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[2602.20427] GauS: Differentiable Scheduling Optimization via Gaussian Reparameterization
Summary
The paper presents GauS, a novel differentiable framework for operator scheduling that utilizes Gaussian distributions to optimize scheduling problems efficiently, capturing the ordinal nature of time and improving performance on modern computing devices.
Why It Matters
This research addresses a critical challenge in software compilation and hardware synthesis by introducing a flexible and efficient method for scheduling optimization. By leveraging Gaussian distributions, it enhances the capability of gradient-based approaches, potentially leading to better performance in various applications, including machine learning and hardware design.
Key Takeaways
- GauS models operator scheduling as a stochastic relaxation using Gaussian distributions.
- The framework captures the ordinal nature of time, improving optimization efficiency.
- GauS significantly reduces the optimization space compared to traditional methods.
- It provides a flexible formulation for complex scheduling problems, including pipelined scheduling.
- Empirical evaluations demonstrate that GauS achieves Pareto-optimal results across benchmarks.
Computer Science > Machine Learning arXiv:2602.20427 (cs) [Submitted on 23 Feb 2026] Title:GauS: Differentiable Scheduling Optimization via Gaussian Reparameterization Authors:Yaohui Cai, Vesal Bakhtazad, Cunxi Yu, Zhiru Zhang View a PDF of the paper titled GauS: Differentiable Scheduling Optimization via Gaussian Reparameterization, by Yaohui Cai and 3 other authors View PDF HTML (experimental) Abstract:Efficient operator scheduling is a fundamental challenge in software compilation and hardware synthesis. While recent differentiable approaches have sought to replace traditional ones like exact solvers or heuristics with gradient-based search, they typically rely on categorical distributions that fail to capture the ordinal nature of time and suffer from a parameter space that scales poorly. In this paper, we propose a novel differentiable framework, GauS, that models operator scheduling as a stochastic relaxation using Gaussian distributions, which fully utilize modern parallel computing devices like GPUs. By representing schedules as continuous Gaussian variables, we successfully capture the ordinal nature of time and reduce the optimization space by orders of magnitude. Our method is highly flexible to represent various objectives and constraints, which provides the first differentiable formulation for the complex pipelined scheduling problem. We evaluate our method on a range of benchmarks, demonstrating that Gaus achieves Pareto-optimal results. Subjects: Machine Lea...
