Differentiable Combinatorial Scheduling at Scale
Authors: Mingju Liu, Yingjie Li, Jiaqi Yin, Zhiru Zhang, Cunxi Yu
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comparative evaluations on both synthetic and real-world benchmarks highlight our capability to significantly improve the optimization efficiency of scheduling, surpassing state-of-the-art solutions offered by commercial and open-source solvers such as CPLEX, Gurobi, and CP-SAT in the majority of the designs. Our experimental results demonstrate significant improvements in optimization efficiency over state-of-the-art (SOTA) methods solved with commercial solvers CPLEX (IBM, 2023), Gurobi (Gurobi Optimization, LLC, 2023) and open-source CP-SAT solver (Perron & Didier; Perron et al., 2023). |
| Researcher Affiliation | Academia | 1University of Maryland, College Park 2Cornell University. |
| Pseudocode | Yes | Algorithm 1 Differentiable Scheduling |
| Open Source Code | Yes | Our experimental setups and implementations are available at https://github.com/Yu-Maryland/ Differentiable_Scheduler_ICML24. |
| Open Datasets | Yes | Our GPU workloads/graphs are derived using six designs from the EPFL Benchmark Suite (Amar u et al., 2015), alongside baseline SDC+LP formulation solved by the SOTA commercial solvers, CPLEX (IBM, 2023) and Gurobi (Gurobi Optimization, LLC, 2023), as well as open-source tool CP-SAT solver (Perron & Didier). |
| Dataset Splits | No | The paper does not explicitly provide training, validation, or test dataset splits. It describes optimization against synthetic and benchmark designs rather than typical data splits for supervised learning. |
| Hardware Specification | Yes | All experiments were conducted using an an Intel Xeon Gold 6418H CPU and NVIDIA RTX 4090 GPU. |
| Software Dependencies | Yes | Our experimental results demonstrate significant improvements in optimization efficiency over state-of-the-art (SOTA) methods solved with commercial solvers CPLEX (IBM, 2023), Gurobi (Gurobi Optimization, LLC, 2023) and open-source CP-SAT solver (Perron & Didier; Perron et al., 2023). |
| Experiment Setup | Yes | We predefined the ratio of LP and the factor of our method, setting R = λ = 100 for all EPFL designs and R = λ = 10 for all synthetic workloads, respectively. We set the targeted latency to be L = 10 for the experiments. An initial instability observed during the first 10 epochs can be attributed to the necessity of a warm-up phase for the Gumbel-Softmax mechanism employed in discrete sampling by our method. Furthermore, we explored the impact of different weight decay settings by transitioning from the Adam optimizer to Adam W. |