Balanced Column-Wise Block Pruning for Maximizing GPU Parallelism
Authors: Cheonjun Park, Mincheol Park, Hyun Jae Oh, Minkyu Kim, Myung Kuk Yoon, Suhyun Kim, Won Woo Ro
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate that BCBP is superior to previous pruning methods through comprehensive experiments. and Experiments Datasets and Models We evaluate the performance of BCBP using IWSLT English-Vietnamese (Luong and Manning 2016), SQu AD (Rajpurkar, Jia, and Liang 2018), and Image Net dataset (Deng et al. 2009). |
| Researcher Affiliation | Collaboration | Cheonjun Park1, Mincheol Park1,2, Hyun Jae Oh3, Minkyu Kim1, Myung Kuk Yoon4, Suhyun Kim2, and Won Woo Ro1* 1Yonsei University 2Korea Institute of Science and Technology 3Samsung Electronics 4Ewha Womans University |
| Pseudocode | No | The paper describes the process of BCBP in text but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The footnote '1https://github.com/cheonjun-park/appendix1' refers to Appendix 1 for 'additional verification of the following DNN models' but does not explicitly state that the source code for the BCBP methodology itself is provided at this link or elsewhere. |
| Open Datasets | Yes | Datasets and Models We evaluate the performance of BCBP using IWSLT English-Vietnamese (Luong and Manning 2016), SQu AD (Rajpurkar, Jia, and Liang 2018), and Image Net dataset (Deng et al. 2009). |
| Dataset Splits | No | Image Net contains 1.28M training images and 50K test images. The paper mentions training and testing sets, but does not explicitly describe a separate validation split with specific details for reproducibility. |
| Hardware Specification | Yes | We evaluate BCBP using NVIDIA RTX 2080 TI GPUs. |
| Software Dependencies | No | The paper mentions using 'Py Torch framework' but does not specify a version number or other software dependencies with their versions. |
| Experiment Setup | Yes | For settings of the fine-tuning, we use SGD optimizer with the weight decay, 1 10 4 and the momentum as 0.9. We set a batch size of 256 and a learning rate of 0.0001. |