OpEvo: An Evolutionary Method for Tensor Operator Optimization
Authors: Xiaotian Gao, Wei Cui, Lintao Zhang, Mao Yang12320-12327
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | 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, maoyang@microsoft.com |
| 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. |