MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems

• URL: http://arxiv.org/abs/2607.02558v1
• Date: Sun, 28 Jun 2026 02:30:44 GMT
• Title: MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems
• Abstract summary: We present MLSysim, a first-s analytical framework that formalizes the "physics of systems" into a dimensionally-strict Python engine.<n>Every input is drawn from a typed, provenance-tracked registry, so no number enters an analysis without a documented source.<n>On this engine we codify a taxonomy of 22 "Systems Walls" resolved by 28 composable models and solvers, enabling sub-second design-space exploration.
• Score: 7.557409593010283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As machine learning shifts from laboratory curiosity to critical infrastructure, the systems that sustain it span an extraordinary range, from sub-milliwatt microcontrollers to multi-gigawatt datacenter fleets. Reasoning across this range is hard: empirical profiling requires the target hardware in hand, while cycle-accurate simulation costs hours per configuration, leaving no tool for rapid, full-stack architectural reasoning. We present MLSYSIM (Machine Learning Systems Infrastructure Modeling), a first-principles analytical framework that formalizes the "physics of systems" into a dimensionally-strict Python engine. MLSysim is built on a demand-supply abstraction that decouples computational demand from silicon supply and environmental context, and it enforces unit integrity at runtime so the silent conversion errors that plague ad-hoc modeling cannot occur. Every input is drawn from a typed, provenance-tracked registry, so no number enters an analysis without a documented source. On this engine we codify a taxonomy of 22 "Systems Walls" resolved by 28 composable models and solvers, enabling sub-second design-space exploration that identifies binding constraints and synthesizes ideal hardware specifications across the entire ML systems lifecycle.

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