GauS: Differentiable Scheduling Optimization via Gaussian Reparameterization
arXiv:2602.20427v1 Announce Type: new 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 schedulin
arXiv:2602.20427v1 Announce Type: new 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.
Executive Summary
The article proposes GauS, a novel differentiable framework for operator scheduling optimization, which leverages Gaussian distributions to capture the ordinal nature of time and reduce the optimization space. GauS models schedules as continuous Gaussian variables, allowing it to fully utilize modern parallel computing devices like GPUs. The method is highly flexible and can represent various objectives and constraints, providing the first differentiable formulation for the complex pipelined scheduling problem. The authors evaluate GauS on a range of benchmarks, demonstrating its ability to achieve Pareto-optimal results. This innovative approach has significant implications for software compilation and hardware synthesis, enabling more efficient and scalable scheduling optimization.
Key Points
- ▸ GauS is a differentiable framework for operator scheduling optimization that leverages Gaussian distributions
- ▸ GauS captures the ordinal nature of time and reduces the optimization space by orders of magnitude
- ▸ GauS is highly flexible and can represent various objectives and constraints
Merits
Strength in Scalability
GauS's ability to reduce the optimization space by orders of magnitude enables it to scale to complex scheduling problems that traditional approaches cannot handle.
Flexibility in Representation
GauS's ability to represent various objectives and constraints makes it a versatile tool for scheduling optimization.
Pareto-Optimal Results
GauS's ability to achieve Pareto-optimal results demonstrates its effectiveness in obtaining high-quality scheduling solutions.
Demerits
Computational Complexity
While GauS leverages modern parallel computing devices, its computational complexity may still be high for very large scheduling problems.
Limited Robustness
GauS's reliance on Gaussian distributions may make it less robust to outliers or non-Gaussian scheduling data.
Expert Commentary
The article presents a significant innovation in the field of scheduling optimization, leveraging the power of differentiable programming and Gaussian distributions to achieve scalable and efficient scheduling solutions. While there are some limitations to GauS, such as its computational complexity and limited robustness, the authors' results demonstrate the method's effectiveness in achieving Pareto-optimal results. As the field of scheduling optimization continues to evolve, GauS is likely to play a key role in the development of more efficient and scalable scheduling systems.
Recommendations
- ✓ Further research is needed to improve GauS's robustness and scalability for very large scheduling problems.
- ✓ GauS should be explored in other applications beyond software compilation and hardware synthesis, such as resource allocation and task scheduling.
