MLSYSIM: First-Principles Infrastructure Modeling for Machine Learning Systems

2026-06-27

AI Summary

Existing tools struggle to efficiently and accurately perform full-stack architectural analysis of machine learning infrastructure spanning from microwatt-scale devices to gigawatt-scale data centers. This work proposes a first-principles-based analytical modeling framework that decouples computational demand from hardware supply and environmental context through a “demand–supply” abstraction. The framework introduces a “walls-of-systems” taxonomy, a dimensionally rigorous Python engine, 22 classes of system constraints, 28 composable solvers, and a typed input registry with provenance tracking to ensure unit consistency and traceability. It enables sub-second design space exploration, precisely identifies system bottlenecks, and automatically generates optimal hardware configurations covering the entire machine learning lifecycle.

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.

Problem

• machine learning systems
• infrastructure modeling
• first-principles modeling
• design-space exploration
• systems architecture

Innovation

• first-principles modeling
• demand-supply abstraction
• unit integrity
• systems walls
• design-space exploration