The mlsysim Optimization Engine

mlsysim bridges the gap between pedagogical simplicity and production-grade Operations Research (OR).

While many academic tools rely on hardcoded Python for loops to find optimal configurations, mlsysim implements a strict Solver Protocol. This allows the engine to route mathematical problems to the correct industrial backend (Google OR-Tools, SciPy) without exposing the complexity to the textbook reader.

Why this Architecture?

In production ML infrastructure, finding the optimal deployment strategy is rarely a simple grid search.

• Finding the optimal batch size against a latency SLA requires continuous gradient descent down a queueing theory curve.
• Routing 10,000 heterogeneous workloads across global datacenters based on variable carbon intensity requires linear programming.
• Designing a custom ASICs memory hierarchy requires mixed-integer constraint satisfaction.

By decoupling the Physics (the Models) from the Search (the Optimizers), mlsysim serves as both a pristine educational tool for understanding the bounds of computation, and a highly extensible framework for systems researchers building the next generation of ML infrastructure.