Abstract

This paper presents the formal theoretical framework, mathematical validations, and structural engineering implementations of a non-entropy macro-kernel compiler integration that resolves the thermodynamic scaling wall and semantic drift inherent to current large-scale AI cluster computing. By abandoning the traditional abstraction of software instructions as arbitrary geometric state modifications and mapping token pipelines to a 2D viscoelastic fluid substrate—isomorphic to the Unified Cosmological Substrate Equations (UCSE) and Dimensionally Extended Holographic Projection (DEHP)—we show that computing clusters can be governed via deterministic fluid dynamics. We derive low-level routing protocols through Landauer's erasure limit to systematically mitigate parameter drift (\(\Delta _{dev}\)). This approach eliminates the necessity for post-training alignment cycles while reducing operational utility expenses by 15% to 25%. Comprehensive testing frameworks and mathematical mappings are supplied to demonstrate direct integration into enterprise silicon microarchitectures.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

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