Earliest-Completion Scheduling of Contract Algorithms with End Guarantees
Authors: Spyros Angelopoulos, Shendan Jin
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition, we present computational results on its implementation which demonstrate that it achieves a considerable improvement over the known schedule that optimizes the acceleration ratio, but is oblivious of L. The paper is structured as follows: ... Section 5 provides a computational evaluation of the schedule. |
| Researcher Affiliation | Academia | Spyros Angelopoulos and Shendan Jin Sorbonne Universit e, CNRS, Laboratoire d Informatique de Paris 6, LIP6, F-75252 Paris, France {spyros.angelopoulos, shendan.jin}@lip6.fr |
| Pseudocode | Yes | Algorithm 1 summarizes the steps needed to obtain the optimal schedule. Algorithm 1: Earliest-completion scheduling of contract algorithms with end guarantee L |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or links to a code repository. |
| Open Datasets | No | The paper focuses on theoretical algorithm design and computational evaluation of a schedule based on mathematical parameters (L, n) rather than traditional datasets. It does not mention using any publicly available or open datasets for training purposes. |
| Dataset Splits | No | The paper does not use traditional datasets or machine learning models, and therefore does not discuss training, validation, or test splits. |
| Hardware Specification | No | The paper mentions a "computational evaluation" and "implementation" but does not specify any hardware details (e.g., CPU, GPU models, memory, or specific computing environments) used for the experiments. |
| Software Dependencies | No | The paper discusses linear programming (LP) as a technique but does not specify any particular software, libraries, or solvers with version numbers that were used for the implementation or computational evaluation. |
| Experiment Setup | Yes | In this section we present computational results on the implementation of our schedule, whose completion time recall we denote by T (L). ... We choose τ to be equal to 1, and L to be integral in the range [1, 10^6]. Figure 1 illustrates the completion times of the two schedules for n = 5. ... Figure 2 illustrates the ratio Texp(L)/T (L) for n {1, 2, 20}. |