Programming with Neural Surrogates of Programs

TL;DR

This paper formalizes surrogate-based programming patterns, demonstrating their effectiveness in accelerating, adapting, and optimizing CPU simulators through case studies and establishing a foundational methodology for surrogate-based workflows.

Contribution

It introduces a formal taxonomy and programming methodology for surrogate-based design patterns, validated through large-scale CPU simulator case studies.

Findings

Surrogate compilation accelerates CPU simulation by 1.6x.

Surrogate adaptation reduces error by up to 50%.

Surrogate optimization decreases error by 5% over expert parameters.

Abstract

Surrogates, models that mimic the behavior of programs, form the basis of a variety of development workflows. We study three surrogate-based design patterns, evaluating each in case studies on a large-scale CPU simulator. With surrogate compilation, programmers develop a surrogate that mimics the behavior of a program to deploy to end-users in place of the original program. Surrogate compilation accelerates the CPU simulator under study by $1.6\times$. With surrogate adaptation, programmers develop a surrogate of a program then retrain that surrogate on a different task. Surrogate adaptation decreases the simulator's error by up to $50\%$. With surrogate optimization, programmers develop a surrogate of a program, optimize input parameters of the surrogate, then plug the optimized input parameters back into the original program. Surrogate optimization finds simulation parameters that decrease the simulator's error by $5\%$ compared to the error induced by expert-set parameters. In this paper we formalize this taxonomy of surrogate-based design patterns. We further describe the programming methodology common to all three design patterns. Our work builds a foundation for the emerging class of workflows based on programming with surrogates of programs.