The “Memory Wall” and the super-linear computational complexity (O (N¹. 5) relative to data volume N of matrix operations remain the primary bottlenecks in scaling Large Language Models (LLMs) and high-performance computing hardware. While incremental improvements have reduced the exponent of matrix multiplication, a fundamental shift toward near-linearscaling has remained elusive. Here, we introduce Modular Matrix Algebra (MMA), a novel framework derived from Ramanujan’s Mock Theta functions that bypasses traditional Euclidean algebraic constraints. By mapping matrix transforms into modular space, we achieve a breakthrough computational complexity of O (N log N). Crucially, this transition achievesPerfect Numerical Invariance, maintaining a scale-invariant precision floor of 5. 37 × 10^−15 (Machine Epsilon) across all recursive depths. This enables a projected 80% reduction in data movement, allowing high-dimension matrix products to be derived via in-cache analytical extraction within the L1/L2 hierarchy. The MMA framework effectively dismantles the “Exponential Wall” associated with infinite context windows in transformer architectures, with profound implications across data-intensivedomains: • Climate Science: Enabling multi-decadal, high-resolution global weather simulations that were previously computationally prohibitive. Precision Medicine: Accelerating real-time genomic sequencing and protein folding at the point of care. Quantitative Finance: Facilitating instantaneous risk-modeling and high-frequency tradeanalysis across trillion-parameter market datasets. Digital Forensics: Revolutionizing large-scale pattern recognition and cryptographic analysis in near-real-time. Our algorithmic performance models and empirical stress-tests confirm a transformative speedup over current O (N². 37) state-of-the-art methods, providing a rigorous theoretical and practical foundation for the next generation of near-linear AI hardware and the future of the global computer.
Prakash Vaithyanathan (Tue,) studied this question.