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 and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
OpEvo: An Evolutionary Method for Tensor Operator Optimization
Authors: Xiaotian Gao, Wei Cui, Lintao Zhang, Mao Yang12320-12327
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our comprehensive experiment results show that compared with state-of-the-art (SOTA) methods Op Evo can find the best configuration with the lowest variance and least efforts in the number of trials and wall-clock time. |
| Researcher Affiliation | Industry | Xiaotian Gao, Wei Cui, Lintao Zhang, Mao Yang Microsoft Research xiaotian.gao, weicu, lintaoz, EMAIL |
| Pseudocode | Yes | The Op Evo algorithm is summarized in Algorithm 1. |
| Open Source Code | Yes | All code of this work is available online. |
| Open Datasets | No | Three different Mat Mul operators are chosen from BERT to evaluate proposed method. ... A classic CNN architecture, Alex Net, is used to evaluate the end-to-end performance of the proposed method... |
| Dataset Splits | No | The paper describes experimental runs and evaluation metrics but does not specify traditional train/validation/test dataset splits as it focuses on optimizing tensor operators rather than training a machine learning model on a fixed dataset. |
| Hardware Specification | Yes | on both Nvidia (GTX 1080Ti) and AMD (MI50 GPU) platforms. |
| Software Dependencies | Yes | compiled and run with CUDA 10.0 or RCOM 2.9. |
| Experiment Setup | Yes | Op Evo has two important hyperparameters, the mutation rate q which trade-offs the exploration and exploitation and the parent size λ which governs the diversity in the evolutionary process. In this part, we evaluate Op Evo with different q and λ for better understanding of each introduced technique and the hyperparameter sensitivity. |