Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Learning Augmented Energy Minimization via Speed Scaling
Authors: Etienne Bamas, Andreas Maggiori, Lars Rohwedder, Ola Svensson
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we will test the LAS algorithm on both synthetic and real datasets. We will calculate the competitive ratios with respect to the offline optimum. |
| Researcher Affiliation | Academia | Etienne Bamas EPFL Switzerland EMAIL Andreas Maggiori EPFL Switzerland EMAIL Lars Rohwedder EPFL Switzerland EMAIL Ola Svensson EPFL Switzerland EMAIL |
| Pseudocode | Yes | Algorithm 1 LEARNING AUGMENTED SCHEDULING (LAS) Input: T, D, and wpred initially and wreal in an online fashion Output: A feasible schedule (si)T D i=0 Let δ > 0 with 1+δ 1 δ α = 1 + ε. Compute optimal offline schedule for (wpred, T, (1 δ)D) where the jobs wpred i are run at uniform speeds ci an disjoint intervals [ai, bi] using [17]. |
| Open Source Code | Yes | We note that the code is publicly available at https://github.com/andreasr27/LAS. |
| Open Datasets | Yes | Real dataset. We provide additional evidence that the LAS algorithm outperforms purely online algorithms by conducting experiments on the login requests to Bright Kite [5] |
| Dataset Splits | No | The paper uses synthetic and real datasets but does not explicitly provide details on train/validation/test splits with specific percentages, counts, or a detailed splitting methodology for their experiments. For the real dataset, it describes using 'access patterns of the previous day as a prediction for the current day' which is a form of temporal split for the input, not a standard training/validation/testing split for model evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions that 'the code is publicly available' but does not list specific software dependencies with version numbers (e.g., Python version, library versions like TensorFlow, PyTorch, scikit-learn). |
| Experiment Setup | Yes | We fix α = 3 in all our experiments as this value models the power consumption of modern processors (see Bansal et al. [2]). For artificial datasets, 'We used m = 20, M = 80, s = 5, T = 220 and D = 20.' For the real dataset, 'The timeline was discretized in chunks of ten minutes and D was set to 20.' The paper also discusses performance for different values of ε (e.g., 'ε = 0.01' and 'ε = 0.8'). |