# DiffTune: Optimizing CPU Simulator Parameters with Learned Differentiable Surrogates

arXiv: https://arxiv.org/abs/2010.04017 · HTML: https://mlsanity.com/paper/2010.04017/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2020.

## Built on by (most cited first, 14 of 14)

- [A Survey of Machine Learning for Computer Architecture and Systems](https://mlsanity.com/paper/2102.07952/) (2021)
- [Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation](https://mlsanity.com/paper/2207.05984/) (2022)
- [Mind Mappings: Enabling Efficient Algorithm-Accelerator Mapping Space Search](https://mlsanity.com/paper/2103.01489/) (2021)
- [Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate](https://mlsanity.com/paper/2106.09019/) (2021)
- [Zero Grads: Learning Local Surrogate Losses for Non-Differentiable Graphics](https://mlsanity.com/paper/2308.05739/) (2023)
- [SimNet: Accurate and High-Performance Computer Architecture Simulation using Deep Learning](https://mlsanity.com/paper/2105.05821/) (2021)
- [Programming with Neural Surrogates of Programs](https://mlsanity.com/paper/2112.06148/) (2021)
- [Learning Generalizable Program and Architecture Representations for Performance Modeling](https://mlsanity.com/paper/2310.16792/) (2023)
- [Friction on Demand: A Generative Framework for the Inverse Design of Metainterfaces](https://mlsanity.com/paper/2511.03735/) (2025)
- [NPS: A Framework for Accurate Program Sampling Using Graph Neural Network](https://mlsanity.com/paper/2304.08880/) (2023)
- [Turaco: Complexity-Guided Data Sampling for Training Neural Surrogates of Programs](https://mlsanity.com/paper/2309.11726/) (2023)
- [Accelerating Computer Architecture Simulation through Machine Learning](https://mlsanity.com/paper/2402.18746/) (2024)
- [PerfReasoning: How Well Do LLMs Reason on Hardware Performance?](https://mlsanity.com/paper/2609.04476/) (2026)
- [Accelerating Transfer-Learning-Based Autotuning with Predictive LLVM IR Performance Ranking](https://mlsanity.com/paper/2609.15807/) (2026)

## Abstract (arXiv)

CPU simulators are useful tools for modeling CPU execution behavior. However, they suffer from inaccuracies due to the cost and complexity of setting their fine-grained parameters, such as the latencies of individual instructions. This complexity arises from the expertise required to design benchmarks and measurement frameworks that can precisely measure the values of parameters at such fine granularity. In some cases, these parameters do not necessarily have a physical realization and are therefore fundamentally approximate, or even unmeasurable. In this paper we present DiffTune, a system for learning the parameters of x86 basic block CPU simulators from coarse-grained end-to-end measurements. Given a simulator, DiffTune learns its parameters by first replacing the original simulator with a differentiable surrogate, another function that approximates the original function; by making the surrogate differentiable, DiffTune is then able to apply gradient-based optimization techniques even when the original function is non-differentiable, such as is the case with CPU simulators. With this differentiable surrogate, DiffTune then applies gradient-based optimization to produce values of the simulator's parameters that minimize the simulator's error on a dataset of ground truth end-to-end performance measurements. Finally, the learned parameters are plugged back into the original simulator. DiffTune is able to automatically learn the entire set of microarchitecture-specific parameters within the Intel x86 simulation model of llvm-mca, a basic block CPU simulator based on LLVM's instruction scheduling model. DiffTune's learned parameters lead llvm-mca to an average error that not only matches but lowers that of its original, expert-provided parameter values.