing a highly statistically significant difference (p-value $<$ 1e-323). When applied in real-world scenarios relating to deep learning, these small performance gains translate into massive long-term benefits, potentially amounting to hours. This article highlights the implications of memory layouts in computational efficiency relating to data science workflows.

Initial release of the project. This version introduces the foundational DataFrame type definition and its helper functions. No models or training functionality are included yet.